Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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This achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
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We are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
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Thank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"5394",leadTitle:null,fullTitle:"Updates in Liver Cancer",title:"Updates in Liver Cancer",subtitle:null,reviewType:"peer-reviewed",abstract:"Liver cancers result in considerable amount of financial and social burden. On the other hand, researches and clinical studies related to liver cancers continue to advance at a rapid pace. The chapters in this book provide state-of-the-art reviews on the current knowledge and advances in research and management of liver cancers. It includes the most recent advances in that field, particularly, hepatocarcinogenesis and the potential role of intestinal microbiota, nonalcoholic steatohepatitis, cancer stem cells, aldehyde dehydrogenase-1, and hepatitis B virus. This book also discusses the methods of diagnosis of HCC, the minimally invasive therapies for liver cancers, living donor liver transplantation for HCC, surgical management of liver metastases from colorectal cancers, and assessment and optimization for the future liver remnant.",isbn:"978-953-51-3064-2",printIsbn:"978-953-51-3063-5",pdfIsbn:"978-953-51-4885-2",doi:"10.5772/62680",price:119,priceEur:129,priceUsd:155,slug:"updates-in-liver-cancer",numberOfPages:222,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"a502cea22e6d113a70f609c947235665",bookSignature:"Hesham Mohamed Abdeldayem",publishedDate:"April 5th 2017",coverURL:"https://cdn.intechopen.com/books/images_new/5394.jpg",numberOfDownloads:17373,numberOfWosCitations:8,numberOfCrossrefCitations:8,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:16,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:32,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 23rd 2016",dateEndSecondStepPublish:"April 13th 2016",dateEndThirdStepPublish:"July 18th 2016",dateEndFourthStepPublish:"October 16th 2016",dateEndFifthStepPublish:"November 15th 2016",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"72383",title:"Prof.",name:"Hesham",middleName:null,surname:"Abdeldayem",slug:"hesham-abdeldayem",fullName:"Hesham Abdeldayem",profilePictureURL:"https://mts.intechopen.com/storage/users/72383/images/system/72383.png",biography:"Professor Abdeldayem graduated from Kasr Elaini School of Medicine in 1987. He received training at Cairo University Hospitals, Menoufia University, University of Pittsburgh Medical Center and King Abdul-Aziz Medical City. He Joined the National Liver Institute in 1993. He has several publications in the fields of hepato-pancreato-biliary surgery and organ transplantation. He is a Member of InTechOpen\\'s Editorial Advisory Board, and is Associate editor in another publication. He currently holds the positions of Professor of Surgery and Dean of the National Liver Institute, Menoufia University, Egypt.",institutionString:"Menoufia University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"8",totalChapterViews:"0",totalEditedBooks:"9",institution:{name:"Menoufia University",institutionURL:null,country:{name:"Egypt"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1078",title:"Gastrointestinal Oncology",slug:"gastrointestinal-oncology"}],chapters:[{id:"54274",title:"Introductory Chapter: Updates on the Management of Hepatocellular Carcinoma",doi:"10.5772/67557",slug:"introductory-chapter-updates-on-the-management-of-hepatocellular-carcinoma",totalDownloads:1642,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Asmaa Gomaa, Alzhraa Alkhatib, Shimaa Alkilany, Osama Lotfy El\nAbd, Hesham Abdeldayem and Imam Waked",downloadPdfUrl:"/chapter/pdf-download/54274",previewPdfUrl:"/chapter/pdf-preview/54274",authors:[{id:"72383",title:"Prof.",name:"Hesham",surname:"Abdeldayem",slug:"hesham-abdeldayem",fullName:"Hesham Abdeldayem"},{id:"194522",title:"Prof.",name:"Asma",surname:"Gomaa",slug:"asma-gomaa",fullName:"Asma Gomaa"},{id:"194523",title:"Dr.",name:"Alzhraa",surname:"Alkhatib",slug:"alzhraa-alkhatib",fullName:"Alzhraa Alkhatib"},{id:"194524",title:"Dr.",name:"Shimaa",surname:"Alkilany",slug:"shimaa-alkilany",fullName:"Shimaa Alkilany"},{id:"194525",title:"Prof.",name:"Osama Lotfy",surname:"El Abd",slug:"osama-lotfy-el-abd",fullName:"Osama Lotfy El Abd"},{id:"194526",title:"Prof.",name:"Imam",surname:"Waked",slug:"imam-waked",fullName:"Imam Waked"}],corrections:null},{id:"54460",title:"Intestinal Microbiota, Nonalcoholic Steatohepatitis and Hepatocellular Carcinoma: The Potential Role of Dysbiosis in the Hepatocarcinogenesis",doi:"10.5772/67322",slug:"intestinal-microbiota-nonalcoholic-steatohepatitis-and-hepatocellular-carcinoma-the-potential-role-o",totalDownloads:1454,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Introduction: Hepatocellular carcinoma (HCC) accounts for the majority of primary liver cancers. Approximately 5–30% of HCC patients lack a readily identifiable risk factor for their cancer, and most of these cases are attributed to nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH).",signatures:"Giovanni Brandi, Stefania De Lorenzo, Marco Candela and\nFrancesco Tovoli",downloadPdfUrl:"/chapter/pdf-download/54460",previewPdfUrl:"/chapter/pdf-preview/54460",authors:[{id:"189262",title:"Dr.",name:"Giovanni",surname:"Brandi",slug:"giovanni-brandi",fullName:"Giovanni Brandi"}],corrections:null},{id:"53120",title:"Cancer Stem Cells and Aldehyde Dehydrogenase 1 in Liver Cancers",doi:"10.5772/66454",slug:"cancer-stem-cells-and-aldehyde-dehydrogenase-1-in-liver-cancers",totalDownloads:1444,totalCrossrefCites:2,totalDimensionsCites:3,hasAltmetrics:0,abstract:"The cancer stem cell (CSC) theory posits that a small population of cells with stem cell-like features is responsible for tumor growth, resistance, and recurrence in many malignancies. This theory could be a useful paradigm for designing innovative targeted drug therapies. Liver cancer is the fifth most common cancer worldwide, with hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) as the predominant forms. Hepatic stem/progenitor cells are believed to be the origin of HCCs and CCAs; however, this remains a controversial topic. Aldehyde dehydrogenase (ALDH) is the main enzymatic system responsible for the clearance of acetaldehyde from the hepatocytes in the liver tissue. Therefore, ALDH1 has been suggested to be a potential, biological and CSC marker in liver cancers. We here provide an overview of the current state of knowledge of CSCs in liver and the role of ALDH1 in the development and progression of liver cancers and discuss its potential value as a prognostic and diagnostic biomarker.",signatures:"Hiroyuki Tomita, Tomohiro Kanayama, Ayumi Niwa, Kei Noguchi,\nKazuhisa Ishida, Masayuki Niwa and Akira Hara",downloadPdfUrl:"/chapter/pdf-download/53120",previewPdfUrl:"/chapter/pdf-preview/53120",authors:[{id:"187144",title:"Dr.",name:"Hiroyuki",surname:"Tomita",slug:"hiroyuki-tomita",fullName:"Hiroyuki Tomita"},{id:"194547",title:"Mr.",name:"Tomohiro",surname:"Kanayama",slug:"tomohiro-kanayama",fullName:"Tomohiro Kanayama"},{id:"194548",title:"Mrs.",name:"Ayumi",surname:"Niwa",slug:"ayumi-niwa",fullName:"Ayumi Niwa"},{id:"194549",title:"Mr.",name:"Kei",surname:"Noguchi",slug:"kei-noguchi",fullName:"Kei Noguchi"},{id:"194550",title:"Mr.",name:"Kazuhisa",surname:"Ishida",slug:"kazuhisa-ishida",fullName:"Kazuhisa Ishida"},{id:"194551",title:"Prof.",name:"Masayuki",surname:"Niwa",slug:"masayuki-niwa",fullName:"Masayuki Niwa"},{id:"194552",title:"Prof.",name:"Akira",surname:"Hara",slug:"akira-hara",fullName:"Akira Hara"}],corrections:null},{id:"52145",title:"Lipid Metabolism in Liver Cancer",doi:"10.5772/64993",slug:"lipid-metabolism-in-liver-cancer",totalDownloads:1818,totalCrossrefCites:2,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Hepatocellular carcinoma (HCC) represents 90% cases of liver cancer that is the second leading cause of cancer death in the world. With the pandemic of obesity and other metabolic syndromes in both adults and children, the incidences of fatty liver diseases and the derived HCC are on their upward track. Emerging metabolomic studies have revealed the perturbation of lipid profiles and other metabolites in fatty liver diseases and HCC. Two common metabolic features including enforced fatty acid oxidation and glycolysis could distinguish HCC from healthy liver and chronic non-tumor liver diseases. The potential translational impacts of fatty acid oxidation are gaining great interests, because many recent investigations have demonstrated that tumor cells were dependent on fatty acid oxidation for cell survival and tumor growth. Blockage of fatty acid oxidation could sensitize to metabolic stress-induced cell death and tumor growth inhibition. Thus, lipid catabolism, in terms of fatty oxidation, is tuned for tumor maintenance but vulnerable to pharmacological disruption. The therapeutic potentials of blocking fatty acid oxidation are yet to be further carefully examined.",signatures:"Guo-Dong Lu and Shing Chuan Hooi",downloadPdfUrl:"/chapter/pdf-download/52145",previewPdfUrl:"/chapter/pdf-preview/52145",authors:[{id:"187142",title:"Prof.",name:"Guo-Dong",surname:"Lu",slug:"guo-dong-lu",fullName:"Guo-Dong Lu"},{id:"189095",title:"Prof.",name:"Shing Chung",surname:"Hooi",slug:"shing-chung-hooi",fullName:"Shing Chung Hooi"}],corrections:null},{id:"52436",title:"Hepatitis B Virus–Related Hepatocellular Carcinoma: Carcinogenesis, Prevention, and Treatment",doi:"10.5772/65424",slug:"hepatitis-b-virus-related-hepatocellular-carcinoma-carcinogenesis-prevention-and-treatment",totalDownloads:2057,totalCrossrefCites:2,totalDimensionsCites:8,hasAltmetrics:0,abstract:"Hepatocellular carcinoma (HCC) is the sixth most common cancer in the world and the second leading cause of cancer death. Hepatitis B virus (HBV) infection is one of the major risk factors for the development of HCC in the world. Most of the burden of disease (85%) is observed in the HBV endemic regions. Chronic infection with HBV predisposes patients with or without cirrhosis to HCC. Patients with high HBV DNA levels are at an increased risk for HCC. Studies have shown that the suppression of HBV with anti-viral therapy (nucleos(t)ide analogs) (NAs) decreases the incidence of HCC but does not eliminate the risk entirely. Chronic viral suppression alone is not sufficient treatment to prevent HCC development. Therefore, along with NAs, treatment may need to include targeting the cccDNA and inhibiting the viral entry into the newly formed hepatocytes and T-cell vaccine which specifically targets HBV and enhancing innate immunity with Toll-like receptor agonist. With all of these working together, we may achieve the goal of HBV cure.",signatures:"Bolin Niu and Hie-Won Hann",downloadPdfUrl:"/chapter/pdf-download/52436",previewPdfUrl:"/chapter/pdf-preview/52436",authors:[{id:"188150",title:"Dr.",name:"Hie-Won",surname:"Hann",slug:"hie-won-hann",fullName:"Hie-Won Hann"},{id:"188930",title:"Dr.",name:"Bolin",surname:"Niu",slug:"bolin-niu",fullName:"Bolin Niu"}],corrections:null},{id:"52230",title:"Diagnosis of Hepatocellular Carcinoma",doi:"10.5772/64992",slug:"diagnosis-of-hepatocellular-carcinoma",totalDownloads:2206,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Hepatocellular carcinoma (HCC) is one of the commonest cancers worldwide, particularly in the developing countries HCC occurs predominantly in patients with underlying chronic liver disease and cirrhosis, especially due to chronic hepatitis C virus (HCV) and hepatitis B virus (HBV) infection. Tumors progress with local expansion, intrahepatic spread, and distant metastases, and the life expectancy of patients with HCC is poor, with a mean survival of 6–20 months. Thus, developing effective and efficient care for patients with HCC must become a significant subject. Removal of HCC by surgical, transplantation or resection of the tumors, means offers the best chance for possible cure. Criteria for such intervention have been refined over the last decade to optimize long-term survival in selected patients with Milan criteria. Not many patients are candidate given the advanced stage of their cancer at diagnosis or degree of liver disease. The other main limiting factor is inadequate organ storage. Unfortunately, many patients die when they are waiting a donor organ. Local ablative therapies may be effective for time saving as a bridge therapy, and may provide palliation, in these patients. Diagnostic tools commonly used include radiographic imaging, and rarely serum markers and liver biopsy. A suspicious lesion on the ultrasound generally requires additional imaging studies to confirm the diagnosis of the tumor. Histologic confirmation is not required in a patient at increased risk for hepatocellular carcinoma whose lesion(s) fulfill criteria for hepatocellular carcinoma which are presence of typical features, including hypervascularity during arterial phase followed by decreased enhancement (washout) during portal venous phases on computerized tomography or has increased T2 signal intensity on magnetic resonance imaging.",signatures:"Ayse Kefeli, Sebahat Basyigit and Abdullah Ozgur Yeniova",downloadPdfUrl:"/chapter/pdf-download/52230",previewPdfUrl:"/chapter/pdf-preview/52230",authors:[{id:"177011",title:"Dr.",name:"Sebahat",surname:"Basyigit",slug:"sebahat-basyigit",fullName:"Sebahat Basyigit"},{id:"177013",title:"Dr.",name:"Ayse",surname:"Kefeli",slug:"ayse-kefeli",fullName:"Ayse Kefeli"},{id:"188826",title:"Dr.",name:"Abdullah Ozgur",surname:"Yeniova",slug:"abdullah-ozgur-yeniova",fullName:"Abdullah Ozgur Yeniova"}],corrections:null},{id:"52518",title:"Onco-Surgical Management of Liver Metastases from Colorectal Cancer",doi:"10.5772/65223",slug:"onco-surgical-management-of-liver-metastases-from-colorectal-cancer",totalDownloads:1661,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"Metastatic disease is the main cause of death in patients with colorectal cancer and the most frequent location of metastases is in the liver. The treatment of liver metastases of colorectal origin is multimodal and should be based on a multidisciplinary team decision. A systematic review of the literature revealed that the number of liver metastases, their maximum size, CEA level, advanced age of the patients, and presence of extrahepatic disease are no longer contraindications to liver resection. The resectability rate of colorectal liver metastases increased from 10 to almost 40%, enabling 5-year overall survival rates higher than 30%. Short-term and long-term results achieved by simultaneous resection (SR) are similar to those achieved by staged resections in patients with synchronous colorectal liver metastases. Whenever possible, major hepatectomies should be replaced by ultrasound-guided limited liver resections, and primary tumor should be approached in a minimally invasive manner. Even initially unresectable colorectal liver metastases could be rendered resectable by an aggressive multimodal approach (“two-stage” hepatectomies, hepatectomy after portal vein embolization/ligation, resection after conversion chemotherapy, and hepatectomy associated with ablation). The presence of extrahepatic metastases is no longer a contraindication to liver resection, when extrahepatic disease is resectable. Repeat hepatectomy improves survival in patients with recurrent liver metastases.",signatures:"Irinel Popescu and Sorin Tiberiu Alexandrescu",downloadPdfUrl:"/chapter/pdf-download/52518",previewPdfUrl:"/chapter/pdf-preview/52518",authors:[{id:"76729",title:"Prof.",name:"Irinel",surname:"Popescu",slug:"irinel-popescu",fullName:"Irinel Popescu"},{id:"187962",title:"M.D.",name:"Sorin Tiberiu",surname:"Alexandrescu",slug:"sorin-tiberiu-alexandrescu",fullName:"Sorin Tiberiu Alexandrescu"}],corrections:null},{id:"52372",title:"Minimally Invasive Treatments for Liver Cancer",doi:"10.5772/65107",slug:"minimally-invasive-treatments-for-liver-cancer",totalDownloads:1505,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"While surgical resection and chemotherapy have remained mainstays in the treatment of both primary and metastatic liver cancers, various minimally invasive techniques have been developed to treat patients for whom traditional approaches either are not available or have failed. Percutaneous ablation techniques such as radiofrequency, microwave, cryoablation, and