p53 family-related anticancer properties of resveratrol
\r\n\tThus, we call for research and review papers on the chemistry and physics of dyes, pigments, and their intermediates, including chemical constituents, spectroscopic aspects, surface, solution, crystal formation, photochemical, and ecological or biological properties. The book will be of interest to a wide variety of researchers worldwide whose work involves various fields of activity such as dyes and pigment synthesis, imaging, sensor, energy, medicine, polymers, food product, toxicological properties, etc.
",isbn:"978-1-83768-114-3",printIsbn:"978-1-83768-113-6",pdfIsbn:"978-1-83768-115-0",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"fcd069956c2e931195925b19a74ce9a3",bookSignature:"Dr. Brajesh Kumar",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/12081.jpg",keywords:"Heterocycles Pigments, Azo, Nitro, Indigo, Alizarin, Chromophores, Chromophores, Photochemical, Sulphonation, Diazotisation, UV-Vis Spectroscopy, Metal-Ligand",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 19th 2022",dateEndSecondStepPublish:"June 16th 2022",dateEndThirdStepPublish:"August 15th 2022",dateEndFourthStepPublish:"November 3rd 2022",dateEndFifthStepPublish:"January 2nd 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"20 days",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Brajesh Kumar has worked as a faculty member in various universities in India, Ecuador, and South Korea. He has published numerous SCI/SCIE/Scopus research articles and is an active reviewer of more than 50 Journals. Dr. Kumar is a member of the American Chemical Society, the Indian Society of Chemists and Biologists, and the Indian Science Congress Association and holder of two registered patents. He is included in the top 2% of the scientist list prepared by experts at Stanford University,",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"176093",title:"Dr.",name:"Brajesh",middleName:null,surname:"Kumar",slug:"brajesh-kumar",fullName:"Brajesh Kumar",profilePictureURL:"https://mts.intechopen.com/storage/users/176093/images/system/176093.JPG",biography:"Dr. Brajesh Kumar is currently working as an Assistant Professor and Head in the Post Graduate Department of Chemistry, TATA College, Chaibasa, India. He received a Ph.D. in Chemistry from the University of Delhi, India. His research interest is in the development of sustainable and eco-friendly techniques for (a) nanoparticles synthesis and their applications for environmental remediation, (b) active films of organic solar cells, (c) nanomedicine, (d) sensors, (e) natural product extraction, purification, and analysis,(f) natural polymers, (g) peptide chemistry, (h) microwave and ultrasound-assisted organic synthesis and (i) organic synthesis. Dr. Brajesh Kumar has been credited for different national and international fellowships and he has also worked as a faculty member in various universities of India, Ecuador, and South Korea. He has also published numerous SCI/ SCIE/ Scopus research articles (h index = 28, Citations 2690) and is also an active reviewer of more than 50 Journals. He is also included in the top 2% of the scientist list prepared by experts at Stanford University, USA.",institutionString:"TATA College, Kolhan University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"2",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"8",title:"Chemistry",slug:"chemistry"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"444318",firstName:"Nika",lastName:"Karamatic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/444318/images/20011_n.jpg",email:"nika@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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Polyphenols are believed to be multi-targets drugs and in the present chapter we will give an overview of recent investigations concerning apoptosis induction by three major compounds, resveratrol, curcumin and epigallocatechin-3-gallate (EGCG) mainly through the regulation of the p53 tumor suppressor pathway. The potential regulation by polyphenols of p53 expression at the transcriptional and post-translational levels has been extensively described. Interestingly, polyphenolic compounds are also able to trigger apoptosis of numerous cancer cells, independently of the p53 status (wild-type, mutated or deficient). Moreover alternative mechanisms supported by recent studies highlight the role of p73, a p53 related tumor suppressor, as another key target for polyphenols. Then the molecular mechanisms involved in tumor suppressors (mainly p53 and p73) expression by polyphenols will be discussed with a specific focus on the role of oxidative stress which is believed to be a key element in polyphenols-induced cancer cells death.
Polyphenols are natural compounds characterized by a structure containing at least one benzene ring substituted by at least one hydroxyl group. Beside this chemical hallmark, phenolic products currently constitute a large and still expanding complex and heterogeneous family of molecules (more than 8000 phenolic structures currently known) with a great diversity of structure and size ranging from the low molecular weight simple phenols up to the high molecular weight tannins [1-3]. Polyphenols are also one of the largest and most widespread classes of constituents present in plant kingdom and more particularly in plant-derived foods and beverages giving them their color and taste properties. Polyphenols can be structurally divided into two main families: flavonoids and non-flavonoids. Flavonoids are especially abundant in fruits, vegetables, seeds, spices, herbs, tea, cocoa, and wine. The six major subclasses of flavonoids are anthocyanidins (e.g., cyanidin, delphinidin; primary sources: red berries, red cabbages, cherries, grapes, and onions), flavan-3-ols (e.g., catechin, epicatechin, EGCG; primary sources: tea, grapes, cocoa, apples, and red wine), flavanones (e.g., hesperitin, naringenin; primary sources: oranges, lemons, and grapefruits), flavones (e.g., apigenin, luteolin; primary sources: celery, parsley, and thyme), flavonols (e.g., kaempferol, myricetin, quercetin; primary sources: apples, beans, broccoli, and onions), and isoflavonoids (e.g., daidzein, genistein; primary sources: legumes and soy products). Phenolic acids represent a large subclass of non-flavonoid polyphenolic compounds which can be further divided into two main types: benzoic acids (e.g., gallic acid, ellagic acid, vanillic acid; primary sources: tea, red wine, berries, nuts, and herbs) and cinnamic acids (e.g., caffeic acid, chlorogenic acid; primary sources: coffee, berries, plum, and apple). Other important classes of non flavonoids with healthy properties are stilbenes, such as resveratrol (primary sources: red wine, berries and nuts) and curcuminoids such as curcumin the main component of dried turmeric and curry powder [4]. Polyphenols are considered as secondary plant metabolites and have been associated with several functions in plants such as resistance against microbial pathogens and insects, protection against DNA-damaging UV light, reproduction, nutrition and growth [3]. In parallel to their protective properties in plants, polyphenols have long been regarded as a pool of bioactive natural products with potential benefits for human health. Plant extracts, herbs and spice containing these compounds have been used for thousands of years in traditional medicines. Nowadays, plant polyphenols enjoy an ever-increasing recognition not only by scientific community but also, and most remarkably, by the general public because of their presence and abundance in fruits, seeds, vegetables and derived foodstuffs and beverages, whose regular consumption has been claimed to be beneficial for human health [3, 5]. Indeed, epidemiological and experimental studies have shown the potential of polyphenols or polyphenolic nutritional sources in reducing the risk of chronic diseases such as cardiovascular diseases [6-10] and cancers [10-14], as well as the risk of degenerative diseases [10, 15, 16]. Altogether these observations led to the current nutritional recommendations to eat five servings of fruits and vegetables per days in order to keep healthy.
A wealth of data, including epidemiological and animal studies, has described the chemopreventive and anticancer properties of polyphenolic compounds, such as resveratrol, curcumin or tea catechins, or polyphenol-rich nutritional sources [13, 14, 17-19]. Nonetheless, recent investigations have highlighted additional mechanisms responsible for direct anti-proliferative and chemotherapeutic properties of polyphenols. Indeed, these compounds can interfere with the initiation, as well as the progression of cancer through the modulation of different cellular events, such as cell cycle arrest by decreasing cyclins or apoptosis induction through cytochrome c release, activation of caspases and down- or up-regulation of Bcl-2 family members, and inhibition of survival/proliferation signals (AKT, MAPK, NF-κB, etc.). Furthermore, they play an important role in inflammation (COX-2, TNF secretion, etc.), as well as in suppression of key proteins involved in angiogenesis and metastasis [13]. Importantly, it has been established that tumor suppressors like p53 and its analogs are key molecular targets of polyphenols responsible for their pro-apoptotic effect in human and animal cancer models. Here we provide an overview of the molecular mechanisms involved in p53 family proteins modulation by three major and well characterized polyphenolic compounds, resveratrol, curcumin and EGCG.
Since the discovery of p53 in 1979 [20-22] numerous studies have been conducted related to its functions in response to stress and its regulatory mechanisms. p53 is a sequence-specific nuclear transcription factor that binds to defined consensus sites within DNA as a tetramer and represses transcription of a set of genes involved in cell growth stimulation, while activating a different set of genes involved in cell cycle control, like p21. It causes growth arrest providing a window for DNA repair or elimination of cells with severely damaged DNA strands. In some conditions, p53 activation triggers the transcription of pro-apoptotic genes such as Bax or PUMA, as well as the repression of anti-apoptotic genes like survivin [23]. Moreover, p53 can induce transcription-independent apoptosis. This mechanism involves early p53 translocation to mitochondria where it binds to Bcl-2 family proteins, such as Bax, Bak and Bcl-XL, activating cytochrome c release and caspases cascade [24]. Undoubtedly p53 exerts major anti-neoplastic effects and is considered actually as the “guardian of the genome” [25]. Tumor suppressive capabilities of p53 are related to a coordinated regulatory circuit that monitors and responds to a variety of stress signals, including DNA damage, abnormal oncogenic events, telomere erosion and hypoxia [26]. Importantly, in unstressed cells, p53 is latent and is maintained at low level by targeted ubiquitin-mediated degradation related to its interaction with ubiquitine ligases, mainly MDM2 [27]. Regarding the “guardian” functions of p53, mutations of p53 gene or disruptions of p53 coordination such as post-translational inactivation, can disturb the normal physiological balance, and lead to cancer if genome disarrangement reachs a critical value [28]. Indeed, low level of functionnal p53 is a common characteristic of cancer from several localizations including lung, colon, rectum, breast, brain, bladder, stomac, prostate, ovary, liver or lymphoid organs [29]. Somatic p53 missense inactivating mutations are found in approximately 50% of human cancers [30] and this inactivating mutations render the mutant p53 protein unable to carry out its normal function, that is, transcriptional transactivation of downstream target genes that regulate cell cycle and apoptosis [31-33]. On the other hand, p53 pathway can be also inactivated in wild-type (WT) p53-carrying tumors via indirect mechanisms such as MDM2 amplification leading to p53 destabilization [34, 35].
Recently, cDNAs with strong homologies to p53 have been identified and their products were termed p63 and p73 [36-38]. Both proteins are structurally similar and functionally related to p53, and consequently the entire p53 family may be regarded as a unique signalling network controlling cell proliferation, differentiation and death. Interestingly, in contrary to p53, the role of the other two p53-related proteins in tumor suppression is less obvious, since they are rarely deleted or mutated in cancer, and the respective knockout mice die tumor-free from developmental defects [39-41]. However, increasing number of evidences suggest that both p63 and p73 have a role in tumor suppression. Indeed, different studies indicated that TAp73 and TAp63, the transcriptionally active isoforms, can induce cell cycle arrest, senescence, DNA repair, and apoptosis in response to chemotherapeutic drugs, independently of p53 [42-45]. In addition, even if not mutated, p63 and p73 can be aberrantly expressed in cancer. More particularly, the dominant negative and transcriptionally inactive isoforms ∆Np63 and ∆Np73 are frequently overexpressed in a wide range of tumors, in which they are associated with poor prognosis [46]. Actually, the imbalance in the TAp73/∆Np73 may be more critical for tumorigenesis and response to chemotherapy than mutations [47]. In summary, despite their differences, the three members of the p53 family may be considered as therapeutic targets for cancer management.
Many
One of this alternative pathway might involve Egr-1, an immediate early-response gene induced by stress, injury, mitogens, and differentiation [70]. Egr-1 regulates the expression of genes involved in the control of growth and apoptosis by transactivating many proteins including p21. One study has shown that transcription of the p21 gene is activated by Egr-1 independently of p53 but under the control of MAPKs in response to curcumin treatment in U-87MG human glioblastoma cells [71]. In addition, the apoptotic effect of resveratrol in colorectal cancer cells as well as EGCG-mediated cytotoxicity in pulmonary cancer cells are also associated with Egr-1 upregulation [72, 73].
Alternatively to p53, its functionally related proteins p63 and p73 might represent targets for polyphenols. Nevertheless only few data are available concerning a potential regulatory effect of polyphenolic compounds on p63 and p73 (Table1-4). Different flavones (luteolin, apigenin, chrysin) and flavonols (quercetin, kaempferol, myricetin) are able to induce cytotoxicity in p53-mutated oesophageal squamous carcinoma cells together with upregulation of p63 and p73 [74]. Similarly, EGCG induces selective apoptosis in multiple myeloma cells with overexpression of p63 and p73 without any change in the p53 expression level [75], as well as overexpression of p73 in p53-mutated T-lymphocyte leukemic cells [76]. As previously mentioned, different isoforms of p73 have been described and quercetin has been shown to control the subcellular localization of the dominant negative isoform ∆Np73 in melanoma cells expressing wild-type p53. In this model, quercetin caused redistribution of ΔNp73 into the cytoplasm and nucleus, which has been associated with increased p53 transcriptional activity and apoptosis [47, 77]. Beside isolated compounds, more complex sources of polyphenol such as red wine polyphenolic extract or berries-derived product can also modulate p53 and/or p73 expression level,
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t|
Prostate cancer cells (LNCaP, DU145, p53-mutated CWR22Rv1, p53-null PC-3) | \n\t\t\t- No change in p53 mRNA, increased expression of p53-p(ser15) and/or p53-ac(lys382) and total p53 protein - p53 translocation to mitochondria - cell cycle alteration and apoptosis induction maintained in p53-mutated cancer cells - potentiation of radiation–induced p53 expression in p53-mutated cancer cells | \n\t\t\t[135, 158, 164, 165, 175, 176] | \n\t\t
Ovarian carcinoma cells (OVCAR-3) | \n\t\t\t- nuclear accumulation of p53-p(Ser15) | \n\t\t\t[110] | \n\t\t
Breast cancer cells (MCF-7, p53-mutated MDA-MB-231, p53-mutated MDA-MB-435) | \n\t\t\t- increased expression of p53-p(ser15), p53-p(ser20), p53-p(ser392) and p53-ac(lys382/lys373) | \n\t\t\t[177-180] | \n\t\t
- no change in total p53 protein expression, p53-independent apoptosis | \n\t\t\t[178, 181] | \n\t\t|
- increased expression of p53 mRNA and total protein | \n\t\t\t[182-184] | \n\t\t|
- no change in p53 mRNA | \n\t\t\t[185] | \n\t\t|
- p53-independent cytochrome c release | \n\t\t\t[181] | \n\t\t|
- increased p53-dependent transcriptional activity | \n\t\t\t[50] | \n\t\t|
Colon cancer cells (HCT116, p53-null HCT116) | \n\t\t\t- increased p53-p(ser15) expression - resveratrol-induced senescence is p53 dependent | \n\t\t\t[103] | \n\t\t
Pancreatic cancer cells (capan-2, colo357) | \n\t\t\t- upregulation and nuclear accumulation of p53 in both cell line (restoration of wild-type expression) | \n\t\t\t[186] | \n\t\t
Glioblastoma cells (A172, p53-mutated T98G) | \n\t\t\t- no change in p53 mRNA | \n\t\t\t[187] | \n\t\t
Hepatocellular carcinoma cells (HepG2) | \n\t\t\t- no change in p53 mRNA | \n\t\t\t[185] | \n\t\t
Osteosarcoma cells (U-2 OS) | \n\t\t\t- increased p53-p(ser15) and p53-p(Ser37) expression | \n\t\t\t[188] | \n\t\t
Lung adenocarcinoma cells (A549) | \n\t\t\t- increased p53-p(ser15) and p53-p(Ser37) expression | \n\t\t\t[188] | \n\t\t
Head and neck squamous cancer cells (UMSCC-22B) | \n\t\t\t- increased p53-p(ser15) and total p53 expression | \n\t\t\t[109] | \n\t\t
Cervical cancer cells (HeLa) | \n\t\t\t- increased p53-ac(lys373) and total p53 expression | \n\t\t\t[185] | \n\t\t
Hodgkin lymphoma cells (L-428) | \n\t\t\t- increased p53-p(ser15) expression | \n\t\t\t[129] | \n\t\t
Follicular lymphoma cells (LY8) | \n\t\t\t- increased p53-p(ser15) and total p53 expression | \n\t\t\t[190] | \n\t\t
Acute lymphoblastic leukemia cells (MOLT-4) | \n\t\t\t- increased p53-p(ser15) expression | \n\t\t\t[189] | \n\t\t
Neuroblastoma cells (B65, NUB-7) | \n\t\t\t- increased p53-p(ser15) and total p53 expression - nuclear translocation of p53 | \n\t\t\t[94, 132] | \n\t\t
DMBA-TPA-induced mouse skin tumor ; DEN-induced rat hepatocellular carcinoma | \n\t\t\t- increased p53-p(ser15) and total p53 expression | \n\t\t\t[18, 191, 192] | \n\t\t
- increased wild-type p53 and decreased mutated-p53 expression | \n\t\t\t[193] | \n\t\t
p53 family-related anticancer properties of resveratrol
Cancer cell lines express wild-type p53 except where otherwise stated; ac(lys.)=acetylated lysine, p(ser.)=phosphorylated serine
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t|
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Breast cancer cells (MCF-7, p53-mutated MDA-MB-231, p53-mutated SkBr3) | \n\t\t\t- increased expression of p53-p(ser15), no change or increased expression of total p53 -decreased expression of mutated p53 | \n\t\t\t[68, 178, 194-197] | \n\t\t
Cervical cancer cells (p53-mutated C33A, Caski) | \n\t\t\t-increased expression of p53 and p73 | \n\t\t\t[83, 198] | \n\t\t
Ovarian cancer cells (HEY, OVCA429, p53-mutated OCC1, p53-null SKOV3, CaOV3, Ho-8910) | \n\t\t\t-p53-independent cell death | \n\t\t\t[106] | \n\t\t
-increased expression of p53-p(ser15) -increased expression of p53 | \n\t\t\t[107, 199, 200] | \n\t\t|
Prostate cancer cells (LNCaP, p53-null PC3) | \n\t\t\t-increased expression of p53-p(ser15),p53-ac(lys) and total p53 protein -p53-independent cell death -p53 translocation to mitochondria | \n\t\t\t[95, 100, 201] | \n\t\t
Bladder cancer cells (p53 mutated-T-24 and AY-27) | \n\t\t\t-no change or increased expression of p53 | \n\t\t\t[202, 203] | \n\t\t
Erhlich Ascite carcinoma cells | \n\t\t\t-increased expression of p53-ac(lys373) and total p53 | \n\t\t\t[120] | \n\t\t
Colorectal cancer cells (LoVo, HCT116, p53-null HCT116, p53-mutated HT29, p53-mutated Colo205) | \n\t\t\t-increased expression of total p53 | \n\t\t\t[52, 204] | \n\t\t
-increased expression of p53-p(ser15), p53-p(ser33) and total p53 | \n\t\t\t[64] | \n\t\t|
-no change in p53 expression | \n\t\t\t[173] | \n\t\t|
-increased expression of p53-p(ser15) -decreased or unchanged expression of mutated p53 | \n\t\t\t[156, 205] | \n\t\t|
-cell cycle arrest, senescence and autophagy independent of p53 expression -cytochrome c release independent of p53 expression -increased expression of total p53 | \n\t\t\t[206, 207] | \n\t\t|
Colitis-associated colorectal cancer in mice | \n\t\t\t-no change in p53 expression | \n\t\t\t[17] | \n\t\t
Acute lymphoblastic leukemia cells (B6p210, T315I) | \n\t\t\t-increased expression of total p53 | \n\t\t\t[208] | \n\t\t
Chondrosarcoma cells and xenograft in nude mice (JJ012) | \n\t\t\t-increased expression of total p53 in vitro and in vivo -p53-dependent apoptosis | \n\t\t\t[57] | \n\t\t
Melanoma cells (MMRU, p53-mutated PMWK, B16BL6) | \n\t\t\t-no change in p53 expression | \n\t\t\t[209, 210] | \n\t\t