irreversible electroporation are considered as potentially curative treatments in patients with hepatocellular carcinoma with early-stage tumors. Transarterial chemoembolization (TACE) and radioembolization with yttrium-90 (Y-90) are palliative treatments that have improved survival in patients with unresectable disease. In this chapter, we discuss these minimally invasive techniques, the criteria for selecting appropriate candidates for treatment, and potential limitations to their use.",signatures:"Nicolas Cardenas, Rahul Sheth and Joshua Kuban",downloadPdfUrl:"/chapter/pdf-download/52372",previewPdfUrl:"/chapter/pdf-preview/52372",authors:[{id:"77053",title:"Dr.",name:"Rahul",surname:"Sheth",slug:"rahul-sheth",fullName:"Rahul Sheth"},{id:"188923",title:"Dr.",name:"Joshua",surname:"Kuban",slug:"joshua-kuban",fullName:"Joshua Kuban"},{id:"194607",title:"Mr.",name:"Nicolas",surname:"Cardenas",slug:"nicolas-cardenas",fullName:"Nicolas Cardenas"}],corrections:null},{id:"53476",title:"Assessment and Optimization of the Future Liver Remnant",doi:"10.5772/66139",slug:"assessment-and-optimization-of-the-future-liver-remnant",totalDownloads:2157,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Safe liver resection is a vital element in the management of primary and secondary hepatic malignancies. The indications for resection have evolved Over time, and this has in part been due to the ability to improve the future liver remnant (FLR). This chapter reviews the current and future methods used for assessing the future liver remnant volume and function in order to minimize the risk of post-hepatectomy liver failure (PHLF). Current and evolving methods used in augmenting the future liver remnant are also considered. Since its introduction in the 1990s, portal venous embolization (PVE) has become the most widely used method of augmenting the FLR. The factors that affect hypertrophy following embolization as well as techniques used in portal venous embolization will be reviewed. Other methods of augmentation discussed include portal vein ligation (PVL) and the emerging method of associating liver partition and portal vein ligation for staged hepatectomy (ALPPS). The chapter also considers the various methods in the context of limiting tumour progression in the future liver remnant and attempts to integrate newer techniques such as ALPPS into current treatment algorithms.",signatures:"Mandivavarira Maundura and Jonathan B Koea",downloadPdfUrl:"/chapter/pdf-download/53476",previewPdfUrl:"/chapter/pdf-preview/53476",authors:[{id:"188727",title:"Dr.",name:"Jonathan",surname:"Koea",slug:"jonathan-koea",fullName:"Jonathan Koea"},{id:"188728",title:"Dr.",name:"Mandivavarira",surname:"Maundura",slug:"mandivavarira-maundura",fullName:"Mandivavarira Maundura"}],corrections:null},{id:"52505",title:"Living Donor Liver Transplantation for Hepatocellular Carcinoma",doi:"10.5772/65109",slug:"living-donor-liver-transplantation-for-hepatocellular-carcinoma",totalDownloads:1432,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Hepatocellular carcinoma (HCC) is a major worldwide health problem, which is expected to increase steadily due to different underlying liver diseases. Surgical treatment modalities including liver transplantation (LT) or liver resection (LR) are the mainstay options for early cases of HCC. Liver transplantation for well‐selected cases provides excellent survival outcomes comparable to nonmalignant indications of LT. Living donor liver transplantation (LDLT) is an alternative option or even the sole one in the current era of organ shortage problem and in some Asian countries where deceased organ donation is markedly reduced due to various reasons. The adoption of LDLT for HCC treatment elicited many dynamic changes and debates to the dilemma of LT as a whole. In this chapter, we focus on different perspectives of LDLT for HCC, including selection criteria evolution, controversial topics, ethical considerations, operative highlights, and other points.",signatures:"Chih‐Che Lin, Ahmed Mohammed Abdel Aziz Elsarawy and Chao‐\nLong Chen",downloadPdfUrl:"/chapter/pdf-download/52505",previewPdfUrl:"/chapter/pdf-preview/52505",authors:[{id:"183753",title:"Dr.",name:"Chih-Che",surname:"Lin",slug:"chih-che-lin",fullName:"Chih-Che Lin"},{id:"187289",title:"Prof.",name:"Chao-Long",surname:"Chen",slug:"chao-long-chen",fullName:"Chao-Long Chen"},{id:"188761",title:"Dr.",name:"Ahmed",surname:"Elsarawy",slug:"ahmed-elsarawy",fullName:"Ahmed Elsarawy"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"3164",title:"Hepatic Surgery",subtitle:null,isOpenForSubmission:!1,hash:"e54bf2639e53e684ac2233e94ae53c19",slug:"hepatic-surgery",bookSignature:"Hesham Abdeldayem",coverURL:"https://cdn.intechopen.com/books/images_new/3164.jpg",editedByType:"Edited by",editors:[{id:"72383",title:"Prof.",name:"Hesham",surname:"Abdeldayem",slug:"hesham-abdeldayem",fullName:"Hesham Abdeldayem"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2060",title:"Liver Transplantation",subtitle:"Technical Issues and Complications",isOpenForSubmission:!1,hash:"f1a1413332fb74229afd9d4d68248cbc",slug:"liver-transplantation-technical-issues-and-complications",bookSignature:"Hesham Abdeldayem and Naglaa Allam",coverURL:"https://cdn.intechopen.com/books/images_new/2060.jpg",editedByType:"Edited by",editors:[{id:"72383",title:"Prof.",name:"Hesham",surname:"Abdeldayem",slug:"hesham-abdeldayem",fullName:"Hesham Abdeldayem"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"967",title:"Liver Transplantation",subtitle:"Basic Issues",isOpenForSubmission:!1,hash:"d61ca05025c7154ff4579b2d6c95d7ae",slug:"liver-transplantation-basic-issues",bookSignature:"Hesham Abdeldayem and Naglaa Allam",coverURL:"https://cdn.intechopen.com/books/images_new/967.jpg",editedByType:"Edited by",editors:[{id:"72383",title:"Prof.",name:"Hesham",surname:"Abdeldayem",slug:"hesham-abdeldayem",fullName:"Hesham Abdeldayem"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6502",title:"Topics in the Surgery of the Biliary Tree",subtitle:null,isOpenForSubmission:!1,hash:"6e1e8b08aab8583fc30db6351ae123d6",slug:"topics-in-the-surgery-of-the-biliary-tree",bookSignature:"Hesham Abdeldayem",coverURL:"https://cdn.intechopen.com/books/images_new/6502.jpg",editedByType:"Edited by",editors:[{id:"72383",title:"Prof.",name:"Hesham",surname:"Abdeldayem",slug:"hesham-abdeldayem",fullName:"Hesham Abdeldayem"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5155",title:"Frontiers in Transplantology",subtitle:null,isOpenForSubmission:!1,hash:"f358194cd9d33671b03808b346f354dc",slug:"frontiers-in-transplantology",bookSignature:"Hesham Abdeldayem, Ahmed F. 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Sanitarian Raimi Morufu Olalekan received his M.Phil in Environmental Health Science from Kwara State University in August 2018 and MSc in Environmental Health Management from the University of Uyo in March 2017, Diploma in Environmental Health from Public Health Training Institute and Bsc in Geography and Environmental Management from Niger Delta University. \nHe has taught classes at the Niger Delta University (department of community medicine), University of Maiduguri (department of geography), University of Uyo (center for wetlands and waste management studies) and Kwara State University (department of environmental health). Raimi Morufu Olalekan is the author or coauthor of more than 100 scientific publications and expert papers in American, European and Asian journal to his credit, 20 research projects under way including cumulative impact assessment of air quality and assessment of digital debris management in health Institutions in South-South, Nigeria. 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\n\t\t\t
1. Introduction
\n\t\t\t
Many ferroelectric oxides possess the ABO3 perovskite structure (Mitchell, 2002), in which the A-site cations are typically larger than the B-site cations and similar in size to the oxygen anion. Figure 1 shows a schematic drawing for this structure, where the A cations are surrounded by 12-anions in the cubo-octahedral coordination and the B cations are surrounded by 6-anions in the octahedral coordination. An ideal perovskite exhibits a cubic space group Pm3m. This structure is centrosymmetric and cannot allow the occurrence of ferroelectricity that is the presence of a switchable spontaneous electric polarization arising from the off-center atomic displacement in the crystal (Jaffe et al., 1971; Lines & Glass, 1977). The instability of ferroelectricity in the perovskite oxides is generally discussed with the Goldschimidt tolerance factor (t) (Goldschmidt, 1926 and Fig. 2),
where r\n\t\t\t\tO, r\n\t\t\t\tA, and r\n\t\t\t\tB are the ionic radii of the O, A, and B ions. For a critical value t=1, the cubic paraelectric phase is stable. This unique case can be found in SrTiO3, which has an ideal cubic perovskite structure at room temperature and doesn’t show ferroelectricity down to the absolute 0 K (Müller & Burkard, 1979). However, ferroelectricity can be induced by the substitution of O18 in this quantum paraelectric system at T<T\n\t\t\t\tc~23 K (Itoh et al., 1999). When t>1, since the B-site ion is too small for its site, it can shift off-centeringly, leading to the occurrence of displacive-type ferroelectricity in the crystal. Examples of such materials are BaTiO3 and KNbO3 (Shiozaki et al., 2001). On the other hand, for t<1, the perovskite oxides are in general not ferroelectrics because of different tilts and rotations of BO6 octahedra, which preserve the inversion symmetry. But exceptions may be found in the Bi-based materials, in which large A-site displacement is observed. This large A-site displacement is essentially attributed to the strong hybridization of Bi with oxygen (Baettig et al., 2005). Similar cases are observed in Pb-based materials, which commonly have large Pb displacement in the A-site (Egami et al., 1998) and strong covalent nature due to the unique stereochemistry of Pb (Cohen, 1992; Kuroiwa et al., 2001).
\n\t\t\t
Although BaTiO3- and PbTiO3-based ceramics materials have been widely used in electronic industry (Uchino, 1997; Scott, 2000), there remain some importance issues to be solved. One of such challenges is to seek novel compounds to replace the Pb-based materials, which have a large Pb-content and raises concerns about the environmental pollution (Saito et al.,2004; \n\t\t\t\t\tRodel et al.,2009\n\t\t\t\t).
\n\t\t\t
Figure 1.
The structure of an ABO3 perovskite with the origin centered at (a) the B-site ion and (b) the A-site ion.
\n\t\t\t
Figure 2.
Tolerance factor of typical dielectric oxides.
\n\t\t\t
The discovery of extremely large polarization (52 µC/cm2) under high electric field in the AgNbO3 ceramics (Fu et al., 2007) indicates that Ag may be a key element in the designs of lead-free ferroelectric perovskite oxides (Fu et al., 2011a). With the advance of first-principles calculations (Cohen, 1992) and modern techniques of synchrotron radiation (Kuroiwa et al., 2001), we now know that the chemical bonding in the perovskite oxides is not purely ionic as we have though, but also possesses covalent character that plays a crucial role in the occurrence of ferroelectricity in the perovskite oxides (Cohen, 1992; Kuroiwa et al., 2001 ). It is now accepted that it is the strong covalency of Pb with O that allows its large off-center in the A-site. Although Ag doesn’t have lone-pair electrons like Pb, theoretical investigations suggest that there is hybridization between Ag and O in AgNbO3(Kato et al., 2002; Grinberg & Rappe, 2003,2004), resulting in a large off-center of Ag in the A-site of perovskite AgNbO3 (Grinberg & Rappe, 2003,2004). This prediction is supported by the results from X-ray photoelectron spectroscopy, which suggest some covalent characters of the chemical bonds between Ag and O as well as the bonds between Nb and O (Kruczek et al., 2006). Moreover, bond-length analysis also supports such a theoretical prediction. Some of the bond-lengths (~2.43 Å) in the structure (Sciau et al., 2004; Yashima et al. 2011) are significantly less than the sum of Ag+ (1.28 Å) and O2- (1.40 Å) ionic radii (Shannon, 1967). All these facts make us believe that AgNbO3 may be used as a base compound to develop novel ferroelectric materials. Along such a direction, some interesting results have been obtained. It was found that ferroelectricity can be induced through the chemical modification of the AgNbO3 structure by substitution of Li (Fu et al., 2008, 2011a), Na(Arioka, 2009; Fu et al., 2011b), and K (Fu et al., 2009a) for Ag. Large spontaneous polarization and high temperature of para-ferroelectric phase transition were observed in these solid solutions. In the following sections, we review the synthesis, structure, and dielectric, piezoelectric and ferroelectric properties of these solid solutions together with another silver perovskite AgTaO3 (Soon et al.,2009, 2010), whose solid solutions with AgNbO3 are promising for the applications in microwaves devices due to high dielectric constant and low loss (Volkov et al. 1995; Fortin et al., 1996; Petzelt et al., 1999; Valant et al., 2007a).
\n\t\t
\n\t\t
\n\t\t\t
2. AgNbO3\n\t\t\t
\n\t\t\t
\n\t\t\t\t
2.1. Synthesis
\n\t\t\t\t
Both single crystal and ceramics of AgNbO3 are available. Single crystal can be grown by a molten salt method using NaCl or V2O5 as a flux (Łukaszewski et al., 1980; Kania, 1989). Ceramics samples can be prepared through a solid state reaction between Nb2O5 and silver source (Francombe & Lewis, 1958; Reisman & Holtzberg, 1958). Among the silver sources of metallic silver, Ag2O and Ag2CO3, Ag2O is mostly proper to obtain single phase of AgNbO3(Valant et al., 2007b). For silver source of Ag2O, thermogravimetric analysis indicates that phase formation can be reach at a firing temperature range of 1073-1397 K (Fig. 3). The issue frequently encountered in the synthesis of AgNbO3 is the decomposition of metallic silver, which can be easily justified from the color of the formed compounds. Pure powder is yellowish, while grey color of the powder generally indicates the presence of some metallic silver. It has been shown that the most important parameter that influences the phase formation is oxygen diffusion (Valant et al., 2007b). In our experiments to prepare the AgNbO3 ceramics, we first calcined the mixture of Ag2O and Nb2O5 at 1253 K for 6 hours in O2 atmosphere and then sintered the pellet samples for electric measurements at 1323 K for 6 hours in O2 atmosphere (Fu et al., 2007). Insulation of these samples is very excellent, which allows us to apply extremely high electric field to the sample (Breakdown field >220 kV/cm. For comparison, BaTiO3 ceramics has a value of ~50 kV/cm.)
\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
2.2 Electric-field induced ferroelectric phase
\n\t\t\t
Previous measurements on D-E hysteresis loop by Kania et al. (Kania et al., 1984) indicate that there is small spontaneous polarization P\n\t\t\t\ts in the ceramics sample of AgNbO3. P\n\t\t\t\ts was estimated to be ~0.04 µC/cm2 for an electric field with an amplitude of E=17 kV/cm and a frequency of 60 Hz. Our results obtained at weak field have confirmed Kania’s reports (Fig. 4 and Fu et al., 2007). The presence of spontaneous polarization indicates that AgNbO3 must be ferroelectric at room temperature. The good insulation of our samples allows us to reveal a novel ferroelectric state at higher electric field. As shown in Fig.4, double hysteresis loop is distinguishable under the application of E~120 kV/cm, indicating the appearance of new ferroelectric phase. When E>150 kV/cm, phase transformation is nearly completed and very large polarization was observed. At an electric field of E=220 kV/cm, we obtained a value of 52 µC/cm2 for the ceramics sample. Associating with such structural change, there is very large electromechanical coupling in the crystal. The induced strain was estimated to be 0.16% for the ceramics sample (Fig.5). The D-E loop results unambiguously indicate that the atomic displacements are ordered in a ferri-electric way rather than an anti-ferroelectric way in the crystal at room temperature.
\n\t\t\t
Figure 3.
Thermogravimetric curves of the AgNbO3 formation in air using Ag2O or metallic Ag powder as the silver source (Valant et al., 2007b). The curves are normalized to a weight of single-phase AgNbO3. Temperatures of phase formation completed and decomposition are also indicated. For case of Ag2O, decomposition of Ag2O into Ag and oxygen occurs at temperature of ~730 K.
\n\t\t\t
Figure 4.
D-E hysteresis loops for poly-crystalline AgNbO3 at room temperature.
\n\t\t\t
Figure 5.
Strain vs electric field for poly-crystalline AgNbO3 at room temperature.
\n\t\t\t
\n\t\t\t\t
2.3. Room-temperature structure
\n\t\t\t\t
There are many works attempting to determine the room-temperature structure of AgNbO3 (Francombe & Lewis, 1958; Verwerft et al., 1989; Fabry et al., 2000; Sciau et al., 2004; Levin et al., 2009). However, none of these previous works can provide a non-centrosymmetric structure to reasonably explain the observed spontaneous polarization. Very recently, this longstanding issue has been addressed by R. Sano et al. (Sano et al., 2010). The space group of AgNbO3 has been unambiguously determined to be Pmc2\n\t\t\t\t\t1 (No. 26) by the convergent-beam electron diffraction (CBED) technique, which is non-centrosymmetric and allows the appearance of ferroelectricity in the crystal (Fig.6).
\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\tPmc21 (T=298K)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Site
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\ty\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\tz\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\tU (Å2)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Ag1 4c\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
0.7499(3)
\n\t\t\t\t\t\t\t
0.7468(3)
\n\t\t\t\t\t\t\t
0.2601(5)
\n\t\t\t\t\t\t\t
0.0114(2)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Ag2 2b\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
1/2
\n\t\t\t\t\t\t\t
0.7466(6)
\n\t\t\t\t\t\t\t
0.2379(5)
\n\t\t\t\t\t\t\t
0.0114(2)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Ag3 2a\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
0
\n\t\t\t\t\t\t\t
0.7424(4)
\n\t\t\t\t\t\t\t
0.2759(6)
\n\t\t\t\t\t\t\t
0.0114(2)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Nb1 4c\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
0.6252(2)
\n\t\t\t\t\t\t\t
0.7525(5)
\n\t\t\t\t\t\t\t
0.7332(2)
\n\t\t\t\t\t\t\t
0.00389(18)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Nb2 4c\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
0.1253(2)
\n\t\t\t\t\t\t\t
0.24159
\n\t\t\t\t\t\t\t
0.27981
\n\t\t\t\t\t\t\t
0.00389(18)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
O1 4c\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
0.7521(9)
\n\t\t\t\t\t\t\t
0.7035(12)
\n\t\t\t\t\t\t\t
0.7832(24)
\n\t\t\t\t\t\t\t
0.0057(5)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
O2 2b\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
1/2
\n\t\t\t\t\t\t\t
0.804(3)
\n\t\t\t\t\t\t\t
0.796(3)
\n\t\t\t\t\t\t\t
0.0057(5)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
O3 4c\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
0.6057(7)
\n\t\t\t\t\t\t\t
0.5191(18)
\n\t\t\t\t\t\t\t
0.4943(18)
\n\t\t\t\t\t\t\t
0.0057(5)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
O4 4c\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
0.6423(7)
\n\t\t\t\t\t\t\t
0.0164(18)
\n\t\t\t\t\t\t\t
0.539(2)
\n\t\t\t\t\t\t\t
0.0057(5)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
O5 2a\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
0
\n\t\t\t\t\t\t\t
0.191(3)
\n\t\t\t\t\t\t\t
0.256(3)
\n\t\t\t\t\t\t\t
0.0057(5)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
O6 4c\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
0.1339(9)
\n\t\t\t\t\t\t\t
0.0410(17)
\n\t\t\t\t\t\t\t
0.980(2)
\n\t\t\t\t\t\t\t
0.0057(5)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
O7 4c\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
0.1154(8)
\n\t\t\t\t\t\t\t
0.4573(17)
\n\t\t\t\t\t\t\t
0.5514(19)
\n\t\t\t\t\t\t\t
0.0057(5)
\n\t\t\t\t\t\t
\n\t\t\t\t\t
Table 1.
Structural parameters of AgNbO3 at T=298K (Yashima et al., 2011). Number of formula units of AgNbO3 in a unit cell: Z=8. Unit-cell parameters: a = 15.64773(3) Å, b = 5.55199(1) Å, c = 5.60908(1) Å, α=β=γ= 90 deg., Unit-cell volume: V = 487.2940(17) Å3. U (Å2)=Isotropic atomic displacement parameter.
\n\t\t\t\t
Figure 6.
Convergent-beam electron diffraction (CBED) pattern of AgNbO3 taken at the [100] incidence. In contrast to a mirror symmetry perpendicular to the b*-axis, breaking of mirror symmetry perpendicular to the c*-axis is seen, indicating that spontaneous polarization is along the c-direction (Taken by R. Sano & K. Tsuda (Sano et al., 2010)).
\n\t\t\t\t
On the basis of this space group, M. Yashima (Yashima et al., 2011) exactly determined the atom positions (Table 1) in the structure using the neutron and synchrotron powder diffraction techniques. The atomic displacements are schematically shown in Fig.7. In contrast to the reported centrosymmetric Pbcm (Fabry et al. 2000; Sciau et al. 2004; Levin et al. 2009), in which the Ag and Nb atoms exhibit antiparallel displacements along the b-axis, the Pmc2\n\t\t\t\t\t1 structure shows a ferri-electric ordering of Ag and Nb displacements (Yashima et al., 2011) along the c-axis of Pmc2\n\t\t\t\t\t1 orthorhombic structure. This polar structure provides a reasonable interpretation for the observed polarization in AgNbO3.
\n\t\t\t\t
Figure 7.
a) Ferrielectric crystal structure of AgNbO3 (Pmc21) at room temperature. The atomic displacements along the c-axis lead to the spontaneous polarization in the crystal. (b) For comparison, the patterns for the previously reported Pbcm (Sciau et al., 2004) are also given. A cross (+) stands for the center of symmetry in the Pbcm structure. (by M. Yashima (Yashima et al., 2011) ).
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\n\t\t\t
\n\t\t\t\t
2.4. Dielectric behaviours and phase transitions
\n\t\t\t\t
Initial works on the phase transitions of AgNbO3 and their influence on the dielectric behaviors were reported by Francombe and Lewis (Francombe & Lewis, 1958) in the late 1950s, which trigger latter intensive interests in this system (Łukaszewski et al., 1983; Kania, 1983, 1998; Kania et al., 1984, 1986; Pisarski & Dmytrow, 1987; Paweczyk, 1987; Hafid et al., 1992; Petzelt et al., 1999; Ratuszna et al., 2003; Sciau et al., 2004). The phase transitions of AgNbO3 were associated with two mechanisms of displacive phase transition: tilting of oxygen octahedra and displacements of particular ions (Sciau et al., 2004). Due to these two mechanisms, a series of structural phase transitions are observed in AgNbO3. The results on dielectric behaviors together with the reported phase transitions are summarized in Fig.8. Briefly speaking, the structures of the room-temperature (Yashima et al., 2011) and the high temperature phases (T> T\n\t\t\t\t\tO1-O2=634 K ) (Sciau et al., 2004) are exactly determined, in contrast, those of low-temperature (T<room temperature) and intermediate phases ( T\n\t\t\t\t\tC\n\t\t\t\t\tFE=345 K <T< T\n\t\t\t\t\tO1-O2) remain to be clarified. In the dielectric curve, we can see a shoulder around 40 K. It is unknown whether this anomaly is related to a phase transition or not. It should be noticed that Shigemi et al. predicted a ground state of R3c rhombohedra phase similar to that of NaNbO3 for AgNbO3 (Shigemi & Wada, 2008). Upon heating, there is an anomaly at T\n\t\t\t\t\tC\n\t\t\t\t\tFE=345 K, above which spontaneous polarization was reported to disappear (Fig.8 (c) and\n\t\t\t\t\tKania et al., 1984). At the same temperature, anomaly of lattice distortion was observed (Fig.8 (c)and Levin et al., 2009). The dielectric anomaly at T\n\t\t\t\t\tC\n\t\t\t\t\tFE=345 K was attributed to be a ferroelectric phase transition. Upon further heating, there is a small peak at T=453 K, which
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15.771(2)
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7.906(1)
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3.9595(3)
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Table 2.
Structural parameters for high temperature phases at 573 K(Pbcm), 645 K(Cmcm), 733 K (P4/mbm), and 903 K(Pm3m) (Sciau et al., 2004).
\n\t\t\t\t
is not so visible. However, it can be easily ascertained in the differential curve or in the cooling curve. This anomaly is nearly unnoticed in the literatures (Łukaszewski et al., 1983; Kania, 1983, 1998; Kania et al., 1986; Pisarski & Dmytrow, 1987; Paweczyk, 1987; Hafid et al., 1992; Ratuszna et al., 2003). The detailed examination of the temperature dependence of the 220o\n\t\t\t\t\td-spacing (reflection was indexed with orthorhombic structure) determined by Levin et al. (Levin et al., 2009 and Fig.8(c)) reveals anomaly that can be associated with this dielectric peak. These facts suggest that a phase transition possibly occurs at this temperature. Around 540 K, there is a broad and frequency-dependent peak of dielectric constant, which is also associated with an anomaly of 220o\n\t\t\t\t\td-spacing. However, current structural investigations using x-ray and neutron diffraction do not find any symmetric changes associated with the dielectric anomalies at 456 K and 540 K, and the structure within this intermediate temperature range was assumed to be orthorhombic Pbcm (Sciau et al., 2004). At T=T\n\t\t\t\t\tC\n\t\t\t\t\tAFE=631 K, there is a sharp jump of dielectric constant due to an antiferroelectric phase transition (Pisarski et al., 1987; Sciau et al., 2004). The atomic displacement patterns in the antiferroelectric phase (Pbcm) are shown in Fig.7 (b) using the structural parameters determined by Ph Sciau et al. (Sciau et al., 2004). For T>T\n\t\t\t\t\tC\n\t\t\t\t\tAFE, there are still three phase transitions that are essentially derived by the tilting of oxygen octahedral and have only slight influence on the dielectric constant. For conveniences, the tilting of octahedral (Sciau et al., 2004) described in Glazer’s notation is given in Fig.8 and the structure parameters (Sciau et al., 2004) are relisted in Table 2.
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Figure 8.
Temperature dependence of (a) dielectric constant, (b) dielectric loss, and (c) polarization (Kania et al., 1984) and 220\n\t\t\t\t\t\t\t\tO\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\td-spacing of the lattice (Levin et al., 2009).
Li can be incorporated into the Ag-site of AgNbO3. However, due to the large difference of ionic radius of Li+(0.92Å), and Ag+( 1.28Å)(Shannon, 1976), the solid solution is very limited. Nalbandyan et al.(Nalbandyan et al., 1980), systematically studied the stable and metastable phase equilibrias and showed that solid solution limit is narrow (x~0.02) for the stable phase, but is relatively wide (x~0.12) for the metastable phase(Sakabe et al., 2001; Takeda et al., 2003; Fu et al., 2008, 2011a). With a small substitution of Li for Ag (x>x\n\t\t\t\t\n\t\t\t\t\tc\n\t\t\t\t\n\t\t\t\t=0.05~0.06), a ferroelectric rhombohedra phase is evolved in the solid solution (Nalbandyan et al., 1980; Sakabe et al., 2001; Takeda et al., 2003;Fu et al., 2008, 2011a). In this solid solution, the strong off-center of small Li (Bilc & Singh, 2006) plays an important role in triggering the ferroelectric state with large spontaneous polarization (Fu et al., 2008, 2011a).
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3.1. Synthesis
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Single crystals of (Ag1-x\n\t\t\t\t\tLi\n\t\t\t\t\t\tx\n\t\t\t\t\t)NbO3 can be obtained by the melt growth process (Fu et al., 2008). Stoichiometric compositions of Ag2O, Li2CO3, and Nb2O5 were mixed and calcined at 1253 K for 6 h in an oxygen atmosphere. The calcined powder was milled, put into an alumina container, and melted at 1423 K for 4 h in an oxygen atmosphere. The melt was cooled to 1323 K to form the crystal at a rate of 4 K/h, followed by furnace cooling down to room temperature. Using this process, single crystals with size of 1–3 cm can be obtained for the (100)\n\t\t\t\t\t\tp\n\t\t\t\t\t (Hereafter, subscript p indicates pseudocubic structure) growth face. Due to the volatility of lithium at high temperature, the exact chemical composition of the crystal is generally deviated from the starting composition and is required to be determined with methods like inductively coupled plasma spectrometry.
\n\t\t\t\t
Ceramics samples can be prepared by a solid state reaction approach. Mixtures of Ag2O, Nb2O5, and Li2CO3 were calcined at 1253 K for 6 h in O2 atmosphere, followed by removal of the powder from the furnace to allow a rapid cooling to prevent phase separation. The calcined powder was milled again and pressed to form pellets that were sintered at 1323 K for 6 h in O2 atmosphere, followed by a rapid cooling.
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\n\t\t\t
\n\t\t\t\t
3.2. Structure
\n\t\t\t\t
The structural refinements using the powder X-ray diffraction data suggest that (Ag1-x\n\t\t\t\t\tLi\n\t\t\t\t\t\tx\n\t\t\t\t\t)NbO3 solid solution with x>x\n\t\t\t\t\tc has the space group of R3c (Fu et al., 2011a). Table 3 lists the structural parameters of this model for composition x=0.1. Figure 9 shows a schematic drawing for this structure. In this rhombohedral R3c phase, the spontaneous polarization is essentially due to the atomic displacements of the Ag/Li, Nb, and O atoms along the pseudocubic [111] direction.
\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Ag0.9Li0.1NbO3 (R3c, No.161, T=room temperature)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\ta(Å)
\n\t\t\t\t\t\t\t
5.52055(9)
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
α
\n\t\t\t\t\t\t\t
90
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\tb(Å)
\n\t\t\t\t\t\t\t
5.52055(9)
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
β
\n\t\t\t\t\t\t\t
90
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\tc(Å)
\n\t\t\t\t\t\t\t
13.7938(3)
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
γ
\n\t\t\t\t\t\t\t
120
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
V(Å3)
\n\t\t\t\t\t\t\t
364.07
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Atom
\n\t\t\t\t\t\t\t
Site
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\ty\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\tz\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\tU (Å2)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Ag/Li
\n\t\t\t\t\t\t\t
6a
\n\t\t\t\t\t\t\t
0
\n\t\t\t\t\t\t\t
0
\n\t\t\t\t\t\t\t
0.2545(8)
\n\t\t\t\t\t\t\t
0.5
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Nb
\n\t\t\t\t\t\t\t
6a
\n\t\t\t\t\t\t\t
0
\n\t\t\t\t\t\t\t
0
\n\t\t\t\t\t\t\t
0.0097(8)
\n\t\t\t\t\t\t\t
0.5
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
O
\n\t\t\t\t\t\t\t
18b
\n\t\t\t\t\t\t\t
0.5533
\n\t\t\t\t\t\t\t
1
\n\t\t\t\t\t\t\t
0.2599(9)
\n\t\t\t\t\t\t\t
0.5
\n\t\t\t\t\t\t
\n\t\t\t\t\t
Table 3.
Structural parameters for rhombohedra structure of (Ag,Li)NbO3 solid solution.
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3.3. Ferroelectric and piezoelectric properties
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Evolution of the polarization state in Ag1-x\n\t\t\t\t\tLi\n\t\t\t\t\t\tx\n\t\t\t\t\tNbO3 solid solutions is shown in Fig.10. Basically, when x<x\n\t\t\t\t\tc, the solid solutions have the ferrielectric state of pure AgNbO3 with a small spontaneous polarization at zero electric field. In contrast, when x>x\n\t\t\t\t\tc, a normal
\n\t\t\t\t
Figure 9.
Schematic structure of Ag1-x\n\t\t\t\t\t\t\tLi\n\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\tNbO3 (x = 0.1) with symmetry R3c (No.161).