Glioblastoma cells (C6, U-87MG, p53-mutated U138MG and U251, DBTRG, T98G, T67) | \n\t\t\t-p53-independent cell death -unchanged or increased expression of p53 | \n\t\t\t[60, 71, 163, 211, 212] | \n\t\t
Neuroblastoma cells (SK-N-AS, NUB-7, p53-mutated SK-N-BE(2)) | \n\t\t\t-p53-independent cell death -nuclear translocation of p53 | \n\t\t\t[65, 94] | \n\t\t
p53 family-related anticancer properties of curcumin
Cancer cell lines express wild-type p53 except where otherwise stated; ac(lys.)=acetylated lysine, p(ser.)=phosphorylated serine
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t|
Breast Cancer cells (MCF7, p53-mutated MDA-MB-468) | \n\t\t\t-increased expression of p53-p(ser15) and total p53 -p53-independent cell death | \n\t\t\t[213, 214] | \n\t\t
Prostate cancer cells (LNCaP, p53-null PC-3, p53-expressing PC-3, PC3-ML, p53-mutated DU-145) | \n\t\t\t-increased expression of p53-p(ser6), p53-p(ser15), p53-p(ser20), p53-p(ser37), p53-p(ser392) , p53-ac(lys373), p53-ac(lys382) and total p53 -p53-dependent and independent cell death -increased expression of p73 | \n\t\t\t[53, 66, 89, 139, 140, 170, 215] | \n\t\t
PC3-ML cells (prostate cancer) xenograft in mice | \n\t\t\t-increased expression of p53 and p73 (synergistic effect with paclitaxel and docetaxel) | \n\t\t\t[170] | \n\t\t
Cervical cancer cells (Hela) | \n\t\t\t-increased expression of p53 | \n\t\t\t[216] | \n\t\t
Ovarian cancer cells (PA-1, p53-null SKOV3, p53-mutated OVCAR-3) | \n\t\t\t-p53-independent cell death | \n\t\t\t[217] | \n\t\t
Hepatocellular carcinoma cells (HepG2, p53-null Hep3B) | \n\t\t\t-increased expression of p53 -p53-independent cytotoxicity | \n\t\t\t[218, 219] | \n\t\t
Colorectal cancer cells ( HCT116, p53-mutated HT-29) | \n\t\t\t-increased expression of p53 | \n\t\t\t[55, 155, 157] | \n\t\t
Head and neck squamous carcinoma cells (KB, Hep2, Tu686, 686NL, Tu212, Tu177, p53-null M4e) | \n\t\t\t-increased expression of p53-p(ser15) and p53-p(ser37), -decreased expression of p53-p(ser6), p53-p(ser392) -unchanged or increased expression of p53 -p53-dependent cytotoxicity | \n\t\t\t[101, 172, 220] | \n\t\t
Head and neck squamous cell carcinoma syngenic mouse model (SCC VII/SF cells xenograft) | \n\t\t\t-increased | \n\t\t\t[101] | \n\t\t
Lung cancer cells (A549) | \n\t\t\t-increased expression of p53-p(ser15) and total p53 -absence of p73 expression -p53-dependent activation of caspases 3/7 | \n\t\t\t[221] | \n\t\t
Fibrosarcoma cells (HT-1080) | \n\t\t\t-increased expression of p53 | \n\t\t\t[222] | \n\t\t
Sarcoma xenograft (S180) | \n\t\t\t-increased | \n\t\t\t[90] | \n\t\t
Multiple myeloma cells (INA6) | \n\t\t\t-increased expression of p63 and 73, unchanged expression of p73 | \n\t\t\t[75] | \n\t\t
T lymphocyte leukemic cells (p53-mutated Jurkat, HuT-102, C91-PL, p53-mutated CEM) | \n\t\t\t-increased expression of p73 -increased expression of p53 | \n\t\t\t[76, 223] | \n\t\t
p53 family-related anticancer properties of EGCG
Cancer cell lines express wild-type p53 except where otherwise stated; ac(lys.)=acetylated lysine, p(ser.)=phosphorylated serine
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Grape-derived products (red wine, grape seed extract) | \n\t\t\tC26 colorectal cancer cells xenograft in mice | \n\t\t\t-increased expression of p53 and p73 | \n\t\t\t[78] | \n\t\t
Human colorectal cancer cells (LoVo, p53-mutated HT29, P53-mutated SW480) | \n\t\t\t-p53-independent apoptosis | \n\t\t\t[224] | \n\t\t|
Oral squamous carcinoma cells (SCC-25, p53-mutated OEC-MI) | \n\t\t\t-p53-independent cytotoxicity | \n\t\t\t[225] | \n\t\t|
Leukemia cells (p53-mutated Jurkat) | \n\t\t\t-increased expression of p73 | \n\t\t\t[80] | \n\t\t|
Teratocarcinoma cells (P19) | \n\t\t\t-increased expression of p53 | \n\t\t\t[174] | \n\t\t|
Prostate cancer cells (LNCaP, p53-mutated DU145) | \n\t\t\t-increased expression of p53-p(ser15) and total p53 | \n\t\t\t[226] | \n\t\t|
Black and green tea | \n\t\t\tDMBA-induced mammary tumor in rat | \n\t\t\t-increased expression of wild-type p53 and decreased expression of mutated-p53 | \n\t\t\t[227] | \n\t\t
3,4-benzopyrene-induced lung carcinoma in rat | \n\t\t\t-increased expression of p53 | \n\t\t\t[228] | \n\t\t|
Ehrlich’s ascites carcinoma cell xenograft in mice | \n\t\t\t-increased expression of p53 | \n\t\t\t[229] | \n\t\t|
Oral cells from smoker and non-smoker subjects | \n\t\t\t-increased expression of p53 | \n\t\t\t[230] | \n\t\t|
Patients with high-risk oral premalignant lesions | \n\t\t\t-no association between p53 expression and clinical response | \n\t\t\t[231] | \n\t\t|
Prostate cancer cells (LNCaP, p53-null PC3) | \n\t\t\t-increased expression of p53-ac(lys373), p53-ac(lys382) and total p53 -p53-independent apoptosis | \n\t\t\t[139, 140] | \n\t\t|
Colorectal cancer cells (LoVo, p53-mutated HT29) | \n\t\t\t-p53-independent cytotoxicity | \n\t\t\t[232] | \n\t\t|
Berry-derived products (aronia juice, strawberry extract) | \n\t\t\tLeukemia cells (p53-mutated Jurkat) | \n\t\t\t-increased expression of p73 | \n\t\t\t[79] | \n\t\t
Breast cancer cells (p53-mutated T47D) | \n\t\t\t-increased expression of p73 | \n\t\t\t[81] | \n\t\t
p53 family-related anticancer properties of polyphenolic sources
Cancer cell lines express wild-type p53 except where otherwise stated; ac(lys.)=acetylated lysine, p(ser.)=phosphorylated serine
Under physiological conditions, the transcriptional activity of p53 is downregulated by three different ways: i) ubiquitin-mediated proteasomal degradation mainly through the action of mouse double minute protein (MDM2), ii) nuclear export leading to a decrease in nuclear level, or iii) transcriptional repression of chromatin. MDM2 is an ubiquitin E3 ligase considered as an oncoprotein because of its activity in promoting p53 ubiquitination and proteasomal degradation. Moreover MDM2 binds to the NH2 terminus of p53 and blocks its transactivational activities [27]. Interestingly, MDM2 promotes also cell cycle progression independently of p53 for instance by modulating the activity of p21 [85]. Then MDM2 itself represents a potential target for new drug with chemotherapeutic properties including polyphenolic compounds [86]. Indeed, curcumin has been identified as an inhibitor of MDM2 expression (Figure 1)\n\t\t\t\t
As mentioned previously, p53 activity depends upon its expression level but also its subcellular localization. Indeed, p53 displays direct pro-apoptotic effects related to mitochondrial translocation and this pathway works in synergy with transcriptional activation function of p53 dependent upon its nuclear translocation [24, 93]. Therefore, the control of p53 subcellular localization might interfere with p53-mediated cell death. For instance, treatment of neuroblasma cells by either curcumin or resveratrol transiently upregulated p53 expression and induced nuclear translocation of p53, followed by induction of p21 and Bax expression associated with apoptosis [94]. In addition curcumin increases p53 and Bax expression in mitochondrial fraction under the control of the PI3K/Akt pathway in prostate cancer cells followed by caspase-dependent apoptosis [95]. Altogether, these data indicate that polyphenols are able to control not only p53 expression, but also its localization and therefore its pro-apoptotic activity in cancer cells. However, other post-translational regulatory effects of polyphenols have been also described and related to phosphorylation and acetylation of the tumor suppressor.
Phosphorylation of serine/threonine residues are essential for stabilization and activation of p53, the most extensively studied being serine 15 (Ser 15). These phosphorylation sites are mainly concentrated in the N-terminal transactivation domain and in the C-terminal regulatory domain [96]. Recent data about p53 phosphorylation induced by resveratrol, curcumin or EGCG,
Alternatively, MAPKs such as ERK1/2, p38 or JNK have been involved in p53 activation and phosphorylation [104, 105]. Therefore, the potential MAPKs/p53-dependent activation of apoptosis by polyphenols has been investigated. As previously mentioned, curcumin induces phospho-p38, phospho-ERK1/2 and phospho-JNK in colorectal as well as ovarian cancer cells [64, 106, 107]. In addition, it has been shown that resveratrol- or luteolin-induced apoptosis depends on the activities of ERK1/2, JNK and p38 kinase which target p53 phosphorylation at Ser 15 [49, 108]. An alternative pathway which implicates cyclooxygenase (COX)-2 activity and expression has been decribed by Lin et al. They have shown that resveratrol-induced apoptosis of human head and neck squamous cancer cells or human ovarian carcinoma cells is associated with p53 phosphorylation at Ser 15 and that both processes are downregulated by pERK1/2 and COX-2 specific inhibitors [109, 110]. Recent investigations indicate that ERK and p53 regulate each other and that ATM controls their interaction [104]. Therefore polyphenols might likely trigger p53 activation through ATM and MAPKs complementary pathways.