\n\t\t\t\t
ferroelectric state with large value of remanent polarization (P\n\t\t\t\t\tr) is observed. All ceramics samples show P\n\t\t\t\t\tr value comparable to P\n\t\t\t\t\tS of BaTiO3 single crystal (26 µC/cm2) (Shiozaki et al., 2001). Moreover, the polarization in Ag1-x\n\t\t\t\t\tLi\n\t\t\t\t\t\tx\n\t\t\t\t\tNbO3 solid solution is very stable after switching. Large P\n\t\t\t\t\tr value and ideal bistable polarization state of Ag1-x\n\t\t\t\t\tLi\n\t\t\t\t\t\tx\n\t\t\t\t\tNbO3 ceramics may be interesting for non-volatile ferroelectric memory applications. Measurements on single crystal samples (Fig.11) indicate that saturation polarization along the <111>\n\t\t\t\t\t\tp\n\t\t\t\t\t rhombohedra direction (\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tP\n\t\t\t\t\t\t\t\t\ts\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t111\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t~40 µC/cm2) is greatly larger than that along the <001>\n\t\t\t\t\t\tp\n\t\t\t\t\t tetragonal direction (\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tP\n\t\t\t\t\t\t\t\t\ts\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t001\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t~24 µC/cm2) and the ratio between them is \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t3\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t (Fu et al., 2008), which is in good agreement with results of structural refinements. This suggests that the polar axis is the <111>\n\t\t\t\t\t\tp\n\t\t\t\t\t direction of pseudo-cubic structure.
\n\t\t\t\t
The strain-E loops indicate that there are good electromechanical coupling effects in Ag1-x\n\t\t\t\t\tLi\n\t\t\t\t\t\tx\n\t\t\t\t\tNbO3 crystals. Although the spontaneous polarization is along the <111>\n\t\t\t\t\t\tp\n\t\t\t\t\t axis, the <001>\n\t\t\t\t\t\tp\n\t\t\t\t\t-cut crystal shows larger strain and less hysteresis than the <111>\n\t\t\t\t\t\tp\n\t\t\t\t\t-cut one (Fig.11 (b) and (c)). These phenomena are very similar to those reported for the relaxor-ferroelectric crystals (Wada et al., 1998). The most significant result exhibited from Ag1-x\n\t\t\t\t\tLi\n\t\t\t\t\t\tx\n\t\t\t\t\tNbO3 single crystal is its excellent g\n\t\t\t\t\t33 value that determines the voltage output of the piezoelectric device under the application of an external stress (Fu et al., 2008). The g\n\t\t\t\t\t33 value together the d\n\t\t\t\t\t33 value and dielectric constants for the <001>\n\t\t\t\t\t\tp\n\t\t\t\t\t-cut single crystals are shown in Fig. 12. The high g\n\t\t\t\t\t33 value is a direct result from the large d\n\t\t\t\t\t33 constant and the low dielectric constant of the single crystal.
\n\t\t\t\t
Figure 10.
Typical D-E and strain-E loops for the Ag1-x\n\t\t\t\t\t\t\tLi\n\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\tNbO3 ceramics samples.
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Figure 11.
a) D-E loops, (b) strain vs E for bipolar electric field, and (c) strain vs E for unipolar field for Ag1-x\n\t\t\t\t\t\t\tLi\n\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\tNbO3 single crystal with x=0.062.
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Figure 12.
Composition dependence of dielectric constant ε, d\n\t\t\t\t\t\t\t33 and g\n\t\t\t\t\t\t\t33 for the <001>\n\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t-cut Ag1-x\n\t\t\t\t\t\t\tLi\n\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\tNbO3 single crystals.
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3.4. Dielectric behaviours and proposed phase diagram
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\n\t\t\t\t\tFigure 13 shows the dielectric behaviours of the ferroelectric Ag1-x\n\t\t\t\t\tLi\n\t\t\t\t\t\tx\n\t\t\t\t\tNbO3 solid solutions. For comparison, the temperature dependence of dielectric constant of AgNbO3 is also shown. It can be seen that solid solution with x > x\n\t\t\t\t\tc shows different temperature evolutions of the dielectric constant as compared with AgNbO3. In sharp contrast to the complex successive phase transitions in AgNbO3, ferroelectric Ag1-x\n\t\t\t\t\tLi\n\t\t\t\t\t\tx\n\t\t\t\t\tNbO3 solid solutions (x>x\n\t\t\t\t\tc) show only two phase transitions in the temperature range of 0-820 K. Polarization measurements suggest that the high temperature phase (\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tFE\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t) is nonpolar, thus it seems
\n\t\t\t\t
Figure 13.
Typical dielectric behaviours of Ag1-x\n\t\t\t\t\t\t\tLi\n\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\tNbO3 ceramics in comparison with that of pure AgNbO3.
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Figure 14.
Phase diagram proposed for Ag1-x\n\t\t\t\t\t\t\t Li\n\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\tNbO3 solid solution. The gray area indicates the phase boundary (Fu et al., 2011a).
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that the higher temperature phase transition is not related to a ferroelectric phase transition. On the basis of the dielectric measurements, the phase diagram of Ag1-x\n\t\t\t\t\tLi\n\t\t\t\t\t\tx\n\t\t\t\t\tNbO3 solution is summarized in Fig.14, where T, O, R and M represent tetragonal, orthorhombic, rhombohedra, and monoclinic symmetries, respectively. At room temperature, structure transformation from O to R phase at x\n\t\t\t\t\tc dramatically changes the polar nature of Ag1-x\n\t\t\t\t\tLi\n\t\t\t\t\t\tx\n\t\t\t\t\tNbO3. It is a ferrielectric with small spontaneous polarization in the O phase, but becomes a ferroelectric with large polarization in the R phase.
The ionic radius of Na+ (1.18 Å) is comparable to that of Ag+ (1.28 Å) (Shannon, 1976), allowing to prepare the Ag1-x\n\t\t\t\tNa\n\t\t\t\t\tx\n\t\t\t\tNbO3 solid solution within the whole range of composition (x=0-1) (Kania & Kwapulinski, 1999). Kania et al. previously performed investigation on the dielectric behaviors and the differential thermal analysis for the Ag1-x\n\t\t\t\tNa\n\t\t\t\t\tx\n\t\t\t\tNbO3 solid solutions, and stated that the solid solution evolves from disordered antiferroelectric AgNbO3 to normal antiferroelectric NaNbO3. As described in section §2.3, we now know that AgNbO3 is not antiferroelectric but rather is ferrielectric at room temperature (Yashima et al., 2011). Moreover, recent reexamination on the polarization behaviors of stoichiometric and non-stoichiometric NaNbO3 polycrystallines indicates that the reported clamping hysteresis loop of NaNbO3 can be interpreted by pining effects while stoichiometric NaNbO3 is intrinsically ferroelectric (Arioka et al., 2010). Therefore, reexamination on this solid solution is necessary. Evolution of the polarization with composition clearly indicates that the solid solution evolves from ferrielectric AgNbO3 to ferroelectric NaNbO3 (Fu et al., 2011b).
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\n\t\t\t\t
4.1. Synthesis
\n\t\t\t\t
Ag1-x\n\t\t\t\t\tNa\n\t\t\t\t\t\tx\n\t\t\t\t\tNbO3 solid solution was prepared by a solid state reaction approach. Mixtures of Ag2O(99%), Nb2O5 (99.99%), and Na2CO3 (99.99%) were calcined at 1173 K for 4 h in O2 atmosphere. The calcined powder was ground, pressed into pellet with a diameter of 10 mm at thickness of 2 mm, and sintered with the conditions listed in Table 4.
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\n\t\t\t\t\t\t
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Composition
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Temperature
\n\t\t\t\t\t\t\t
Time
\n\t\t\t\t\t\t\t
atmosphere
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\tx=0,0.1,0.2
\n\t\t\t\t\t\t\t
1273K
\n\t\t\t\t\t\t\t
5h
\n\t\t\t\t\t\t\t
O2\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\tx=0.4, 0.5, 0.6
\n\t\t\t\t\t\t\t
1323K
\n\t\t\t\t\t\t\t
5h
\n\t\t\t\t\t\t\t
O2\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\tx= 0.8,0.9,1
\n\t\t\t\t\t\t\t
1373K
\n\t\t\t\t\t\t\t
5h
\n\t\t\t\t\t\t\t
O2\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
Table 4.
Sintering conditions for Ag1-xNaxNbO3.
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\n\t\t\t\t
4.2. Polarization
\n\t\t\t\t
\n\t\t\t\t\tFigure 15 shows the change in polarization with composition in the Ag1-x\n\t\t\t\t\tNa\n\t\t\t\t\t\tx\n\t\t\t\t\tNbO3 solid solutions. For a wide range of composition x<0.8, the solid solution possesses the characteristic polarization behaviours of pure AgNbO3: small spontaneous polarization at E=0 but large polarization at E> a critical field. This fact suggests that the solid solution is ferrielectric within this composition range. This is also supported by the temperature dependence of dielectric constant (Fig.16). On the other hand, for the Na-rich composition, particularly, x>0.8, we observed large remanent polarization with value close to the saturation polarization at high field. This result indicates that a normal ferroelectric phase is stable in these compositions. Therefore, polarization measurements show that the Ag1-x\n\t\t\t\t\tNa\n\t\t\t\t\t\tx\n\t\t\t\t\tNbO3 solid solution evolves from ferrielectric AgNbO3 to ferroelectric NaNbO3 (Fu et al., 2011b).
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\n\t\t\t
\n\t\t\t\t
4.3. Dielectric properties
\n\t\t\t\t
The dielectric properties of the Ag1-x\n\t\t\t\t\tNa\n\t\t\t\t\t\tx\n\t\t\t\t\tNbO3 solid solutions are summarized in Fig.16. The change in dielectric behaviours with composition is very similar to that observed in polarization measurements (Fig.15). For x≤0.8, the solid solution shows successive phase transitions similar to pure AgNbO3. In contrast, it has the characteristic phase transition of pure NaNbO3 for x>0.8. The composition dependence of transition temperature derived from the dielectric measurements is shown in Fig.17. Two noticed features may be seen: (a) the thermal hysteresis is extremely large for antiferroelectric phase transition and reaches a value greater than 100 K at x~0.5. Such large thermal hysteresis is rarely observed in normal polar phase transition. (b) It seems that there is a phase boundary at x=xc~0.8 (Fu et al., 2011b), around which structural transformation between ferri- and ferro-electric phases occurs.
(Ag1-x\n\t\t\t\tK\n\t\t\t\t\tx\n\t\t\t\t)NbO3 solid solutions are available only for very limited composition (Weirauch & Tennery, 1967; Łukaszewski, 1983; Kania, 2001). Weirauch et al. reported that solid solution of AgNbO3 in KNbO3 was limited to slightly less than 6 mole % and solid solution of KNbO3 in AgNbO3 was limited to less than 0.5 mole % (Weirauch & Tennery, 1967). However, our process indicates that KNbO3 and AgNbO3 can be alloyed with each other within 20 mole % (Fu et al., 2009a). Apparently, the reported solid solution limit is dependent on the process. In our samples, we found that a ferroelectric phase with large spontaneous polarization can be induced by substitution of K for Ag for x>x\n\t\t\t\tc1=0.07. This ferroelectric phase shows nearly composition-independent ferroelectric phase transition. On the other hand, the K-rich solid solution (x>0.8) possesses the ferroelectric phase transition sequence of pure KNbO3 and the transition temperature is dependent on the composition.
\n\t\t\t
\n\t\t\t\t
5.1. Synthesis
\n\t\t\t\t
(Ag1-x\n\t\t\t\t\tK\n\t\t\t\t\t\tx\n\t\t\t\t\t)NbO3 solid solutions were prepared by a solid state reaction method. Mixture of Ag2O, Nb2O5, and K2CO3 were calcined at 1173 K for 6 h in O2 atmosphere with a slow heating rate of 1 K/min. The calcined powder was milled again, pressed in a 6-mm steel die with a pressure of 10 MPa to form the pellets, which were then preheated at 773 K for 2 h, followed by a sintering at temperature of 1253–1323 K (1323 K for x=0–0.1 and 1.00, 1273 K for x=0.15 and 0.90, and 1253 K for x=0.17 and 0.80, respectively) for 3 h in O2 atmosphere. The atmosphere and heating rate are found to have significant influences on the phase stability of the solid solution.
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\n\t\t\t\t
5.2. Structural change with composition
\n\t\t\t\t
\n\t\t\t\t\tFigure 18 shows the change in lattice parameters with composition in the Ag1-x\n\t\t\t\t\tK\n\t\t\t\t\t\tx\n\t\t\t\t\tNbO3 solid solutions. When the amount of substitution is small, the solid solution possesses the orthorhombic structure of pure AgNbO3. In the phase boundary x\n\t\t\t\t\tc1=0.07, structural transformation occurs and the phase changes into a new orthorhombic structure. In this new ferroelectric phase, the lattice constants show linear increase with composition. Interestingly, the orthorhombic distortion angle βis nearly independent with the composition. In the K-rich region (x>x\n\t\t\t\t\tc3=0.8), the solid solution has the orthorhombic structure of pure KNbO3 at room-temperature, which is also ferroelectric. In contrast to nearly unchanged orthorhombic angle βin the Ag-rich orthorhombic phase, βshows monotonous decreases with x in the K-rich phase.
\n\t\t\t\t
Figure 15.
Composition dependence of D-E loops obtained at room temperature for Ag1-x\n\t\t\t\t\t\t\tNaxNbO3 solid solutions
\n\t\t\t\t
Figure 16.
Temperature dependence of dielectric constants for Ag1-x\n\t\t\t\t\t\t\tNaxNbO3 solid solutions.
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Figure 17.
Composition dependence of phase transition temperatures detected from dielectric measurements for Ag1-x\n\t\t\t\t\t\t\tNaxNbO3 solid solutions.
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5. 3. Ferroelectric and piezoelectric properties
\n\t\t\t
\n\t\t\t\tFigure 19 shows typical results of polarization and strain behaviors observed at room temperature for the Ag1-x\n\t\t\t\tK\n\t\t\t\t\tx\n\t\t\t\tNbO3 solid solutions. Similar to pure AgNbO3, merely a small spontaneous polarization was observed in samples with x<x\n\t\t\t\tc1. However, when x>x\n\t\t\t\tc1, a normal D-E loop with large value of remanent polarization P\n\t\t\t\tr was observed. A value of P\n\t\t\t\tr=20.5 µC/cm2 was obtained for a ceramics sample with x =0.10, which is greatly larger than that observed for BaTiO3 ceramics (Fu et al., 2010). These results show that Ag-rich orthorhombic phase (x\n\t\t\t\tc1<x<x\n\t\t\t\tc2 ) is really under a ferroelectric state with large polarization. For K-rich region x>x\n\t\t\t\tc3, normal D-E loops were also obtained. Associating with the evolution into the ferroelectric phase, butterfly strain curve were observed. The piezoelectric constants determined from the piezo-d\n\t\t\t\t33 meter generally have values of 46–64 pC/N for these ferroelectric samples (Fu et al., 2009a).
\n\t\t\t
Figure 18.
Lattice parameters change with composition in Ag1-x\n\t\t\t\t\t\tK\n\t\t\t\t\t\t\tx\n\t\t\t\t\t\tNbO3 solid solution. β is the orthorhombic distortion angle. The inset shows the orthorhombic distortion due to the ferroelectric displacements along the <110>\n\t\t\t\t\t\t\tp\n\t\t\t\t\t\t direction of the pseudocubic structure.
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Figure 19.
Changes of polarization and strain under the application of electric field with composition at room temperature.