Functions of p53 and p73 are also regulated by acetylation on different lysine (Lys) residues. These posttranslational covalent modifications occur in response to DNA damage in the close vicinity of the oligomerization domain. The main Histone Acetyl Transferases (HATs) responsible for these modifications include p300, CREB-Binding Protein (CBP), P300/CBP-Associated Factor (PCAF) and Tat-Interactive Protein of 60 kDa (TIP60) [38]. As a consequence of its acetylation, p53 is stabilized by excluding ubiquitination on the same site and acetylation also promotes p53 transcriptional activity [96]. In comparison to p53, only few data are available concerning p73 interaction with HAT and acetylation. It has been established that p300 can acetylate p73 in response to DNA damage, but p300 can also behave as a co-activator of p73 independently of its HAT activity [111]. Importantly, the level of p53 or p73 acetylation seems to be a major way of regulation for the tumor suppressors function since deacetylated p53 and p73 are compromised in their ability to induce cell cycle arrest and apoptosis [91, 112].
The transcriptional coactivator p300 is a large multidomain protein that possesses histone acetyl-transferase ability [113]. Together with its homolog CBP, p300 mediate transcription through binding to transcriptional activators such as JUN, E1A, NF-κB, as well as to the p53 family members and they have been involved in human diseases including cancers [114]. Recent studies indicate that the transcriptional activity of p53 and p73 in response to genotoxic stress is regulated by its interaction with p300 [115-117]. Indeed, it has been established that interaction between p73 and p300 acetyl-transferase promotes first p73 stability and then its transcriptional activity.
Narayanan et al., have suggested that resveratrol-induced apoptosis of prostate cancer cells is mediated by transcriptional activation of p300 which subsequently acetylates and stabilizes p53 [118]. Similarly in breast cancer cells, resveratrol enhanced p300 expression and interaction with the phosphorylated form of p53 by a MAPK-dependent mechanism [119]. Interestingly, p53-p300 interaction fails to occur in doxorubicin-resistant cells, but curcumin pre-treatment could restore this interaction. Consistently, curcumin also restored drug-induced p53 acetylation (lysine 373) and p53-dependent transcription of Bax, PUMA, and Noxa in resistant cells leading to their apoptosis [120]. Therefore, polyphenols-induced acetylation of p53 by p300 might represent a key molecular mechanism for the cytotoxic properties of these natural compounds in cancer cells including chemoresistant cells (Figure 1).
Acetylation level of tumor suppressors is dependent upon the balance between acetylation and deacetylation reactions. Indeed, deacetylation of p53 or p73 by SIRT1 (silent information regulator 1), a member of the sirtuin Histone DeACetylase (HDAC) class III family, prevents p53-mediated transactivation of cell cycle inhibitor p21 and pro-apoptotic factor Bax, allowing promotion of cell survival after DNA damage and ultimately tumorigenesis [121]. Members of the Silent Information Regulator family (SIRT or sirtuins) are evolutionary conserved NAD-dependent protein deacetylases and adenosine diphosphate (ADP)-ribosylases. There are seven identified isoforms (SIRT1-7) that differ in their subcellular localization (cytoplasmic, mitochondrial or nuclear), substrate specificities and functions [122]. The founding member of this class of deacetylases, SIRT1 (homolog of yeast silent information regulator, Sir2), is the most widely studied sirtuins. SIRT1 has been associated with aging processes as well as a variety of human diseases such as metabolic syndrome, inflammation, neurodegeneration and more recently cancer [123, 124]. SIRT1 can deacetylate a variety of histones as well as a number of non-histone substrates, the first identified of these non-histone substrates being p53 (Lys 382-p53). The SIRT1 activity on p53 results in repression of p53-dependent apoptosis in response to DNA damage and oxidative stress [125, 126]. SIRT1 deacetylates also other tumor suppressors such as p73 [91]. Then SIRT1 has been considered as an oncogenic protein because of its role in inactivating tumor suppressors such as p53, p73 but also PTEN [127], and/or activating other oncogenic proteins like N-Myc [128]. Nevertheless, the oncogenic potential of SIRT1 has been controversial and, depending on the context, SIRT1 might also act as a tumor suppressor [122]. However, inhibition of the oncogenic potential of SIRT1 is likely able to induce apoptosis by counteracting the deacetylation of p53 or p73 and other key factors such as FOXO3a [91, 125, 129].
In 2003, resveratrol was the top hit in a screen designed to identify activators of sirtuin enzymes [130] and was subsequently shown to extend lifespan in yeast. However following experiments led to confusing data suggesting that resveratrol might not be a direct activator of SIRT1 [131]. Regardless of the controversy about its mode of action, resveratrol has been confirmed to have numerous health benefits, including anticancer properties. Nevertheless, the role of SIRT1 in the anti-proliferative and pro-apoptotic effects of resveratrol on cancer cells is still unclear. Indeed, in neuroblastoma cells, resveratrol-induced apoptosis was associated with a reduced expression of SIRT1 as well as up-regulation of the acetylated and active form of p53, but the pre-treatment of cancer cells with SIRT1 enzymatic inhibitors such as sirtinol or nicotinamide has no cytotoxic effect suggesting that resveratrol-induced apoptosis is independent of SIRT1 activity [132]. In the opposite, siRNA-mediated downregulation of SIRT1 in lymphoma cells decreased the resveratrol-induced apoptosis, indicating in this case a critical role of SIRT1 in polyphenol-mediated cancer cell death [133]. Interestingly, Frazzi et al., have recently described anti-proliferative effect of resveratrol associated with downregulation of SIRT1 expression and activity together with upregulation of acetylated-Lys 373-p53, the active form of p53, and total p53 overexpression [129]. All together, these data suggest that, in the context of cancer cells, resveratrol might be an inhibitor, instead of an activator, of SIRT1 functions (Figure 1). However, only few data are available concerning potential regulation of SIRT1/p53 pathway by other polyphenolic compounds [134]. Therefore additional investigations are needed to further understand the role of SIRT1 in polyphenols-mediated anticancer effects.
On the other hand, resveratrol has been shown to enhance p53 acetylation and apoptosis in prostate cancer cells through alternative pathways. Indeed, resveratrol caused down-regulation of MTA1 protein, leading to destabilization of MTA1/NuRD complex thus allowing acetylation/activation of p53 [135]. Metastasis-associated protein 1 (MTA1) is part of the nucleosome remodelling deacetylation (NuRD) complex involved in global and gene-specific histone deacetylation, alteration of chromatin structure and transcriptional repression [136, 137]. This complex, which also contains Histone DeACetylase (HDAC)1 and HDAC2, plays an essential role in governing deacetylation of histones but also non histone proteins, such as p53 [138]. In addition, green tea polyphenols have been shown to behave as HDAC class I inhibitors which results in p21 and Bax expression irrespective of p53 status in prostate cancer cells [139, 140]. Moreover HDAC inhibition by EGCG is associated with p53 acetylation in p53-wild-type LNCAP prostate cancer cells suggesting an increase of p53 halftime and binding to p21 and Bax promoters as previously described [141]. The mechanism by which HDAC inhibition could induce apoptosis in absence of functional p53 in p53-null PC3 prostate cancer cells might be related to interaction with p73 pathway as previously suggested [142, 143] or direct regulation of p21 promoter activation [144]. Similar HDAC inhibition by curcumin has been also seen in prostate cancer cells [145]. However the exact role of HDAC inhibition in polyphenol-induced apoptosis of cancer cells remains to be elucidated especially
Polyphenolic compounds have been extensively described as anti-oxidant molecules with the capability to scavenge reactive oxygen species (ROS), which include radical oxygen and nitrogen species such as O2- (superoxide anion), HO. (hydroxyl radical), NO. (nitric oxide radical), ONOO-. (peroxinitrite anion) and H2O2 (hydrogen peroxide), as well as oxidatively generated free radicals RO. and ROO. from biomolecules like lipids, proteins or nucleic acids (DNA and RNA) [3, 146]. Polyphenols are not only able to quench the ROS but also to regulate directly the oxidative stress-mediated enzyme activity, therefore reducing the formation of ROS. These anti-oxidant properties have been linked to the polyphenol-mediated reduction of chronic disease risk including cancer chemoprevention [13, 147]. Indeed, redox changes are often reported as important inducer of neoplastic transformation as well as chemoresistance. Cerutti et al. identified for the first time in 1985 the close relationship between pro-oxidant conditions and cancer development [148]. More than twenty years later, accumulated evidences indicate that the non-physiological alterations of the intracellular redox state could be considered as a hallmark of tumor biology. Indeed, redox changes have been involved in several key events of carcinogenesis such as self-sufficiency in growth signals [149, 150], resistance to apoptosis [151, 152], sustained angiogenesis [153, 154], autophagy and invasiveness. However, recent findings also suggest that this redox changes might be exploited as therapeutic strategy to selectively kill tumor cells.
Recently and unexpectedly, it has been established that various and structurally different (flavonoids or non-flavonoids) polyphenols are able to induce ROS (mainly superoxide anions or hydrogen peroxide) formation in cancer cells and for some of them to activate the DNA-damage response pathway [51, 79, 80, 95, 102, 103, 155-158]. Heiss et al. have also shown that the resveratrol-induced senescence in colon cancer cells is dependent upon an increased formation of ROS and the subsequent phosphorylation of p53 on the Ser15, suggesting a relationship between polyphenol-induced oxidative stress and p53 activation [103]. On the other hand, curcumin and wogonin induce ROS production and cause cytotoxicity in p53+/+ and p53-/- cancer cells [56, 64], indicating that ROS formation is an event independent of p53 and might be an earlier step in the cell death pathway. This hypothesis is supported by the study showing ROS in cancer cells as earlier as 20 minutes after the beginning of wogonin treatment. Moreover, in the same study, the subsequent up-regulation of p53 (maximal activation at 16 hours) is significantly inhibited by anti-oxidants such as N-Acetyl-Cysteine. Importantly, most of the studies did not investigate the possible alternative role of p73 in p53-mutated cells, but we and others have shown that in p53-mutated or p53-deficient cells, a polyphenolic compound (EGCG) or source (red wine polyphenolic extract, polyphenol-rich aronia juice) strongly induced oxidative stress-mediated up-regulation of p73 and apoptosis [79, 80, 84]. Further reports also supported that p53-family tumor suppressors regulation might be related to oxidative stress [159].
Interestingly, the cytotoxic effects induced by curcumin or its analogue HO-3867 were reduced in non-cancerous cells as well as the ROS formation in comparison to human ovarian cancer cells. This suggests that the specific pro-oxidant activity of polyphenols in cancer cells might explain the selective anticancer properties of these compounds, sparing healthy normal cells [107, 160]. Similarly, EGCG increased preferentially ROS formation, p53 and p21 expression and cytotoxicity in colorectal cancer cells but not in human embryonic kidney cells and normal human lung cell line [157].
Oxidative stress is one of the major conditions that damages DNA, acting as a mediator of environmental stressors such as UV- and X-rays irradiation, drugs, and of metabolic imbalance [161]. Since p53 might be regulated by the redox environment [162], especially by the ROS/DNA damage pathway, it has been proposed that polyphenol-mediated anticancer effects are related to a ROS/DNA damage/p53 pathway (Figure 1). Indeed polyphenol-induced DNA damage and apoptosis have been demonstrated with various compounds such as curcumin in glioblastoma and prostate cancer cells [100, 163], resveratrol in prostate cancer cells [164, 165], EGCG in lung cancer cells and xenograft in mice [166], wogonin in glioblastoma and prostate cancer cells [51, 56], and luteolin in lung and head and neck cancer cells [58]. Therefore the current molecular mechanism of the anticancer properties of polyphenols might involve selective ROS formation together with DNA damage in cancer cells. Thus, this process might lead to the regulation of several pathways (ATM/DNA-PK, MAPKs, p300, SIRT1, HDAC, see Figure 1), and ultimately to the expression and stabilization of p53-family tumor suppressors triggering programmed cell death.
Recent investigations have demonstrated additional or synergistic effects when polyphenols are combined with chemo- or radiotherapy. Indeed, resveratrol induces synergistic apoptosis with 5-fluorouracile [167]. Similar observations have been made with curcumin associated with doxorubicin, cisplatin, gemcitabine or radiation for cell death induction of glioblastoma cells and prostate cancer cells [60, 87]. More importantly, curcumin and its analogue, HO-3867 sensitized doxorubicin-resistant ascite carcinoma cells and breast cancer cells as well as cisplatin-resistant ovarian carcinoma cells together with enhanced p53 expression [107, 120, 168, 169]. Similarly, EGCG displayed synergistic upregulation of p53 and p73 as well as anticancer properties with taxanes (paclitaxel and docetaxel)
Interestingly, curcumin also ameliorated oxaliplatin-induced chemoresistance in colorectal cancer cells without significant effect on p53 expression [173]. Similarly, curcumin and EGCG sensitized glioma cells
Numerous
The present literature review has summarized the results of recent studies focusing mainly on the p53-related anticancer properties of three major polyphenolic compounds (resveratrol, curcumin and EGCG). Despite highly active research in this area, the data are still controversial concerning the possible key role of the tumor suppressor p53 in the polyphenol-mediated apoptosis of tumor cells. However, according to the emerging evidences suggesting that polyphenols might alternatively regulate also the structurally- and functionally-related tumor suppressors such as p73, these natural compounds might be considered as general executors of the p53 family-mediated programmed cell death in cancer cells. Importantly, the selective anticancer properties of polyphenols are maintained even when p53 is mutated or absent, as well as when cells are resistant to current therapies. However, further investigations are still mandatory to better understand the underlying molecular mechanism
Reactive distillation is an operation that incorporates chemical reaction and physical separation in a single unit [1]. This process, when applicable, has several potential advantages for the industry when compared to conventional systems, such as the reduction of capital costs, improvement of component separations, use of fewer instruments for monitoring, reduction of energy costs, and improvement in reaction selectivity [2, 3, 4, 5, 6]. Among the possible applications of reactive distillation, the separation of azeotropic mixtures and compounds with similar boiling points can be carried out by adding a reagent that promotes the consumption of one component of the mixture and forms a product with markedly different physical properties, thereby favoring separation. However, the use of reactive distillation is more frequent in systems where product formation is limited by chemical equilibrium. This separation technique allows for the constant removal of one or more products promotes or increases reactant conversion [7].
The first patents for the reactive distillation process were published in the 1920s, developed by Backhaus for the production of esters [8, 9, 10]. However, few industrial applications were developed before the 1980s [11], when the commercial process for methyl acetate synthesis via reactive distillation with a homogeneous catalyst was patented by Agreda and Partin [12], in collaboration with the Eastman Chemical Company. This application of reactive distillation is considered an exemplary case because of the substantial reduction in process costs (∼80%) [13] achieved through the elimination of units, such as reactors and separation columns, and the possibility of heat integration. The conventional methyl acetate synthesis process (Figure 1), which comprises 11 different steps and 28 pieces of equipment, was replaced by only a highly integrated reactive distillation column (Figure 2), enabling the aforementioned reduction in process costs. Recent uses of reactive distillation in chemicals production such as acetic acid and methanol [14], alkyl carbonates [15], butadiene [16], butyl acetate [17], carboxylic acids and ether [18] and carboxylic esters [19] are described in patents.
Schematic representation of the conventional process for the synthesis of methyl acetate. Caption: R01: reactor; S01: mixer; S02: extractive distillation; S03: solvent recovery; S04: methanol recovery (MeOH); S05: extractor; S06: azeotropic column; S07-S09: flash columns; S08: acetic acid recovery; V01: decanter.
Schematic representation of the integrated process for the production of methyl acetate by reactive distillation.
Reactive separation conveniently combines the production and removal of one or more products, enabling improvements in reaction productivity and selectivity. However, despite the benefits of this process, the planning and control of reactive distillation columns are hindered by complex interactions between reaction and separation. Operation conditions resulting in the formation of equilibrium between liquid–liquid–vapor phases, low mass transfer rates between liquid and vapor phases or diffusion inside the catalyst (for heterogeneous reactions) and chemical kinetics with reduced reaction rate need to be avoided or minimized [20]. Additionally, since both separation and reaction take place simultaneously in the same unit, the temperatures and pressures required for the two steps must have similar values [21]. If the overlap of operating conditions is not significant, the use of reactive distillation is not recommended [2].
The trend toward the use of biofuels has resulted from increased attention to topics related to mitigating environmental impacts by reducing the consumption of fossil fuels [22]. In this context, biodiesel is considered the main substitute fuel for diesel oil due to its lower polluting potential and the possibility of being used in diesel engines without the need for significant modifications [23, 24].
Biodiesel (alkyl esters) can be obtained by several reaction routes. The most conventional of them is the transesterification of triglycerides (oils) with short-chain alcohols (Eqs. (1)–(3)), such as methanol and ethanol, with homogeneous alkaline catalysts [25]. However, the raw material needed for this reaction must have reduced levels of acidity and moisture to avoid saponification reactions [26]. Due to the high costs of obtaining feedstocks that meet these specifications and competition with the food industry, studies aimed at the production of biodiesel from residual oils with a high content of fatty acids have been published [27, 28, 29, 30].
One of the alternatives to reduce the typical acidity of residual oils is to carry out a previous fatty acid esterification step [25] (Eq. (4)). However, in this process, with reaction and separation occurring in different units, chemical equilibrium limits the yield because the reaction that forms one of the products, water is reversible [31].
Therefore, the use of a reactive distillation column can be justified and may result in higher fatty acid conversions compared to those achieved in conventional systems. Tables 1 and 2 provide an overview of the literature on esterification and transesterification reactions aimed at producing biodiesel.
Ref. | Feedstock | Catalyst | Temperature | Simulation software | FFA conv. |
---|---|---|---|---|---|
[31]a | Oleic acid | H2SO4 (1–3 FFA wt%) | 130–150°C | — | 79.6% |
[32]a | Dodecanoic acid | Solid acid catalyst (0–5 FFA wt%) | 120–180°C | AspenOne 2004 | ∼95.0% |
[33]a | Dodecanoic acid | Metal oxides (0–10 FFA wt%) | 120–180°C | AspenOne 2004 | ∼72.0% |
[34]a | Non-edible oil mixture | Tin (II) chloride (1–9 oil wt%) | 40–60°C | Aspen Plus V8.8 | 78.3% |
[35]b | Dodecanoic acid | Sulfated Zirconia | 130°C (FFA feed) | Aspen Plus V9 | 99.9% |
[36]a | Fatty acids mixture | Nb2O5 (5 FFA wt%) | 90–170°C | Aspen Plus V7.3 | 96.0% |
[37]b | Fatty acids mixture | Sulfuric acid | 417°C (FFA feed) | Aspen Plus | — |
[38]b | Fatty acids | Solid acid catalysts | 58, 145, 207°C (FFA feed) | Fortran Algorithm | >98.0% |
[39]b | Hydrolyzed soybean oil | Niobium oxide | 207°C (FFA feed) | Fortran Algorithm | >98.0% |
[40]a | Fatty acids | Sulfuric acid (0.33–0.66 wt%) | 50–70°C | Aspen Plus V10 | ∼90.0% |
Reactive distillation studies aimed at the production of biodiesel through esterification.
Experimental tests performed by the authors.
No experimental tests performed by the authors.