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5.4. Dielectric behaviours and proposed phase diagram
\n\t\t\t\t
Associating with the change in structure, dielectric behaviours of the Ag1-x\n\t\t\t\t\tK\n\t\t\t\t\t\tx\n\t\t\t\t\tNbO3 solid solution also change with composition. As shown in Fig.20, the temperature dependence of dielectric constant can be sorted by three types: (1) AgNbO3-type for x<x\n\t\t\t\t\tc1=0.07, (2) KNbO3-type for K-rich region x>x\n\t\t\t\t\tc3=0.8, and (3) a new type for the intermediate composition x\n\t\t\t\t\tc1<x<x\n\t\t\t\t\tc2. In this intermediate composition, dielectric measurements indicate that there are two phase transitions within the temperature range of 0-750 K. One transition locates at T\n\t\t\t\t\tc2~420 K with thermal hysteresis and shows small change in dielectric constant, which seems to be due to a ferro-to-ferro-electric phase transition. Another phase transition occurs at T\n\t\t\t\t\tc1~525 K. Around this transition, the dielectric constant changes sharply and follows exactly the Curie-Weiss law. The Curie-Weiss constant was estimated to be 1.47 *105 K for x=0.1 sample, which is a typical value for the displacive ferroelectric transition, suggesting that this is a displacive type ferroelectric transition. A phase diagram is proposed in Fig.21, in which PE, FE, and AFE represent the paraelectric, ferroelectric, and antiferroelectric phases, respectively. When carefully comparing the phase transition temperature with the orthorhombic angle β(Fig.18) due to the ferroelectric distortion (For x\n\t\t\t\t\tc1<x<x\n\t\t\t\t\tc2 and x>x\n\t\t\t\t\tc3. In contrast, for x< x\n\t\t\t\t\tc1, β change is basically due to the oxygen-octahedral tilting.), one might find that there is a correlation between β and the temperature of the ferroelectric phase transition.
\n\t\t\t\t
Figure 20.
Temperature dependence of dielectric constant for Ag1−x\n\t\t\t\t\t\t\tK\n\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\tNbO3 solid solution.
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6. AgTaO3\n\t\t\t
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AgTaO3 is another oxide of the two discovered silver perovskites (Francombe & Lewis, 1958). It is generally accepted that AgTaO3 undergoes a series of phase transitions from rhombohedral phase (T≤685 K) to monoclinic phase (650 K≤T≤703 K) and then to tetragonal phase (685 K<T≤780 K), and finally to cubic phase at T\n\t\t\t\tT-C= 780 K upon heating (Francombe & Lewis, 1958; Kania, 1983; Paweczyk, 1987; Kugel et al.,1987; Hafid et al., 1992). However, due to the coexistence regions between rhombohedral and monoclinic, and monoclinic and tetragonal, the actual transition temperatures from rhombohedral to monoclinic T\n\t\t\t\tR-M as well as that from monoclinic to tetragonal T\n\t\t\t\tM-T still remain uncertain. Furthermore, the ground state and the origins that trigger these phase transitions still remain to be addressed (Wołcyrz & Łukaszewski,1986; Kugel et al.,1987;Soon et al., 2010).
\n\t\t\t
Figure 21.
Phase diagram proposed for the Ag1−x\n\t\t\t\t\t\tK\n\t\t\t\t\t\t\tx\n\t\t\t\t\t\tNbO3 solid solution. Phase boundaries are indicated by the dashed lines. C, T, O, and R indicate the cubic, tetragonal, orthorhombic, and rhombohedral symmetries, respectively. PE, FE and AFE represent paraelectric, ferroelectric, and antiferroelectric phases, respectively.
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6.1. Synthesis
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Although single crystal of AgTaO3 is available through a flux method (Łukaszewski et al., 1980; Kania, 1989), it is extremely difficult to prepare its dense ceramics sample (Francombe & Lewis, 1958;Kania,1983) for electrical measurements. Since decomposition of AgTaO3 occurs at 1443±3 K in atmosphere (Valant et al., 2007b), sintering cannot be performed at higher temperatures to obtain dense ceramics. However, this long-standing synthesis difficulty now can be solved by a processing route involving the conventional solid-state reaction and sintering in environment with a high oxygen pressure at ~13 atm (Soon et al., 2010). In this synthesizing route, Ag2O and Ta2O5, first underwent a grind mixing and was calcined at 1273 K for 6 hours. The calcined powder was then pressed into a pellet in 6 mm in diameter. Sintering was carried out by placing the powder compact into a sealed zirconia tube that was connected to a pressure control valve (Fig.22). Prior to the sintering, oxygen gas at ~6.25 atm was filled into the sealed zirconia tube after the evacuation. Upon heating, the pressure of sealed oxygen gas increased and reached ~13 atm when the powder compact was sintered at 1573K for 2 hours. This eventually led to formation of dense polycrystalline AgTaO3.
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Figure 22.
Schematic diagram of the self-customized furnace employed for the sintering at high oxygen pressure.
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6.2. Phase formation and dielectric behaviors
\n\t\t\t\t
X-ray diffraction analyses (Fig.23) suggest the AgTaO3 is rhombohedral with \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tR\n\t\t\t\t\t\t\t\t3\n\t\t\t\t\t\t\t\t¯\n\t\t\t\t\t\t\t\t\tc\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t symmetry at room temperature (Wołcyrz & Łukaszewski, 1986). Diffraction patterns obtained at 68.4 K remain unchanged, indicating that such nonpolar phase persists down to low-temperatures. This is also supported by the measurements on the dielectric constants (Fig.24) and heat capacity (Fig.25), in which no anomaly was probed in the low temperatures.
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Figure 23.
XRD traces for AgTaO3 obtained at 300 K and 68.4 K together with the standard pattern given by the powder diffraction file (PDF) No. 01-072-1383.
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Figure 24.
Temperature dependence of (a) ε together with the fitting to the Barrett’s relation indicated by the dashed line and (b) tanδ for AgTaO3 ceramics samples.
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Figure 25.
Heat capacity for AgTaO3.
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Although several frequency-dependent peaks are seen in the dielectric loss (Fig.24(b)), it can be reasonably attributed to polarization relaxations due to defects (Soon et al., 2010). Interestingly, within the low-temperature region, the dielectric behavior follows the Barrett’s relation (Barrett, 1952) that is characteristic for the quantum paraelectric system (Abel, 1971; Höchli & Boatner,1979; Itoh et al., 1999), suggesting that AgTaO3 may be a quantum paraelectric. On the other hand, two step-like dielectric anomalies corresponding to the phase transitions from monoclinic to tetragonal and tetragonal to cubic were observed at 694 and 780 K, respectively, upon heating the samples (Fig.26). This observation is in agreement with the previous reports (Kania,1983;Kugel et al., 1987). Furthermore, the temperature dependence of 1/ε for AgTaO3 shows non-linear behavior, which is similar to that of KTaO3, obeying the modified form of the Curie-Weiss law ε=ε\n\t\t\t\t\tL+C/(T-T\n\t\t\t\t\t0) (Rupprecht & Bell, 1964).
\n\t\t\t\t
Figure 26.
Temperature dependences of ε′ and 1/ε′ fitted to the modified Curie-Weiss law ε=ε\n\t\t\t\t\t\t\tL+C/(T-T\n\t\t\t\t\t\t\t0) (dashed line, ε\n\t\t\t\t\t\t\tL= 38.2, C=4.7*104 K, T\n\t\t\t\t\t\t\t0= -137 K) for AgTaO3 measured at 1 MHz upon heating. The inset further shows the step-like phase transitions from monoclinic to tetragonal and then to cubic at T\n\t\t\t\t\t\t\tM-T= 694 K and T\n\t\t\t\t\t\t\tT-C =780 K, respectively.
Similar to the case of AgNbO3, ~12 mole% of Li can be incorporated into the Ag-site of AgTaO3 to form (Ag1-x\n\t\t\t\tLi\n\t\t\t\t\tx\n\t\t\t\t)TaO3 (Soon et al., 2009). Although the transition temperature is lower than room temperature, ferroelectricity can be induced in this solid solution due to the strong off-centering nature of the small Li ions.
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7.1. Synthesis
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(Ag1-x\n\t\t\t\t\tLi\n\t\t\t\t\t\tx\n\t\t\t\t\t)TaO3 was prepared by the conventional solid-state reaction with Ag2O, Ta2O5 and Li2CO3, where the powder mixture was calcined at 1273 K for 6 hours. The calcined powder was then pressed into a pellet with 6 mm in diameter. Sintering was carried out by the same high-pressure process used for pure AgTaO3, which eventually led to formation of dense ceramics samples of (Ag1-x\n\t\t\t\t\tLi\n\t\t\t\t\t\tx\n\t\t\t\t\t)TaO3\n\t\t\t\t
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7.2. Dielectric behaviours and confirmation of ferroelectric phase
\n\t\t\t\t
\n\t\t\t\t\tFigures 27& 28 plot the temperature dependence of dielectric constant ε and loss tanδ for (Ag1-x\n\t\t\t\t\tLi\n\t\t\t\t\t\tx\n\t\t\t\t\t)TaO3 with x≤ 0.12 obtained at frequencies ranging from 1 kHz to 1 MHz, respectively. It can be seen that a dielectric peak was gradually induced by Li+ substitution in AgTaO3. In contrast to the single peak of the dielectric constant, there are two to four peaks of the dielectric loss within the same temperature window. Since the additional loss peaks do not associate with a remarkable change in the dielectric constant, it is very likely due to the defect effects dependent with sample processing. It can be seen that a well-defined peaks has occurred in the ~MHz frequency regions for the substitution at extremely low level, for example, at x=0.02. This indicates the existence of local polarization in the doped crystal (Vugmeister & Glinchuk, 1990; Samara, 2003). This fact again suggests that AgTaO3 is actually under a critical state of the quantum paraelectric. Any slight modification will lead to the appearance of observable polarization in the system. For small substitution, the location of the dielectric anomaly depends on the observed frequency. Figure 29 gives an evaluation on this frequency dependence. In the figure, the temperature-axis is scaled with the peak position of 1 MHz, making it easy to see the change with composition. For x=0.008, the frequency dispersion is very strong, peak position of the dielectric constant shifts about 50% with respect to that of 1 MHz for f= 1 kHz. However, for x=0.035, such change is less than 2%, meaning that ferroelectric phase transition temperature T\n\t\t\t\t\tc is well defined in the sample. Thus, we can infer that a macroscopic ferroelectric phase is evolved below T\n\t\t\t\t\tc in this composition. This was also confirmed by the results shown in Fig.30, in which ferroelectric loop was obtained for T<T\n\t\t\t\t\tc for a sample with x=0.12 that has the same dielectric behaviors to that of x=0.035.
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On the basis of the above results, a phase diagram is proposed in Fig.31, in which PE and FE represents the paraelectric and ferroelectric phases, respectively. As mentioned above, since the peak of the dielectric constant is strongly dependent with frequency for 0<x<0.035, the gray zone in the phase diagram may be attributed to dipole-glass phase(Vugmeister & Glinchuk, 1990;Pirc & Blinc, 1999;Samara, 2003) or a phase with nanosized ferroelectric domains (Fisch, 2003; Fu et al., 2009b), which remains to be addressed by further investigations.
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8. Concluding remarks
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Our recent works reveal that silver perovskites are of great interests from either the view-point of fundamental research or that of application research in the fields of ferroelectric or piezoelectric. Promising ferroelectric and piezoelectric properties have been demonstrated in some compounds such as (Ag,Li)NbO3 and (Ag,K)NbO3 alloys, but further works are required to improve the material performance, to understand the basic physics of the ferroelectricity/piezoelectricity of the materials, and to seek novel promising compounds among the discovered solutions or alloys with other ferroelectric systems. Moreover, integration techniques of thin films are also a direction for the future works when considering the practical applications.
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Figure 27.
Dielectric constant ε′(T) for Ag1-xLixTaO3 with 0≤x≤0.12.
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Figure 28.
Dielectric loss for Ag1-xLixTaO3 with 0≤x≤0.12.
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Figure 29.
Relationship between frequency and Tmax in Ag1-xLixTaO3. Tmax was normalized by the Tmax of 1 MHz.
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Figure 30.
Hysteretic D-E loop for Ag1-xLixTaO3 with x=0.12 obtained at 0.1 Hz and 77 K, indicating the ferroelectric state at T< Tc (=258 K).
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Figure 31.
Proposed phase diagram for Ag1-xLixTaO3. The dashed-dotted line shows the fitting to Morf-Schneider’s relationship that is proposed for a quantum phase transition (Morf et al., 1977). PE and FE denote paraelectric and ferroelectric states, respectively. The gray zone may be a dipolar-glass state (Pirc & Blinc, 1999) or a state with nanosized ferroelectric domains (Fisch, 2003; Fu et al., 2009b).
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\n\t
Acknowledgments
\n\t\t\t
Part of this work was supported by the Collaborative Research Project of Materials and Structures Laboratory of Tokyo Institute of Technology, and Grant-in-Aid for Scientific Research, MEXT, Japan.
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\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/18055.pdf",chapterXML:"https://mts.intechopen.com/source/xml/18055.xml",downloadPdfUrl:"/chapter/pdf-download/18055",previewPdfUrl:"/chapter/pdf-preview/18055",totalDownloads:3529,totalViews:363,totalCrossrefCites:3,totalDimensionsCites:4,totalAltmetricsMentions:0,introChapter:null,impactScore:1,impactScorePercentile:60,impactScoreQuartile:3,hasAltmetrics:0,dateSubmitted:"October 20th 2010",dateReviewed:"April 27th 2011",datePrePublished:null,datePublished:"August 24th 2011",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/18055",risUrl:"/chapter/ris/18055",book:{id:"174",slug:"ferroelectrics-material-aspects"},signatures:"Desheng Fu and Mitsuru Itoh",authors:[{id:"27794",title:"Prof.",name:"Desheng",middleName:null,surname:"Fu",fullName:"Desheng Fu",slug:"desheng-fu",email:"fu.tokusho@shizuoka.ac.jp",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Shizuoka University",institutionURL:null,country:{name:"Japan"}}},{id:"38996",title:"Prof.",name:"Mitsuru",middleName:null,surname:"Itoh",fullName:"Mitsuru Itoh",slug:"mitsuru-itoh",email:"itoh.m.aa@m.titech.ac.jp",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Tokyo Institute of Technology",institutionURL:null,country:{name:"Japan"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. AgNbO3\n\t\t\t",level:"1"},{id:"sec_2_2",title:"2.1. Synthesis",level:"2"},{id:"sec_4",title:"2.2 Electric-field induced ferroelectric phase",level:"1"},{id:"sec_4_2",title:"2.3. Room-temperature structure",level:"2"},{id:"sec_5_2",title:"2.4. Dielectric behaviours and phase transitions",level:"2"},{id:"sec_7",title:"3. (Ag1-x\n\t\t\t\tLi\n\t\t\t\t\tx\n\t\t\t\t)NbO3 solid solution",level:"1"},{id:"sec_7_2",title:"3.1. Synthesis",level:"2"},{id:"sec_8_2",title:"3.2. Structure",level:"2"},{id:"sec_9_2",title:"3.3. Ferroelectric and piezoelectric properties",level:"2"},{id:"sec_10_2",title:"3.4. Dielectric behaviours and proposed phase diagram",level:"2"},{id:"sec_12",title:"4. (Ag1-x\n\t\t\t\tNa\n\t\t\t\t\tx\n\t\t\t\t)NbO3 solid solution",level:"1"},{id:"sec_12_2",title:"4.1. Synthesis",level:"2"},{id:"sec_13_2",title:"4.2. Polarization",level:"2"},{id:"sec_14_2",title:"4.3. Dielectric properties",level:"2"},{id:"sec_16",title:"5. (Ag1-x\n\t\t\t\tK\n\t\t\t\t\tx\n\t\t\t\t)NbO3 solid solution",level:"1"},{id:"sec_16_2",title:"5.1. Synthesis",level:"2"},{id:"sec_17_2",title:"5.2. Structural change with composition",level:"2"},{id:"sec_19",title:"5. 3. Ferroelectric and piezoelectric properties",level:"1"},{id:"sec_19_2",title:"5.4. Dielectric behaviours and proposed phase diagram",level:"2"},{id:"sec_21",title:"6. AgTaO3\n\t\t\t",level:"1"},{id:"sec_21_2",title:"6.1. Synthesis",level:"2"},{id:"sec_22_2",title:"6.2. Phase formation and dielectric behaviors",level:"2"},{id:"sec_24",title:"7. (Ag1-x\n\t\t\t\tLi\n\t\t\t\t\tx\n\t\t\t\t)TaO3 solid solution",level:"1"},{id:"sec_24_2",title:"7.1. Synthesis",level:"2"},{id:"sec_25_2",title:"7.2. Dielectric behaviours and confirmation of ferroelectric phase",level:"2"},{id:"sec_27",title:"8. Concluding remarks",level:"1"},{id:"sec_28",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tAbel\n\t\t\t\t\t\t\tW. R.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1971Effect of pressure on the static dielectric constant of KTaO3, Phys. Rev. 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'}],corrections:null},book:{id:"174",type:"book",title:"Ferroelectrics",subtitle:"Material Aspects",fullTitle:"Ferroelectrics - Material Aspects",slug:"ferroelectrics-material-aspects",publishedDate:"August 24th 2011",bookSignature:"Mickaël Lallart",coverURL:"https://cdn.intechopen.com/books/images_new/174.jpg",licenceType:"CC BY-NC-SA 3.0",editedByType:"Edited by",isbn:null,printIsbn:"978-953-307-332-3",pdfIsbn:"978-953-51-4451-9",reviewType:"peer-reviewed",numberOfWosCitations:140,isAvailableForWebshopOrdering:!0,editors:[{id:"10041",title:"Dr.",name:"Mickaël",middleName:null,surname:"Lallart",slug:"mickael-lallart",fullName:"Mickaël Lallart"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"940"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},chapters:[{id:"18036",type:"chapter",title:"BST and Other Ferroelectric Thin Films by CCVD and Their Properties and Applications",slug:"bst-and-other-ferroelectric-thin-films-by-ccvd-and-their-properties-and-applications",totalDownloads:2648,totalCrossrefCites:0,signatures:"Yongdong Jiang, Yongqiang Wang, Kwang Choi Deepika Rajamani and Andrew Hunt",reviewType:"peer-reviewed",authors:[{id:"25363",title:"Dr.",name:"Yongdong",middleName:null,surname:"Jiang",fullName:"Yongdong Jiang",slug:"yongdong-jiang"},{id:"40688",title:"Dr.",name:"Yongqiang John",middleName:null,surname:"Wang",fullName:"Yongqiang John Wang",slug:"yongqiang-john-wang"},{id:"40689",title:"Dr.",name:"Kwang",middleName:null,surname:"Choi",fullName:"Kwang Choi",slug:"kwang-choi"},{id:"40690",title:"Dr.",name:"Deepika",middleName:null,surname:"Rajamani",fullName:"Deepika Rajamani",slug:"deepika-rajamani"},{id:"40691",title:"Dr.",name:"Andrew T.",middleName:null,surname:"Hunt",fullName:"Andrew T. 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1. Introduction
Explainable artificial intelligence (xAI) is one of the research topics that has been intriguing in recent years. Today, even if we are at the beginning of understanding this type of models, the studies that show interesting results about this issue are getting more and more intensive. In the near future, it is predicted that there will be years when the interpretability of artificial intelligence and deep meta-learning models is frequently explored [1]. It is thought to be a solution to overcome constraints in classical deep learning methods.