Ref. | Feedstock | Catalyst | Temperature | Simulation software | Yield |
---|---|---|---|---|---|
[41]a | Soybean oil | NaOH (0.5–1.5 oil wt%) | 50°C (oil feed) | — | 98.2% |
[42]a | Canola oil | KOH, KOCH3 (0.73–1.83 oil wt%) | 100–160°C (reboiler) | — | 94.9% |
[43]a | Canola oil | KOH | 95–150°C (reboiler) | — | 94.4% |
[44]a | Cooking oil | 12-Tungstophosphoric acid hydrate | 20–30°C (oil feed) | — | 93.8% |
[45]a | Palm oil | KOH (0.5–2 oil wt%) | 85–120°C (reboiler) | — | 92.3% |
[46]b | Triolein | NaOH | 55°C (oil feed) | CHEMCAD/MATLAB | 90.3% |
[47]b | Soybean oil | NaOH | 25–90°C (oil feed) | HYSYS | ∼97.0% |
[48]b | Algal oil | H2SO4 | <150°C (reboiler) | MATLAB | — |
[49]b | Trilinolein | NaOH/Magnesium methoxide | 60–150°C (column) | Aspen Plus | 98.3% |
[50]b | Soybean oil | NaOH/CaO + Al2O3 | 60°C/25°C | Aspen Plus V8.4 | — |
Reactive distillation studies aimed at the production of biodiesel through transesterification.
Experimental tests performed by the authors.
No experimental tests performed by the authors.
In general, the main objective of the evaluated studies on esterification in reactive distillation columns is to increase the yield of the biodiesel production process by shifting the chemical equilibrium of the reaction. However, for transesterification studies, reactive distillation systems are considered mainly due to the possible reduction in the reaction time or in the purification costs of the biodiesel alkyl esters formed. This difference in purpose presumably originates from the necessity of milder transesterification reaction conditions when compared to the esterification route for the attainment of high biodiesel yields.
Mathematical modeling of a reactive distillation column was developed in [51] with considerations described in [52]. In their study, the authors assume the absence of chemical equilibrium in the stages and steady-state operation, with reaction rates being explicitly considered in the model of each stage, Murphree separation efficiency equal to 100% and feeding performed by single-phase streams.
Such methodology, presented in this section, was used in the studies of the references [2, 38, 39,53]. The nomenclature for the terms used in the equations is described in Table 3.
Symbol | Description |
---|---|
Ci,j | Molar concentration |
CLp,i | Molar specific heat in the liquid phase |
Ej | Relationship between vapor and liquid streams |
Fi,j | Feed molar flow |
f eqi,j | Phase equilibrium function |
f mi,j | Mass balance function |
f vlj | Function that correlates liquid and vapor streams |
f rk,j | Reaction rate function |
H Ij | Total enthalpy of the vapor stream |
HjII | Total enthalpy of the liquid stream |
k k,j | Kinetic reaction constant |
nIi,j | Molar flow of vapor |
nIIi,j | Molar flow of liquid |
nc | Number of components |
nr | Number of chemical reactions |
Psati,j | Liquid saturation pressure |
Pj | Pressure |
Qj | Heat added or removed |
R | Universal gas constant |
Rj | Liquid withdrawal fraction |
Tj | Temperature |
Tref | Reference temperature (298.15 K) |
vIIj | Liquid molar volume of pure compound |
xIi,j | Molar fraction in vapor phase |
xIIi,j | Molar fraction in liquid phase |
Zj | Lateral vapor withdrawal fraction |
αi,k | Kinetic order of reaction |
Δhvapi | Molar enthalpy of vaporization |
γvapi,j | Activity coefficient in the liquid phase |
νi,k | Stoichiometric coefficient |
ξ k,j | Reaction rate |
Nomenclature of terms used in mathematical modeling.
(references: i = component, j = column stage, k = reaction).
The generic plate scheme adopted by the authors is represented in Figure 3.
Configuration of each stage j in the reactive distillation column. Source: [
Eq. (5), which represents the mass balance of component i in stage j of the column as a residual function, is given by:
Assuming that the streams that leave the stage are in phase equilibrium, Eq. (6) relates the mole fractions in the liquid and vapor phases:
In this expression, the Poynting correction and the fugacity coefficient of the pure saturated compounds are neglected. In addition, and the vapor phase is considered to be an ideal gas mixture as a consequence of the assumption that the column operates at low pressure, close to atmospheric conditions.
The equation for the rate of reaction k in stage j is represented by Eq. (7), which can be expressed as a residual equation by applying the logarithm function (Eq. (8)).
Assuming that the molar volume of the liquid phase is that of an ideal solution and describing Eq. (8) as a function of the activity coefficients of the components in the liquid phase, Eq. (9) is obtained.
Eq. (10), which describes the energy balance of stage j, is needed to calculate the temperature, which is different at each stage of the reactive distillation column. Positive and negative values of Qj correspond to the heat being supplied to or removed from the column, respectively.
The ratio between the molar flows of vapor and liquid leaving each stage of the column is represented using Eq. (11). This equation is intended to make the condenser and reboiler specifications more flexible by associating the relationship between the liquid and vapor streams that leave the column stages.
When written in the residual form, as in Eq. (12), the equation has the following form:
The values of the Ej parameter for each form of operation of the condenser and reboiler (partial or total) are shown in Table 4.
Reboiler (stage 1) | Condenser (stage N) | ||
---|---|---|---|
Partial | Total | Partial | Total |
Z1 = 0 | Z1 ≠ 0 | ZN = 0 | ZN = 0 |
R1 = 0 | R1 = 0 | RN = 0 | RN ≠ 0 |
E1 ≠ 0 | E1 → ∞ | EN ≠ 0 | EN = 0 |
Characteristics of column heaters (reboiler and condenser).
In the study developed by [2, 36, 37, 48], all cases of simulated reactive distillation column configurations used a partial reboiler and total condenser (E1 ≠ 0 and EN = 0).
Solving the set of equations that describe a reactive distillation column is an arduous task, and rigorous mathematical models aimed at a computer simulation of this type of equipment were not developed until the 1970s [54].
In recent decades, commercial software that has specific models and algorithms for reactive distillation operations has been widely used, as shown previously in Tables 1 and 2. The simulations developed in the subsequent section, referring to case studies applied to biodiesel production and co-product valuation, use the RADFRAC module present in the commercial Aspen Plus software, which solves the equations of mass balance, energy balance, phase equilibrium and the sum of molar fractions (MESH) [55] through the “inside-out” algorithm [54].
The kinetic parameters for the esterification of fatty acids (FFA) present in corn distillers oil from DDGS (dried distillers grains with solubles) were estimated by a model fitting of the FFA conversion data (Table 5) obtained by our group. The reaction (Eq. (13)) was carried out at the temperatures of 150, 175 and 200°C, with ethanol and NbOPO4 (catalyst), following a molar alcohol:FFA ratio of 10:1 and catalyst load of 10% (FFA mass).
Temperature (°C) | Time (min) | FFA conversion (%) |
---|---|---|
150 | 15 | 1.68 |
30 | 3.99 | |
60 | 5.25 | |
120 | 9.68 | |
180 | 12.23 | |
240 | 19.44 | |
360 | 28.39 | |
175 | 15 | 10.13 |
30 | 15.36 | |
60 | 19.36 | |
120 | 24.21 | |
180 | 34.74 | |
240 | 43.06 | |
360 | 37.88 | |
200 | 15 | 9.46 |
30 | 17.97 | |
60 | 29.41 | |
120 | 38.35 | |
180 | 45.89 | |
240 | 48.53 | |
360 | 49.55 |
FFA conversion of the esterification reaction kinetic tests.
The methodology applied aims to estimate the pre-exponential factor (k0) and the activation energy (Ea) of the reaction. To fit the kinetic parameters, the objective function to be minimized is the squared difference between the experimental values of the FFA conversion and those calculated with a reversible pseudo-homogeneous model (Eq. (14)). The reaction rate (rF) equation was applied to Eq. (15), which describes a batch reactor in terms of the FFA conversion (x) in a given time (t).
A Nelder–Mead [56] simplex algorithm and a 4th order Runge–Kutta [57] method were used to perform the objective function minimization and conversion equation integration steps, respectively. The reaction rate (rF) was obtained through the model and the specific reaction rate constants k (L/mol.s) were expressed by the Arrhenius equation (Eq. (16)). Through this methodology, the parameters Ea and k0 for the reaction rate constants of the forward and reverse esterification reactions were estimated.
In these equations,
T = Absolute temperature at which the kinetic test was performed (K).
R = Universal gas constant (J/K.mol).
F = Fatty Acids (FFA).
A = Alcohol (ethanol).
E = Ethyl Esters (FAEE).
W = Water.
Cn = Concentration of compound n (mol/L).
t = time (s).
The compounds defined for the simulation of the fatty acid esterification with ethanol were specified in the Aspen Plus V.12 process simulator, with the fatty acid and oil fraction represented by oleic acid and triolein, respectively. A similar approach was used by other researchers as a simplification of the numerous components of the acid and oil fraction of the feedstock [58, 59, 60]. The NRTL thermodynamic model [61] was selected to evaluate the activity coefficients of the components of the reaction mixture and the NRTL binary interaction parameters missing from the simulator database were estimated directly through the Aspen Plus estimation tool that uses the UNIFAC model [62].
The flowsheet developed for the process simulation is shown in Figure 4 and consists of two columns, the first being responsible for the reactive distillation of the reactants fed to the process (C-EST) and the second for removing approximately 95% of the ethyl esters (FAEE) produced (C-DIST).
Flowsheet of the FFA esterification process (F = feed, P = product, S = intermediate stream, H = heat exchanger, B = pump, V = valve, C = column).
The simulated reactive distillation column has 22 total stages, of which 14 compose the reactive zone (5 to 18), while the C-DIST column consists of 10 total stages. The columns operating parameters are presented in Tables 6 and 7. Both the distillation columns have kettle-type reboilers, however the C-EST column is equipped with a total condenser, while the C-DIST with a partial condenser to separate the ethyl esters from the remaining oil and excess ethanol. It is noteworthy that the liquid phase composition and temperature profile graphs, as well as the conversions obtained follow the data referring to the process after the optimization described later.