In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. Currently, in classical deep learning methods, input data and target (class) information can be trained with high performance and tested with new data input [2]. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms [3]. Many layers in a deep network allow it to recognize things at different levels of abstraction. For example, in a structure designed to recognize dogs, the lower layers recognize simple things such as outlines or color; the upper layers recognize more complex things like fur or eyes, and the upper layers define them all as a dog. Presumably speaking, the same approach can be applied to other inputs that lead a machine to teach itself. For example, it can be easily applied to the sounds that make up the words in the speech, the letters and words that form the sentences in the text, or the steering movements required to drive.
However, there are important shortcomings that current deep learning models are currently inadequate [4]. For deep learning, huge data sets are needed to train on, and these data sets must be inclusive/unbiased, and of good quality [5]. In addition, traditional deep learning requires a lot of time to train models for satisfying their purpose with an admissible amount of accuracy and relevancy [6]. Although deep learning is autonomous, it is highly susceptible to errors. Assume that an algorithm is trained with data sets small enough to not be inclusive [4]. The models trained by this way cause to irrelevant responses (biased predictions coming from a biased training set) being displayed to users [7]. One of the most important problems in artificial learning models is transparency and interpretability [8]. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable [9]. In other words, when you receive an output data against the input data, the deep learning model cannot provide the information for which reason the output is generated. The user cannot fully grasp the internal functions of these models and cannot find answers to question why and how the answers the models produce [10]. This situation creates difficulties in the application areas of these models in many aspects. For example, you stopped a taxi and got on it. The driver is such a driver that when he takes you to your destination, he turns right, turns left, and tries to get you on a strange route than you expect, but when you ask why he did so, he cannot give you a satisfactory answer. Would you be nervous? If there is no problem for you, you can ride an autonomous vehicle without a driver. As another example, when you go to the doctor, the doctor you send your complaint asks for tests and when you have those tests and send it to the doctor, the doctor tells you what your illness is. Even though he says his treatment, he does not give explanatory information about the cause of your illness. In this case, questions remain about what caused the disease and you would not be satisfied with the doctor. This is an important open point in artificial neural networks and deep learning models.
The explainable artificial intelligence (xAI) approach can be considered as an area at the intersection of several areas. One of these areas is the end user explanation section that includes social sciences. This area provides artificial intelligence to gain cognitive abilities. Another area is the human machine interface, where it can demonstrate the ability to explain; because explainable artificial intelligence needs a very high-level interaction with the user. And finally, deep learning models are an important part of an explicable artificial intelligence approach (Figure 1).
Figure 1.
Explainable artificial intelligence (xAI) [8].
In this new approach, it is aimed to provide the user with the ability to explain the output data produced as well as being trained at high performance with the input data and target (class) information and tested with the new data input as in the classical machine learning models. This will create a new generation artificial intelligence approach that can establish a cause and effect relationship between input and output. It will also be the mechanism of monitoring the reliability of artificial intelligence from the user point of view. While a classic deep learning model can answer “what” or “who” questions, learning models in explainable artificial intelligence approaches can also answer “why,” “how,” “where,” and “when” questions [10] (Figure 2).
Figure 2.
How can explainable artificial intelligence (xAI) be reliable [11]?
Explainability and accuracy are two separate domains. In general, models that are advantageous in terms of accuracy and performance are not very successful in terms of explainability. Likewise, methods with high explainability are also disadvantageous in terms of accuracy. When methods such as classical deep learning models, artificial neural networks support vector machines are utilized, they do not give reasons why, and how their outputs created in terms of explainability. On the other hand, they are very successful in accuracy and performance. Rule-based structures, decision trees, regression algorithms, and graphical methods are good explainability but not advantageous in terms of performance and accuracy. At this point, explanatory artificial intelligence (xAI), which is targeted to be at the highest level of both explainability and accuracy and performance, reveals its importance at this point (Figure 3).
Figure 3.
Machine learning models with respect to accuracy-explainability domain [12].
2. Related works
There is a transformation of machine learning that has been going on since the 1950s, sometimes faster and sometimes slower. The most studied and remarkable area in the recent past is artificial learning, which aims to model the live decision system, behavior, and responses. Successful results in the field of artificial learning led to the rapid increase of AI applications. Further studies promise to be autonomous systems capable of self-perception, learning, decision-making, and action [13].
Especially after the 1990s, although deep learning concept and foundations go back to the past, the accompanying recurrent neural networks, convolutional neural networks, deep reinforcement learning, and adversarial generative networks have achieved remarkable successes. Although successful results are obtained, these systems are insufficient in terms of explaining the decisions and actions to human users and there are limits.
The U.S. Department of Defense (DoD) explains that it is facing the challenges posed by autonomous and symbiotic systems, which are becoming smarter with each passing day. Explaining artificial intelligence or especially explanatory machine learning is important in terms of being a preview that users will encounter machines with human-like artificial intelligence in the future [14, 15]. Explained artificial intelligence is one of the Defense Advanced Research Projects Agency (DARPA) programs aimed at the development of a new generation of artificial intelligence systems, where they understand the context and environment in which machines operate and build descriptive models that enable them to characterize the real world phenomenon over time. For this purpose, DARPA recently issued a call letter for the Explainable Artificial Intelligence (XAI)—Explanatory Artificial Intelligence project [15]. Within the scope of the project, it is aimed to develop a system of machine learning techniques that focus on machine learning and human-machine interaction, and produce explanatory models that will enable end users to understand, trust, and manage emerging artificial intelligence systems. According to the researchers from DARPA, the striking successes in machine learning have led to a huge explosion in new AI capabilities that enable the production of autonomous systems that perceive, learn, decide, and act on their own. Although these systems provide tremendous benefits, their effectiveness is limited due to the inability to explain machine decisions and actions to human users.
The Explanatory Artificial Intelligence project aims to develop the machine learning and computer-human interaction tools to ensure that the end user, who depends on decisions, recommendations, or actions produced by the artificial intelligence system, understands the reason behind the system’s decisions [1]. For example, an intelligence analyst who gets recommendations from big data analytics algorithms may need to understand why the algorithm advises to examine a particular activity further. Similarly, the operator, who tests a newly developed autonomous system, has to understand how he makes his own decisions to determine how the system will use it in future tasks.
The xAI tools will provide end users with explanations of individual decisions, which will enable them to understand the strengths and weaknesses of the system in general, give an idea of how the system will behave in the future, and perhaps teach how to correct the system\'s mistakes. The XAI project addresses three research and development challenges: how to build more models, how to design an explanation interface, and how to understand psychological requirements for effective explanations [2].
For the first problem, the xAI project aims to develop machine learning techniques to be able to manufacture explanatory models. To solve the second challenge, the program envisions integrating state-of-the-art human-machine interaction techniques with new principles, strategies, and techniques to produce effective explanations. To solve the third problem, the xAI project plans to summarize, disseminate, and apply existing psychological theory explanations. There are two technical areas in the program: the first is to develop an explanatory learning system with an explanatory model and an explanation interface; and the second technical area covers psychological theories of explanation [8].
In 2016, a self-driving car was launched on quiet roads in Monmouth County, New Jersey. This experimental tool developed by researchers at chip maker Nvidia did not look different from other autonomous cars; however, Google was different from what Tesla or General Motors introduced and showed the rising power of artificial intelligence. The car had not even followed a single instruction provided by an engineer or a programmer. Instead, it relied entirely on an algorithm that allowed him to learn to drive by watching a person driving [3]. It was an impressive success to have a car self-driving in this way. But it was also somewhat upsetting as it was not entirely clear how the car made its own decisions. The information from the vehicle’s sensors went directly to a huge artificial neural network that processes the data and then delivers the commands needed to operate the steering wheel, brakes, and other structures. The results seem to match the reactions you can expect from a human driver. But what if one day something unexpected happens; hits a tree or stops at the green light? According to the current situation, it may be difficult to find the cause. The system is so complex that even the engineers who designed it can find it difficult to pinpoint the cause of any action. Moreover, you cannot ask this; there is no obvious way to design such a system that can always explain why it does what it does. The mysterious mind of this vehicle points to a vague-looking issue of artificial intelligence. Artificial intelligence technology, which is located at the base of the car and known as deep learning, has proven to be very strong in problem-solving in recent years, and this technology has been widely applied in works such as image content estimation, voice recognition, and language translation. Now the same methods can be used to diagnose lethal diseases, make million-dollar business decisions, etc. to change all industries.
Currently, the mathematical models are used to help determine who will be on parole, who will be approved to borrow money, and who will be hired. If you can access these mathematical models, it is possible to understand their reasoning. But banks, the military, employers, and others are now turning their attention to more complex machine learning approaches. These approaches can make automated decision-making completely incomprehensible. The most common of these approaches represents deep learning, a fundamentally different way of programming computers. Whether it is an investment decision or a medical decision, or a military decision, you do not want to rely solely on a “black box” method [1]. There is already a debate that it is a fundamental legal right to question a system of artificial intelligence about how it arrived at its conclusions. Starting in the summer of 2018, the European Union may require companies to provide users with an explanation of the decisions made by automated systems. This may be impossible even for systems that look comparatively simple on the surface, such as applications and Websites that use deep learning to offer advertising or song suggestions. Computers performing these services have programmed themselves and have done so in ways we cannot understand. Even the engineers who build these applications cannot fully explain their behavior.
As technology advances, we can go beyond some thresholds where using artificial intelligence in recent times requires a leap of faith. The mankind, of course, are not always able to fully explain our thought processes; but we find a variety of methods to intuitively trust people and measure them. Will this be possible for machines that think and make decisions differently than a person does? We have never built machines that operate in ways that their manufacturers do not understand. How long can we hope to communicate and deal with intelligent machines that can be unpredictable or incomprehensible? These questions take a journey toward new technology research on artificial intelligence algorithms, from Google to Apple and many other places between them, including a conversation with one of the greatest thinkers of our time.
3. Explainable artificial intelligence (xAI)
You cannot see how the deep neural network works just by looking inside. The reasoning of a network is embedded in the behavior of thousands of nerves, which are stacked and tied to tens or even hundreds of layers, mixed together. Each of the nerves in the first layer receives an input, such as the voltage of a pixel in an image, and then performs a calculation before sending a new signal as an output. This output is sent to the next layer in a complex network, and this process continues until a general output is produced. There is also a process known as back propagation that modifies the calculations of individual nerves so that a network learns to produce a desired output. Because deep learning is inherently a dark black box by nature, artificial learning models designed with millions of artificial nerve cells with hundreds of layers like traditional deep learning models are not infallible [1]. Their reliability is questioned when simple pixel changes can be seriously misleaded by causing significant deviations in the weight values in all layers of the neural network, especially in an example such as a one-pixel attack [16]. So, it becomes inevitable to ask the question of how it can succeed or fail. With the success of this type of advanced applications, its complexity also increases and its understanding/clarity becomes difficult.
It is aimed to have the ability to explain the reasons of new artificial learning systems, identify their strengths and weaknesses, and understand how they will behave in the future. For an ideal artificial intelligence system, the best accuracy and best performance, as well as the best explainability and the best interpretability are required within the cause-effect relationship. The strategy developed to achieve this goal is to develop new or modified artificial learning techniques that will produce more explicable models. These models are aimed to be combined with state-of-the-art human-computer interactive interface techniques that can be translated into understandable and useful explanation dialogs for the end user (Figure 4).
Figure 4.
Explainable artificial intelligence (xAI) project proposed by DARPA [14, 15].
In this structure, unlike the classical deep learning approaches, two different elements draw attention as well as a new machine learning process. One of these is the explanatory model and the other is the explanation interface. The process of deep neural network-based machine learning is explained at the core of the artificial intelligence approach. Among the known deep learning models, autoencoder, convolutional, recurrent (LSTM), deep belief network, or deep reinforcement learning can be preferred. However, it is also possible to use a hybrid structure where several deep learning approaches are used together. Autoencoder-type model of deep neural networks are multilayered perceptron structure. In convolution neural network-type models, layers consist of convolutional layer, ReLU activation function, and max pool layer. A conventional component of the LSTM is composed of a memory cell including input, output, and forget gates. For training, the backpropagation through time algorithm can be preferred. Although the most common form of deep reinforcement learning models is deep Q network (DQN), many different variations of this model can be addressed. Many different algorithms are used as optimization algorithm. Gradient-based algorithms are the most common form of these algorithms (Figure 5).
Figure 5.
Deep learning models: (a) autoencoder [17], (b) convolutional neural network [18], and (c) recurrent (LSTM) neural network [19].
Explainable model is an adaptive rule-based reasoning system. It is a structure that reveals the cause-effect relations between input data and the results obtained from the machine learning process. This causal structure learns the rules with its own internal deep learning method. In this way, the explanatory artificial intelligence model allows it to explore the causes and develop new strategies against different situations [20].
The explanation interface is a part of the user interaction. It is similar to the question-answer interface in voice digital assistants. This interface consists of a decoder that evaluates the demands of the user and an encoder unit that enables the responses from the explanatory model, which constitutes the causal mechanism of the explainable artificial intelligence, to the user (Figure 6).
Figure 6.
Semantic knowledge matching for explainable artificial intelligence model [21].
In fact, the large networks of semantic technologies (entities) and relationships associated with Knowledge Graphs (KGs) provide a useful solution for the issue of understandability, several reasoning mechanisms, ranging from consistency checking to causal inference [21]. The ontologies realizing these reasoning procedures provide a formal representation of semantic entities and relationships relevant to a particular sphere of knowledge [21]. The input data, hidden layers, encoded features, and predicted output of deep learning models are passed into knowledge graphs (KGs) or concepts and relationships of ontologies (knowledge matching) [21]. Generally, the internal functioning of algorithms to be more transparent and comprehensible can be realized by knowledge matching of deep learning components, including input features, hidden unit and layers, and output predictions with KGs and ontology components [21]. Besides that, the conditions for advanced explanations, cross-disciplinary and interactive explanations are enabled by query and reasoning mechanisms of KGs and ontologies [21].
Although explanatory artificial intelligence forms are of very different structures, all modules such as this explanation interface, explanatory model, and deep learning work in coordination with each other. For example, while a deep learning process estimates classes, such as the explanatory artificial intelligence model (xAI tool) developed by IBM, the concept features data obtained from this process, and another deep learning process using the same input data set produces an explanatory output for the predicted class label output [22] (Figure 7).
Figure 7.
Explainable artificial intelligence (xAI) tool developed by IBM [22].
At this point, the explainable artificial intelligence (xAI) tool developed by IBM is referred as a self-explaining neural network (SENN) which can be trained end-to-end with back-propagation in case of that g depends on its arguments in a continuous way [18]. The input is transformed into a small set of interpretable basis features by a concept encoder [22]. The relevance scores are produced by an input-dependent parametrizer. A prediction to be generated is merged by an aggregation function. The full model to behave locally as a linear function on h(x) with parameters θ(x), producing interpretation of both concepts and relevances, is induced by the robustness loss on the parametrizer [22]. θ(x) modeling capacity is important so that the model richness realizing higher-capacity architectures is sustained although the concepts are chosen to be raw inputs (i.e., h is the identity).
4. Meta-learning
As research and technology on machine learning progresses, artificial intelligence agents consistently display impressive learning performances that meet and exceed the cognitive skills of people in different fields. However, most AI programs are based on computing technology and even reinforcement learning (RL) models that try to regularly improve their knowledge to match human performance. By contrast, people can quickly learn new skills of new skills, simply by having a new skill [23]. The learning of the human brain so efficiently has surprised neuroscientists for years.
In traditional deep learning approaches, the system develops a data-specific model that is transmitted to it by learning from the data. The learning system will perform a certain task only for a certain environment. In the case of another environment, when a very different data is transmitted to it, this deep learning model will be insufficient to perform the task [24]. This issue reveals hard constraints in utilizing machine learning or data mining methods, since the relationship between the learning problem and the effectiveness of different learning algorithms is not yet understood. Under ideal conditions, a system should be designed in which the quality of the data given to the system differs and it can easily adapt to changes in different environments [25]. The deep learning methods used in the current situation are not successful in these situations. At this point, meta-learning, which learns to learn, is an integrated and hierarchical learning model over several different environmental models [26, 27]. As a subfield of machine learning, meta-learning learning algorithms are applied on metadata about machine learning experiments. Instead of classical machine learning approaches that only learn a specific task with single massive dataset, meta-learning is a high-level machine learning approach that learns other tasks together. Therefore, this approach requires a hierarchical structure that learns to learn a new task with distributed hierarchically structured metadata. It is generally applied for hyper parameter adjustment; recent applications have started to focus on a small number of learning. For example, if the system has already learned a few different models or tasks, meta-learning can generalize them and learn how to learn more efficiently. In this way, it can learn new tasks efficiently and create a structure that can easily adapt to changes in multiple tasks in different environments.
People are good at figuring out the meaning of a word after seeing it used only in a few sentences. Similarly, we want our ML algorithms to be generalized to new tasks, without the need for a large data set each time, and to change behavior after a few samples. In typical learning (on a single dataset), each sample targets pair functions as a training point. However, in a small number of learning situations, each “new” sample area is actually another task in itself. In other words, understanding the way that you use unique words in a particular social environment becomes a new task for your language-understanding model, and when you enter a different social environment, it means that the system can adapt to a different language-understanding model than before since it requires to dominate the words that are specific to that social environment. To make sure an ML framework can behave similarly, we have to train it on multiple tasks on its own, so we make each data set a new example of training [28] (Figure 8).
Figure 8.
Meta-learning approach [29].
An alternative is to handle the task consecutively as a sequential input array and create a repetitive model that can create a representation of this array for a new task. Typically, in this case, we have a single training process with a memory or attention repetitive network [30]. This approach also gives good results, especially when the installations are properly designed for the task. The calculation performed by the optimizer during the meta-forward transition is very similar to the calculation of a repetitive network [31]. It repeatedly applies the same parameters over a series of inputs (consecutive weights and gradients of the model during learning). In practice, this means that we meet a common problem with repetitive networks. Since the models are not trained to get rid of training errors, they have trouble returning to a safe path when they make mistakes, and the models have difficulty generalizing longer sequences than those used in the order in which they were used. In order to overcome these problems, if the model learns an action policy related to the current educational situation, reinforcement learning approaches can be preferred [32] (Figure 9).
Figure 9.
(a) Meta-reinforcement learning (stack of sub-policies representation) [33] and (b) meta-reinforcement learning (inner-outer loop representation) [34].
Formal reinforcement learning algorithm learns a policy for only single task.
θ∗=argmaxθEπθτRτE1
In meta-reinforcement learning, there are two distinct processes. One of them is adaptation (inner-loop) behaving ordinary RL policy learning to produce sub-policy where ϕi=fθMi for each environment (task) Mi.
θ∗=argmaxθ∑i=1nEπϕiτRτE2
Another process is meta-training (outer-loop), which is described as meta-policy learning from all sub-policies in the adaptation process (inner-loop).
One of the main differentiers between the human brain and artificial intelligence structures such as deep neural networks, is the brain that utilizes different chemicals known as neurotransmitters to perform different cognitive functions. A new study by DeepMind believes that one of these neurotransmitters plays an important role in the brain\'s ability to quickly learn new topics. Dopamine acts as a reward system that strengthens connections between neurons in the brain.
The DeepMind team has used different meta-reinforcement learning techniques that simulate the role of dopamine in the learning process. Meta-learning trained a repetitive neural network (representing the prefrontal cortex) using standard deep reinforcement learning techniques (representing the role of dopamine) and then compared the activity dynamics of the repetitive network with actual data from previous findings in neuroscience experiments [27]. Recurrent networks are a good example of meta-learning because they can internalize past actions and observations and then use these experiences while training on various tasks.
The meta-learning model recreated the Harlow experiment by saying a virtual computer screen and randomly selected images, and the experiment showed that the “meta-RL agent” was learned in a similar way to the animals found in the Harlow Experiment, even when presented with the Harlow Experiment. All new images were never seen before. The meta-learning agent quickly adapted to different tasks with different rules and structures.
5. Explainable meta-reinforcement learning (xMRL)
In this section, we will discuss the development of deep reinforcement learning models with an explicable approach to artificial intelligence. Deep reinforcement learning models are machine learning models that learn what action to take according to status and reward information by maximizing reward [27]. Generally, it is widely preferred in robotic, autonomous driverless vehicles, unmanned aerial vehicles, and games. Explanatory artificial intelligence, on the other hand, provides the knowledge of why action should be taken against the situation and reward for deep reinforcement learning models. In this way, it will be possible to gain the causal decision-making ability of the model by revealing the relational links between the input and output of the developed agent (Figure 10).
Figure 10.
(a) Reinforcement learning and (b) inverse reinforcement learning [35].
In addition, it is possible to learn the reward derivation mechanism by using the inverse reinforcement learning model [36, 37]. In this case, unlike the previous approach, a meta-cognitive artificial intelligence model that can adapt to other environments instead of just one environment is developed [38, 39]. Taken together with the explainable artificial intelligence approach, it will be possible for the developed agent to develop his own strategy by establishing a cause-effect relationship. For example, the explainable meta-reinforcement learning agent to be developed means that in terms of meta-learning, it can learn to play Go, chess, checkers, and even learn and adapt when it is encountering a new game, and in terms of explainable artificial intelligence, it means that being aware of why it is doing any specific action against a move made by the opponent, it can explain this.
6. Discussion and concluding remarks
Next generation artificial intelligence structures are expected to have a hierarchical meta-learning ability that can adapt to many different environments, besides being a causal and explanatory power by establishing a cause-effect relationship. For this, serious effort is still needed to create flexible and interpretable models that can hold opinions from many different disciplines together and work in harmony.
We cannot ignore the advantages this will give us. For example, if we start with a medical application, after the patient data is examined, both the physician must understand and explain to the patient why he/she suggested that the explanatory decision support system suggested to the related patient that there was a “risk of heart attack.” At the same time, as a meta-learning agent of this system, it has the same ability against all other diseases and it will be possible to develop appropriate treatment strategies.
While coming to this stage, what data is evaluated first is another important criterion. It is also necessary to explain what data is needed and why, and what is needed for proper evaluation. In the future, next generation deep learning and artificial intelligence forms are expected to reach the level of intelligence (singularity), which has higher performance and ability than human level. Artificial intelligence and deep learning structures mentioned in this section are thought to shed light on reaching these levels. In particular, it can be said that meta-learning approaches are capable of supporting the formation of structures that learn and adapt to multiple tasks and are also called general artificial intelligence (AGI). In the same way, it can be stated that artificial intelligence structures will help the formation of self-awareness and artificial consciousness structures based on content and causality.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"explainable artificial intelligence (xAI), meta-learning, deep learning",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/72398.pdf",chapterXML:"https://mts.intechopen.com/source/xml/72398.xml",downloadPdfUrl:"/chapter/pdf-download/72398",previewPdfUrl:"/chapter/pdf-preview/72398",totalDownloads:1159,totalViews:0,totalCrossrefCites:10,dateSubmitted:"November 14th 2019",dateReviewed:"March 18th 2020",datePrePublished:"June 25th 2020",datePublished:"December 9th 2020",dateFinished:"June 4th 2020",readingETA:"0",abstract:"The explainable artificial intelligence (xAI) is one of the interesting issues that has emerged recently. Many researchers are trying to deal with the subject with different dimensions and interesting results that have come out. However, we are still at the beginning of the way to understand these types of models. The forthcoming years are expected to be years in which the openness of deep learning models is discussed. In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. This is an important open point in artificial neural networks and deep learning models. For these reasons, it is necessary to make serious efforts on the explainability and interpretability of black box models.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/72398",risUrl:"/chapter/ris/72398",signatures:"Evren Dağlarli",book:{id:"9963",type:"book",title:"Advances and Applications in Deep Learning",subtitle:null,fullTitle:"Advances and Applications in Deep Learning",slug:"advances-and-applications-in-deep-learning",publishedDate:"December 9th 2020",bookSignature:"Marco Antonio Aceves-Fernandez",coverURL:"https://cdn.intechopen.com/books/images_new/9963.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83962-879-5",printIsbn:"978-1-83962-878-8",pdfIsbn:"978-1-83962-880-1",isAvailableForWebshopOrdering:!0,editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"168350",title:"Dr.",name:"Evren",middleName:null,surname:"Daglarli",fullName:"Evren Daglarli",slug:"evren-daglarli",email:"evren.daglarli@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Istanbul Technical University",institutionURL:null,country:{name:"Turkey"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Related works",level:"1"},{id:"sec_3",title:"3. Explainable artificial intelligence (xAI)",level:"1"},{id:"sec_4",title:"4. Meta-learning",level:"1"},{id:"sec_5",title:"5. Explainable meta-reinforcement learning (xMRL)",level:"1"},{id:"sec_6",title:"6. Discussion and concluding remarks",level:"1"},{id:"sec_10",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Adadi A, Berrada M. Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access. 2018;6:52138-52160'},{id:"B2",body:'Došilović FK, Brčić M, Hlupić N. Explainable artificial intelligence: A survey. In: 2018 41st International convention on information and communication technology, electronics and microelectronics (MIPRO). IEEE; 2018. pp. 0210-0215'},{id:"B3",body:'Core MG, Lane HC, Van Lent M, Gomboc D, Solomon S, Rosenberg M. Building explainable artificial intelligence systems. 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Reinforcement Meta-Learning Optimizes Visuomotor Learning. 2020. bioRxiv'},{id:"B38",body:'Parisotto E, Ghosh S, Yalamanchi SB, Chinnaobireddy V, Wu Y, Salakhutdinov R. Concurrent Meta Reinforcement Learning. 2019. arXiv preprint arXiv:1903.02710'},{id:"B39",body:'Jabri A, Hsu K, Gupta A, Eysenbach B, Levine S, Finn C. Unsupervised curricula for visual meta-reinforcement Learning. In: Advances in Neural Information Processing Systems; 2019. pp. 10519-10530'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Evren Dağlarli",address:"evren.daglarli@itu.edu.tr",affiliation:'
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His research field is in Sustainable Energy Resources, Environment and Development of zero pollution air engines. He became Member (M) of The Institution of Engineers (India) in 1978, CE (I) in 1985 and a life Fellow Member, FIE (India) in 1985. Also member of IAENG in 2010 and Life member of Indian Society of Technical Education (ISTE) in 2014; Past-Chairman, IE(I), UP State Centre, Lucknow from 2016-18, and President, MNNIT Alumni Association, Lucknow Chapter, Lucknow for the period 2017-2019 & 2019-2021....more on...(www.brsinghlucknow.com).",institutionString:null,institution:null},{id:"52804",title:"Associate Prof.",name:"Akira",surname:"Nishimura",slug:"akira-nishimura",fullName:"Akira Nishimura",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"I received the doctor degree of engineering from Nagoya University, Japan (2000.03).\r\nI worked at the center for integrated research in science and engineering in Nagoya University as research associate from April, 2004 to February, 2002.\r\nI have been working at division of mechanical engineering in Mie University from March, 2003 to now.",institutionString:null,institution:{name:"Mie University",institutionURL:null,country:{name:"Japan"}}},{id:"54866",title:"Prof.",name:"M. Dolores",surname:"Garza-Gil",slug:"m.-dolores-garza-gil",fullName:"M. Dolores Garza-Gil",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Vigo",institutionURL:null,country:{name:"Spain"}}},{id:"59592",title:"Prof.",name:"Manuel M.",surname:"Varela-Lafuente",slug:"manuel-m.-varela-lafuente",fullName:"Manuel M. Varela-Lafuente",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Vigo",institutionURL:null,country:{name:"Spain"}}},{id:"86510",title:"Prof.",name:"Vytautas",surname:"Pilipavicius",slug:"vytautas-pilipavicius",fullName:"Vytautas Pilipavicius",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/86510/images/648_n.jpg",biography:"Professor in weed science, organic agriculture and agroecology at Aleksandras Stulginskis University, Lithuania. \nDegree diploma (1996) (summa cum laude) in agronomy, Lithuanian University of Agriculture, Faculty of Agronomy. Dr. in biomedical sciences, agronomy (2000), Lithuanian University of Agriculture, Faculty of Agronomy, Department of Soil Management; \\Dynamics of spring barley (Hordeum vulgare L.) yield accumulation and of weed seed rain\\. Habilitation in biomedical sciences, agronomy (2007): Lithuanian University of Agriculture, \\Weed spreading regularity and adaptivity to abiotical factors\\.\n2008-2011 president of Lithuanian University of Agriculture Union of Professors \\Scientia\\, member of 14 international and national research organisations. Field of research interest - conventional and organic agriculture, weed science, agroecology. Associate Editor of journal Agronomy Research, member of editorial boards of journal Agricultural Sciences. \nResearch fellowship from Ministry of Science and Education 2003, Acknowledgment awards from Lithuanian U. Agr., 2004, 2008, diplom of journal \\Zemes ukis\\ [Agriculture] for the best articles during 2006, Education mobility foundation award for active and meaningful activity in international Erasmus exchange programme 2007, diploma of Lithuanian ministry of agriculture, the most communicative researcher 2008, State of Lithuania The Medal of January 13.",institutionString:null,institution:{name:"Aleksandras Stulginskis University",institutionURL:null,country:{name:"Lithuania"}}},{id:"118426",title:"Prof.",name:"Onkar",surname:"Singh",slug:"onkar-singh",fullName:"Onkar Singh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"149305",title:"Dr.",name:"Georgios",surname:"Photiadis",slug:"georgios-photiadis",fullName:"Georgios Photiadis",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Dr. Georgios M. Photiadis is currently Scientist in Raman Spectroscopy, Molten Salt Chemistry and Technology, and in Sustainable Chemistry, in Potters Bar, Hertsmere, Hertfordshire, England, United Kingdom. He received a BSc Degree (Ptychion) in Chemistry (Very Good, Grade: 7.50/10.00, top 5%) with Diploma Work (Grade: 10/10) in semiconductor Photoelectrochemical (PEC) Solar Cells, in 1991, from the Department of Chemistry, Aristotelion University of Thessaloniki, Thessaloniki, Greece. He received a PhD (Hons) in Physical Chemistry / Inorganic Chemistry (in Raman Spectroscopy of Molten Salts), in 1999, from the Department of Chemical Engineering, University of Patras, with work within FORTH/ICE-HT, Patras, Greece, in the Lab of Structure and Dynamics of Amorphous Materials and Fluids. His Ph.D. involved considerable work in Developing Raman Spectroscopy Instruments for measurements from Cryogenic liquid Nitrogen temperatures -196oC up to 1000oC, in Molten Salt Chemistry and Technology, and in Synthetic Inorganic Chemistry. He has considerable research experience and expertise in Raman Spectroscopy / Microscopy, Ultrafast Nonlinear Optical Spectroscopy, X-Ray Diffraction, Optical Microscopy, Optics, Nonlinear Optics, Development and Applications of Scientific Instruments for Raman Spectroscopy and Ultrafast Nonlinear Optical Spectroscopy, Development and Applications of High Vacuum and High Temperature Techniques for Synthesis and Purification of Chemicals, Molten Salt Chemistry and Technology, Inorganic Materials Synthesis, Air-Sensitive Chemistry, High Temperature Chemistry, Green Chemistry, Physical Chemistry, Inorganic Chemistry, Materials Chemistry, Low Carbon and Low Energy Chemistry and Technology, Cement Chemistry, Analysis and Interpretation of Data from Raman Spectroscopy / Microscopy, femtosecond Optically Heterodyne Detected – Optical Kerr Effect (OHD-OKE), Polarization Gate (PG) and Second Harmonic Generation (SHG) Frequency Resolved Optical Gating (FROG), and in Data Analysis and Modelling, He is also experienced in teaching Chemistry and Physics to 12-18 year old students and at University level to undergraduate students. His research and / or teaching posts include University College London, London, United Kingdom, specialising in Raman Spectroscopy / Microscopy, XRD, Inorganic Materials Synthesis, Cement Chemistry and in Green Chemistry applied to Low Carbon and Low Energy Molten Salt Synthesis of Cements (project funded by the UK EPSRC), Humboldt University Berlin, Berlin, Germany (project in the Lab of Ultrafast Spectroscopy funded initially by the German DFG, then a two years Marie Curie Individual Fellowship, Human Potential Programme European Union), University of Patras, Patras, Greece and University of Crete, Heraklion, Crete, Greece. He has a very strong interest in the Research and Development of applications of Raman Spectroscopy / Microscopy and Imaging and of Ultrafast Nonlinear Optical Spectroscopy in Chemistry, Physics, Materials Science, Biology, Pharmacy and in Medicine. He is an advocate of Sustainable Chemistry, Green Chemistry, Solar Energy with expertise and very strong interest in the Research and Development of Low Carbon and Low Energy Technologies using Molten Salts for Industrial Processes, and in the use of Novel Molten Salt Formulations for Solar Thermal Energy Storage.",institutionString:null,institution:{name:"University College London",institutionURL:null,country:{name:"United Kingdom"}}},{id:"171399",title:"Ph.D.",name:"Akira",surname:"Tomizuka",slug:"akira-tomizuka",fullName:"Akira Tomizuka",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Nagasaki University",institutionURL:null,country:{name:"Japan"}}},{id:"172867",title:"Ms.",name:"Julia",surname:"Torralba-Cano",slug:"julia-torralba-cano",fullName:"Julia Torralba-Cano",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"279025",title:"Dr.",name:"Sergio H.",surname:"Franchito",slug:"sergio-h.-franchito",fullName:"Sergio H. 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IntechOpen books and journals are available online by accessing all published content on a chapter/article level.