Parameters | Before optimization |
---|---|
Stages | 22 |
Oil feed stage | 5 |
Ethanol feed stage | 18 |
Absolute pressure (bar) | 4 |
Distillate: feed molar ratio | 0.62 |
Reflux molar ratio | 0.08 |
Reactive distillation column operating parameters (C-EST).
Parameters | Distillation column |
---|---|
Stages | 10 |
Feed stage | 4 |
Absolute pressure (bar) | 0.03 |
Distillate: feed molar ratio | 0.51 |
Reflux molar ratio | 1.5 |
Distillation column operating parameters (C-DIST).
The H-1 and H-2 heat exchangers are responsible for heating the oil (F-OIL) and ethanol (F-ETOH) streams up to 200°C and 50°C, respectively, shown in Table 8, while the pumps B-1 and B-2 increase the pressure of the feed streams from 1 bar to 10 bar.
Stream | F-OIL | F-ETOH |
---|---|---|
Temperature (°C) | 20.00 | 20.00 |
Absolute pressure (bar) | 1.00 | 1.00 |
Enthalpy (kW) | −4058.33 | −2461.46 |
Mass Flow (kg/h) | ||
Oleic acid (FFA) | 900 | — |
Ethanol | — | 1467.86 |
Triolein | 5100 | — |
Ethyl oleate (FAEE) | — | — |
Water | — | — |
Properties of the oil feed and ethanol streams (F-OIL and F-ETOH).
The optimization of the process parameters of the esterification reactive distillation column was performed using the MATLAB® R2020b software by implementing the MEIGO package (Metaheuristics for Bioinformatics Global Optimization) [63], an optimization supplement for global optimal search which can be used to optimize industrial processes [64, 65]. The results obtained through Aspen Plus simulations were provided to MATLAB, where the optimization algorithm was performed, and new obtained values of the variables evaluated were used to carry out new simulations iteratively.
The Particle Swarm Optimization (PSO) method was applied to minimize the objective function that describes the conversion of fatty acids (Eq. (17)), starting from an initial population of 50 particles (solution vectors) defined by the algorithm in the pre-defined search intervals.
For the simulation, the varied parameters were molar reflux ratio, internal pressure, molar ratio between distillate stream and total feed, and oil and ethanol feed stages. As restrictions, the reboiler temperature, the recovery of the desired product (ethyl esters) at the bottom of the column and the feed stages of the reagents were evaluated with Eqs. (18)–(20). The reboiler temperature upper limit was defined as 200°C to avoid degradation of the reagents or products and excessive use of the hot utility. It is observed that the minimization of the negative value of the conversion corresponds to the maximization of its positive value.
In these equations,
The kinetic constants obtained through the discussed methodology are presented in Table 9, with the direct reaction of ethyl esters formation indicated by the subscript “1” and the reverse reaction of fatty acids formation indicated by the subscript “2”. Figure 5 shows the comparison between the experimental and calculated conversions, along with the R2 coefficient of the fit for each temperature.
Parameter | Value |
---|---|
K0,1(L/mol.s) | 252.786 |
K0,2(L/mol.s) | 207.093 |
Ea1 (J/mol) | 51,357.1 |
Ea2 (J/mol) | 39,244.1 |
Estimated kinetic constants for the esterification reaction.
Experimental (−) and calculated FFA conversion at 150°C (∆), 175°C (
Observing the results presented, it is noted that the data fitting at 200°C presented a high coefficient of determination, while the data fitting at 175°C obtained a reduced R2. However, as the temperature in the reactive section of the esterification column is, on average, close to 195°C, it was concluded that due to the excellent results achieved in the data fitting at 200°C, the use of the estimated kinetic parameters would not hinder the development of a simulation faithful to the real behavior of the reaction.
The composition and temperature profiles along the stages of the reactive distillation column (column C-EST in Figure 4) are presented in Figures 6 and 7.
Liquid phase mass composition profile (C-EST).
Column temperature profile (C-EST).
The liquid phase composition profile of the C-EST column (Figure 6) indicates that the major component for all stages with values higher than 5 (closer to the bottom) is triolein, while in the others there is a predominance of oleic acid, ethyl oleate (FAEE), ethanol and water, since only negligible amounts of triolein are evaporated along the column, as seen in the vapor phase mass composition profile. Additionally, there is a significant increase in the fraction of ethanol and water in the liquid state in the first stage due to the use of a total condenser in the reactive distillation column.
The composition of the streams that characterize the main products of the process (S-FAEE, P-OIL and P-FAEE) are presented in Table 10 and, based on the simulation results, there is a final fatty acids conversion (mol) of 83.97% inside the column, 94.00% of which is recovered in the P-FAEE stream, while 5.83% is recovered in the P-OIL stream. The remaining 0.17% of FAEE is located at the P-ETOH2 stream. The resulting stream of the desired product (P-FAEE) has a purity (FAEE) greater than 98%, resulting in an ester content superior to the value described in Brazilian and European specifications [66, 67].
Stream | S-FAEE | P-FAEE | P-OIL |
---|---|---|---|
Temperature (°C) | 162.39 | 90.00 | 305.08 |
Absolute pressure (bar) | 8.13 | 0.03 | 0.03 |
Enthalpy (kW) | −3813.85 | −506.62 | −2532.30 |
Mass Flow (kg/h) | |||
Oleic acid (FFA) | 144.22 | 11.80 | 132.41 |
Ethanol | 186.96 | 0.58 | — |
Triolein | 5100.00 | — | 5100.00 |
Ethyl oleate (FAEE) | 830.34 | 780.97 | 48.43 |
Water | 0.58 | — | — |
Composition of the main product streams.
In Table 10, it is possible to observe that there are still traces of ethyl esters present in the oil stream. However, this amount corresponds to less than 1% of the total mass fraction of the stream. P-OIL, therefore, was considered to be non-significant. Furthermore, of the 900 kg/h of FFA fed to the process, only 132.41 kg/h remain, characterizing a reduction of 85.29% of the total fatty acid mass. Finally, the energy demands for H-1, H-2, condensers and reboilers of columns C-EST and C-DIST are presented in Table 11.
Equipment | Energy demand (kW) |
---|---|
H-1 | 619.71 |
H-2 | 31.60 |
C-EST (condenser) | −295.57 |
C-EST (reboiler) | 330.86 |
C-DIST (condenser) | −581.94 |
C-DIST (reboiler) | 1095.36 |
Energy demand of the process equipment.
Table 12 shows the limits and initial estimates for the variables evaluated for the optimization of the esterification process. Table 13 displays the constraints imposed on the reboiler temperature, ethyl ester recovery fraction (FAEE), and conversion. The values chosen as initial estimates were obtained by manually setting different values for the reflux molar ratio, condenser pressure, and distillate feed molar ratio, and adopting the best result obtained.
Variable | Molar reflux ratio | Condenser pressure (bar) | Distilled molar ratio: feed | Oil feed stage | Ethanol feed stage |
---|---|---|---|---|---|
Lower Limit | 0.005 | 0.01 | 0.50 | 5 | 5 |
Upper Limit | 2 | 10 | 0.70 | 18 | 18 |
Initial Estimate | 0.08 | 4 | 0.62 | 5 | 18 |
Lower, upper limits and initial estimates for the variables evaluated in the esterification reaction optimization process.
Restrictions | Reboiler temperature (°C) | Recovery fraction of FAEE | Conversion (%) |
---|---|---|---|
Lower Limit | −273.15 | 0.99 | 0 |
Upper Limit | 200 | 1.00 | 100 |
Initial constraints for the response variables for the variables evaluated in the esterification reaction optimization process.
The results obtained are shown in Figure 8, with a maximum conversion of 83.97% and the final values of the variables are added to Table 14.
Evolution of the FFA conversion as a function of the number of optimization iterations.
Variable | Molar reflux ratio | Condenser pressure (bar) | Distilled molar ratio: feed | Oil feed stage | Ethanol feed stage |
---|---|---|---|---|---|
Result | 0.1130 | 8.1314 | 0.6806 | 5 | 18 |
Response vector of input variables for the esterification reaction optimization process.
An additional simulation performed in a CSTR reactor achieved an FFA conversion of 51.06%, while the maximum average conversion in the kinetic tests (200°C) was 49.55%. The simulated CSTR operated at a constant temperature of 200°C with the residence time of 3 h (same duration of the experimental tests) and was fed with streams following equal mass flows and compositions to the RDC column feed streams. Thus, the optimization results represent a significant improvement of 64.45% and 69.46% compared to the CSTR and experimental tests, respectively, inferring that the use of a reactive distillation column could be beneficial to the process.
As biodiesel production increases so do the production of glycerol as for each liter of biodiesel produced, approximately 100 mL of crude glycerol are obtained [68]. Among the transformation processes for glycerol to viable chemical intermediates, glycerol ketalization for the production of solketal has gained prominence. Solketal can be used as an additive to increase the octane and fluid dynamic properties of the fuel. The addition of up to 5% by volume of solketal to gasoline leads to a significant decrease in gum formation [69]. With this motivation, this study aims to simulate the operation of a reactive distillation column for the production of solketal from glycerol with acetone using heterogeneous catalysis, with high conversion of reagents and separation of the components of the reaction.
The applied methodology considers the ketalization reaction of glycerol (G) with acetone (A), forming solketal (S) and water (W). The reaction is considered reversible and elementary, being described by Eq. (21):
A pseudo-homogeneous model was used to describe the reaction kinetics through a system of differential equations of concentration over time, at different temperatures, in which the kinetic constants of the direct and inverse reaction are represented, respectively, by k1 and k−1(L/mol.s), while the molar concentrations (mol/L) of the species involved are given by CG, CA, CS and CW (Eq. (22)).
The solution of the system of differential equations using a 4th order Runge Kutta method [57] and the fitting of the kinetic parameters, k1 and k−1, and subsequent estimation of the Arrhenius equation parameters were performed by a Nelder–Mead simplex algorithm [56]. The experimental data used was retrieved from the study of [70].