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All IntechOpen published chapters and articles are available OPEN ACCESS and can be read without the requirement for registration of any kind, immediately upon publication, without any barrier.
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The HTML version, as well as the PDF version of publications dated before 2012 that are accessible through a reader, are available to readers with no restriction.
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The full content of chapters and articles can be read, copied and printed from the link location of the chapter/article and these actions are not limited or restricted in any way.
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Registration is requested only to download the PDF of the chapter/article. There are no subscription fees and there is no charge to user groups.
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IntechOpen chapters and articles are distributed under CC BY 3.0 licences allowing users to “copy, use, distribute, transmit and display the work publicly and to make and distribute derivative works, in any digital medium for any responsible purpose, subject to proper attribution of authorship...” and there is no non-commercial restriction.
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Authors may post published works to any repository or website with no delay, and Authors and Editors of IntechOpen books have direct access to the PDF of the full book.
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All published content can be crawled for indexing. Full text and metadata may be accessed with instructions publicly posted.
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All IntechOpen books and Journal articles are indexed in CLOCKSS and preservation of access to published content is clearly indicated.
All IntechOpen published chapters and articles are available OPEN ACCESS and can be read without the requirement for registration of any kind, immediately upon publication, without any barrier.
\n\n
The HTML version, as well as the PDF version of publications dated before 2012 that are accessible through a reader, are available to readers with no restriction.
\n\n
The full content of chapters and articles can be read, copied and printed from the link location of the chapter/article and these actions are not limited or restricted in any way.
\n\n
Registration is requested only to download the PDF of the chapter/article. There are no subscription fees and there is no charge to user groups.
\n\n
IntechOpen chapters and articles are distributed under CC BY 3.0 licences allowing users to “copy, use, distribute, transmit and display the work publicly and to make and distribute derivative works, in any digital medium for any responsible purpose, subject to proper attribution of authorship...” and there is no non-commercial restriction.
\n\n
Authors may post published works to any repository or website with no delay, and Authors and Editors of IntechOpen books have direct access to the PDF of the full book.
\n\n
All published content can be crawled for indexing. Full text and metadata may be accessed with instructions publicly posted.
\n\n
All IntechOpen books and Journal articles are indexed in CLOCKSS and preservation of access to published content is clearly indicated.
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Policy last updated: 2022-04-14
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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. 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After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
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\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
",coverUrl:"https://cdn.intechopen.com/series/covers/24.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:1,editor:{id:"262440",title:"Prof.",name:"Usha",middleName:null,surname:"Iyer-Raniga",slug:"usha-iyer-raniga",fullName:"Usha Iyer-Raniga",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRYSXQA4/Profile_Picture_2022-02-28T13:55:36.jpeg",biography:"Usha Iyer-Raniga is a professor in the School of Property and Construction Management at RMIT University. Usha co-leads the One Planet Network’s Sustainable Buildings and Construction Programme (SBC), a United Nations 10 Year Framework of Programmes on Sustainable Consumption and Production (UN 10FYP SCP) aligned with Sustainable Development Goal 12. The work also directly impacts SDG 11 on Sustainable Cities and Communities. She completed her undergraduate degree as an architect before obtaining her Masters degree from Canada and her Doctorate in Australia. Usha has been a keynote speaker as well as an invited speaker at national and international conferences, seminars and workshops. Her teaching experience includes teaching in Asian countries. She has advised Austrade, APEC, national, state and local governments. She serves as a reviewer and a member of the scientific committee for national and international refereed journals and refereed conferences. She is on the editorial board for refereed journals and has worked on Special Issues. Usha has served and continues to serve on the Boards of several not-for-profit organisations and she has also served as panel judge for a number of awards including the Premiers Sustainability Award in Victoria and the International Green Gown Awards. Usha has published over 100 publications, including research and consulting reports. 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His present research includes organic synthesis, drug discovery and development, biochemistry, nanoscience, and nanotechnology.",institutionString:"Visiting Scientist at Lipid Nanostructures Laboratory, Centre for Smart Materials, School of Natural Sciences, University of Central Lancashire",institution:null},{id:"428125",title:"Dr.",name:"Vinayak",middleName:null,surname:"Adimule",slug:"vinayak-adimule",fullName:"Vinayak Adimule",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/428125/images/system/428125.jpg",biography:"Dr. Vinayak Adimule, MSc, Ph.D., is a professor and dean of R&D, Angadi Institute of Technology and Management, India. He has 15 years of research experience as a senior research scientist and associate research scientist in R&D organizations. He has published more than fifty research articles as well as several book chapters. He has two Indian patents and two international patents to his credit. Dr. Adimule has attended, chaired, and presented papers at national and international conferences. He is a guest editor for Topics in Catalysis and other journals. He is also an editorial board member, life member, and associate member for many international societies and research institutions. His research interests include nanoelectronics, material chemistry, artificial intelligence, sensors and actuators, bio-nanomaterials, and medicinal chemistry.",institutionString:"Angadi Institute of Technology and Management",institution:null},{id:"284317",title:"Prof.",name:"Kantharaju",middleName:null,surname:"Kamanna",slug:"kantharaju-kamanna",fullName:"Kantharaju Kamanna",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284317/images/21050_n.jpg",biography:"Prof. K. Kantharaju has received Bachelor of science (PCM), master of science (Organic Chemistry) and Doctor of Philosophy in Chemistry from Bangalore University. He worked as a Executive Research & Development @ Cadila Pharmaceuticals Ltd, Ahmedabad. He received DBT-postdoc fellow @ Molecular Biophysics Unit, Indian Institute of Science, Bangalore under the supervision of Prof. P. Balaram, later he moved to NIH-postdoc researcher at Drexel University College of Medicine, Philadelphia, USA, after his return from postdoc joined NITK-Surthakal as a Adhoc faculty at department of chemistry. Since from August 2013 working as a Associate Professor, and in 2016 promoted to Profeesor in the School of Basic Sciences: Department of Chemistry and having 20 years of teaching and research experiences.",institutionString:null,institution:{name:"Rani Channamma University, Belagavi",country:{name:"India"}}},{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/158492/images/system/158492.jpeg",biography:"Prof. Dr. Yusuf Tutar conducts his research at the Hamidiye Faculty of Pharmacy, Department of Basic Pharmaceutical Sciences, Division of Biochemistry, University of Health Sciences, Turkey. He is also a faculty member in the Molecular Oncology Program. He obtained his MSc and Ph.D. at Oregon State University and Texas Tech University, respectively. He pursued his postdoctoral studies at Rutgers University Medical School and the National Institutes of Health (NIH/NIDDK), USA. His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. 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He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. 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He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. 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He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a Principal Investigator and Scientist at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via machine-learning-based analyses of exosomal signatures. Dr. Paul has published in more than fifty peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award, a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. Assistant Coordinator of the Higher Course in Aesthetic and Cosmetic Technology at Universidade Paulista Campus Chácara Santo Antônio. Experience in the Pharmacy area, with emphasis on Pharmacotechnics, Pharmaceutical Technology, Research and Development of Cosmetics, acting mainly on topics such as cosmetology, antioxidant activity, aesthetics, photoprotection, cyclodextrin and thermal analysis.",institutionString:null,institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"334285",title:"Ph.D. Student",name:"Sameer",middleName:"Kumar",surname:"Jagirdar",slug:"sameer-jagirdar",fullName:"Sameer Jagirdar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334285/images/14691_n.jpg",biography:"I\\'m a graduate student at the center for biosystems science and engineering at the Indian Institute of Science, Bangalore, India. I am interested in studying host-pathogen interactions at the biomaterial interface.",institutionString:null,institution:{name:"Indian Institute of Science Bangalore",country:{name:"India"}}},{id:"329248",title:"Dr.",name:"Md. Faheem",middleName:null,surname:"Haider",slug:"md.-faheem-haider",fullName:"Md. Faheem Haider",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329248/images/system/329248.jpg",biography:"Dr. Md. Faheem Haider completed his BPharm in 2012 at Integral University, Lucknow, India. In 2014, he completed his MPharm with specialization in Pharmaceutics at Babasaheb Bhimrao Ambedkar University, Lucknow, India. He received his Ph.D. degree from Jamia Hamdard University, New Delhi, India, in 2018. He was selected for the GPAT six times and his best All India Rank was 34. Currently, he is an assistant professor at Integral University. Previously he was an assistant professor at IIMT University, Meerut, India. He has experience teaching DPharm, Pharm.D, BPharm, and MPharm students. He has more than five publications in reputed journals to his credit. Dr. Faheem’s research area is the development and characterization of nanoformulation for the delivery of drugs to various organs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"329795",title:"Dr.",name:"Mohd Aftab",middleName:"Aftab",surname:"Siddiqui",slug:"mohd-aftab-siddiqui",fullName:"Mohd Aftab Siddiqui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329795/images/system/329795.png",biography:"Dr. Mohd Aftab Siddiqui is an assistant professor in the Faculty of Pharmacy, Integral University, Lucknow, India, where he obtained a Ph.D. in Pharmacology in 2020. He also obtained a BPharm and MPharm from the same university in 2013 and 2015, respectively. His area of research is the pharmacological screening of herbal drugs/natural products in liver cancer and cardiac diseases. He is a member of many professional bodies and has guided many MPharm and PharmD research projects. Dr. Siddiqui has many national and international publications and one German patent to his credit.",institutionString:"Integral University",institution:null}]}},subseries:{item:{id:"4",type:"subseries",title:"Fungal Infectious Diseases",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment",scope:"Fungi are ubiquitous and there are almost no non-pathogenic fungi. Fungal infectious illness prevalence and prognosis are determined by the exposure between fungi and host, host immunological state, fungal virulence, and early and accurate diagnosis and treatment. \r\nPatients with both congenital and acquired immunodeficiency are more likely to be infected with opportunistic mycosis. Fungal infectious disease outbreaks are common during the post- disaster rebuilding era, which is characterised by high population density, migration, and poor health and medical conditions.\r\nSystemic or local fungal infection is mainly associated with the fungi directly inhaled or inoculated in the environment during the disaster. The most common fungal infection pathways are human to human (anthropophilic), animal to human (zoophilic), and environment to human (soilophile). Diseases are common as a result of widespread exposure to pathogenic fungus dispersed into the environment. \r\nFungi that are both common and emerging are intertwined. In Southeast Asia, for example, Talaromyces marneffei is an important pathogenic thermally dimorphic fungus that causes systemic mycosis. Widespread fungal infections with complicated and variable clinical manifestations, such as Candida auris infection resistant to several antifungal medicines, Covid-19 associated with Trichoderma, and terbinafine resistant dermatophytosis in India, are among the most serious disorders. \r\nInappropriate local or systemic use of glucocorticoids, as well as their immunosuppressive effects, may lead to changes in fungal infection spectrum and clinical characteristics. Hematogenous candidiasis is a worrisome issue that affects people all over the world, particularly ICU patients. CARD9 deficiency and fungal infection have been major issues in recent years. Invasive aspergillosis is associated with a significant death rate. Special attention should be given to endemic fungal infections, identification of important clinical fungal infections advanced in yeasts, filamentous fungal infections, skin mycobiome and fungal genomes, and immunity to fungal infections.\r\nIn addition, endemic fungal diseases or uncommon fungal infections caused by Mucor irregularis, dermatophytosis, Malassezia, cryptococcosis, chromoblastomycosis, coccidiosis, blastomycosis, histoplasmosis, sporotrichosis, and other fungi, should be monitored. \r\nThis topic includes the research progress on the etiology and pathogenesis of fungal infections, new methods of isolation and identification, rapid detection, drug sensitivity testing, new antifungal drugs, schemes and case series reports. It will provide significant opportunities and support for scientists, clinical doctors, mycologists, antifungal drug researchers, public health practitioners, and epidemiologists from all over the world to share new research, ideas and solutions to promote the development and progress of medical mycology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",hasOnlineFirst:!0,hasPublishedBooks:!1,annualVolume:11400,editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",slug:"yuping-ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",biography:"Dr. Yuping Ran, Professor, Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China. Completed the Course Medical Mycology, the Centraalbureau voor Schimmelcultures (CBS), Fungal Biodiversity Centre, Netherlands (2006). International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. Vice-chief of the editorial board of Chinses Journal of Mycology, China. 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It has become a massive part of our daily lives, making predictions based on experience, making this a fascinating area that solves problems that otherwise would not be possible or easy to solve. This topic aims to encompass algorithms that learn from experience (supervised and unsupervised), improve their performance over time and enable machines to make data-driven decisions. 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