The kinetic parameters evaluated were later used to predict the solketal formation reaction in a reactive distillation column, using the rigorous RADFRAC distillation model of the Aspen Plus commercial simulation software. The system considered in this study is shown in Figure 9.
Flowsheet of the solketal production process used in this study.
Using the estimated kinetic parameters, the glycerol ketalization reaction for the production of solketal was modeled in the Aspen Plus software. For the process simulation, the pressure inside the column was set at 10 atm. The feeding of the 13-stage column, RDC in Figure 9, are streams GLI-02 and ACE-02, originated from the heating of the currents GLI-01 and ACE-01 up to 95°C and 55°C, by the heat exchangers H1 and H2, respectively.
The ACE-01 stream is composed only of acetone, while GLI-01 contains 80% glycerol and 20% water by mass, disregarding other components such as methanol or dissolved salts normally present in glycerol from biodiesel production processes [71]. The products of reactive distillation are characterized by TOP-P and BOT-P streams, which correspond, respectively, to the streams rich in the most volatile and least volatile substances in the process.
Figure 10 shows the concentrations as a function of time according to the fitted kinetic parameters data.
Experimental and calculated concentrations (80°C).
Analyzing Figure 10, it is observed that the curves generated using the fitted parameters represented the experimental data satisfactorily. Table 15 presents the process specifications obtained after a sensitivity analysis, aiming to simulate a column with optimal operating conditions. Figure 11 shows the composition profile in the liquid phase as a function of the column stage number (1 = condenser and 13 = reboiler).
Parameter | Description |
---|---|
Number of stages | 13 |
Condenser type | Total |
Reboiler type | Kettle |
Molar reflux ratio | 0.69 |
Reboiler/condenser heat duty | 55.000 / -43.723 cal/s |
Column pressure | 10 bar |
Glycerol feed | 3rd stage |
Acetone feed | 11th stage |
Feed properties | 95 and 55°C - 1 bar |
Glycerol feed molar flow | 2.500 kmol/h |
Water feed molar flow | 0.625 kmol/h |
Acetone feed molar flow | 15.000 kmol/h |
Ketalization reaction stages | 3 to 11 |
RDC column specifications.
RDC column liquid phase composition profile.
The conversion of glycerol obtained for the operational conditions defined for the simulation was 98.2%, indicating the reaction occurred inside the column.
The SOLKETAL stream in Figure 9 has 99.53% solketal and the WATER stream consists of 99.82% water, on a mass basis. Thus, the simulations show that the methodology employed results in a high purity solketal product stream with solketal conversion superior to 98%. However, additional studies are needed to assess the effect of possible intermediate reactions on the process yield.
In this chapter, a general introduction regarding reactive distillation technology and its application to the biodiesel production process was presented. A literature-based mathematical model to describe reactive distillation columns was discussed, along with experimental and simulation studies developed by the authors of this chapter, using commercial software such as Aspen Plus.
In the case study of biodiesel production through the esterification of a low-cost feedstock, the application of an optimized reactive distillation column promoted an improvement of approximately 70% about FFA conversion. The resulting product stream attained purity above 98% in relation to alkyl esters. Additionally, the production of solketal aiming at the valorization of a co-product of the biodiesel production process (glycerol), was studied through the development of a flowsheet in the Aspen Plus simulator, resulting in a solketal stream with purity above 99%.
The results obtained through the developed studies indicate that the reactive distillation technology, applied to fatty acid esterification reactions for the production of biodiesel and ketalization of glycerol for the production of solketal, is promising and attractive in technical terms, however, further studies are necessary to analyze the economic feasibility of both processes.
The authors thank UTFPR and Sinochem Petroleum Brazil Limited (project 001/2019) for financial support.
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by",editors:[{id:"7153",title:"Prof.",name:"Gustavo",middleName:null,surname:"Morari Do Nascimento",slug:"gustavo-morari-do-nascimento",fullName:"Gustavo Morari Do Nascimento"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10014",title:"Thin Films",subtitle:null,isOpenForSubmission:!1,hash:"f56a9427ff53d989f007df97f6ad873c",slug:"thin-films",bookSignature:"Alicia Esther Ares",coverURL:"https://cdn.intechopen.com/books/images_new/10014.jpg",editedByType:"Edited by",editors:[{id:"91095",title:"Dr.",name:"Alicia Esther",middleName:null,surname:"Ares",slug:"alicia-esther-ares",fullName:"Alicia Esther Ares"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10941",title:"Ferrites",subtitle:"Synthesis and Applications",isOpenForSubmission:!1,hash:"f6a323bfa4565d7c676bc3733b4983b0",slug:"ferrites-synthesis-and-applications",bookSignature:"Maaz Khan",coverURL:"https://cdn.intechopen.com/books/images_new/10941.jpg",editedByType:"Edited by",editors:[{id:"107765",title:"Dr.",name:"Maaz",middleName:null,surname:"Khan",slug:"maaz-khan",fullName:"Maaz Khan"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:315,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"37067",doi:"10.5772/35482",title:"Fourier Transform Infrared Spectroscopy for Natural Fibres",slug:"fourier-transform-infrared-spectroscopy-for-natural-fibres",totalDownloads:9257,totalCrossrefCites:162,totalDimensionsCites:394,abstract:null,book:{id:"2270",slug:"fourier-transform-materials-analysis",title:"Fourier Transform",fullTitle:"Fourier Transform - Materials Analysis"},signatures:"Mizi Fan, Dasong Dai and Biao Huang",authors:[{id:"104647",title:"Prof.",name:"Mizi",middleName:null,surname:"Fan",slug:"mizi-fan",fullName:"Mizi Fan"}]},{id:"60680",doi:"10.5772/intechopen.76082",title:"Environmental Contamination by Heavy Metals",slug:"environmental-contamination-by-heavy-metals",totalDownloads:16133,totalCrossrefCites:180,totalDimensionsCites:391,abstract:"The environment and its compartments have been severely polluted by heavy metals. This has compromised the ability of the environment to foster life and render its intrinsic values. Heavy metals are known to be naturally occurring compounds, but anthropogenic activities introduce them in large quantities in different environmental compartments. This leads to the environment’s ability to foster life being reduced as human, animal, and plant health become threatened. This occurs due to bioaccumulation in the food chains as a result of the nondegradable state of the heavy metals. Remediation of heavy metals requires special attention to protect soil quality, air quality, water quality, human health, animal health, and all spheres as a collection. Developed physical and chemical heavy metal remediation technologies are demanding costs which are not feasible, time-consuming, and release additional waste to the environment. This chapter summarises the problems related to heavy metal pollution and various remediation technologies. A case study in South Africa mines were also used.",book:{id:"6534",slug:"heavy-metals",title:"Heavy Metals",fullTitle:"Heavy Metals"},signatures:"Vhahangwele Masindi and Khathutshelo L. Muedi",authors:[{id:"225304",title:"Dr.",name:"Vhahangwele",middleName:null,surname:"Masindi",slug:"vhahangwele-masindi",fullName:"Vhahangwele Masindi"},{id:"241403",title:"M.Sc.",name:"Khathutshelo",middleName:"Lilith",surname:"Muedi",slug:"khathutshelo-muedi",fullName:"Khathutshelo Muedi"}]},{id:"42566",doi:"10.5772/53706",title:"Challenges and Opportunities for Spark Plasma Sintering: A Key Technology for a New Generation of Materials",slug:"challenges-and-opportunities-for-spark-plasma-sintering-a-key-technology-for-a-new-generation-of-mat",totalDownloads:9148,totalCrossrefCites:98,totalDimensionsCites:209,abstract:null,book:{id:"3478",slug:"sintering-applications",title:"Sintering Applications",fullTitle:"Sintering Applications"},signatures:"M. Suárez, A. Fernández, J.L. Menéndez, R. Torrecillas, H. U. Kessel, J. Hennicke, R. Kirchner and T. Kessel",authors:[{id:"102383",title:"Dr.",name:"Marta",middleName:null,surname:"Suárez",slug:"marta-suarez",fullName:"Marta Suárez"},{id:"103822",title:"Dr.",name:"J.L",middleName:null,surname:"Menendez",slug:"j.l-menendez",fullName:"J.L Menendez"},{id:"103833",title:"Prof.",name:"Ramón",middleName:null,surname:"Torrecillas",slug:"ramon-torrecillas",fullName:"Ramón Torrecillas"},{id:"162633",title:"Dr.",name:"Adolfo",middleName:null,surname:"Fernández",slug:"adolfo-fernandez",fullName:"Adolfo Fernández"}]},{id:"23617",doi:"10.5772/24118",title:"Collagen- vs. Gelatine-Based Biomaterials and Their Biocompatibility: Review and Perspectives",slug:"collagen-vs-gelatine-based-biomaterials-and-their-biocompatibility-review-and-perspectives",totalDownloads:9417,totalCrossrefCites:60,totalDimensionsCites:200,abstract:null,book:{id:"1487",slug:"biomaterials-applications-for-nanomedicine",title:"Biomaterials",fullTitle:"Biomaterials Applications for Nanomedicine"},signatures:"Selestina Gorgieva and Vanja Kokol",authors:[{id:"55577",title:"Prof.",name:"Vanja",middleName:null,surname:"Kokol",slug:"vanja-kokol",fullName:"Vanja Kokol"},{id:"61285",title:"BSc",name:"Selestina",middleName:null,surname:"Gorgieva",slug:"selestina-gorgieva",fullName:"Selestina Gorgieva"}]},{id:"46243",doi:"10.5772/57255",title:"Corrosion Inhibitors – Principles, Mechanisms and Applications",slug:"corrosion-inhibitors-principles-mechanisms-and-applications",totalDownloads:13693,totalCrossrefCites:40,totalDimensionsCites:159,abstract:null,book:{id:"3817",slug:"developments-in-corrosion-protection",title:"Developments in Corrosion Protection",fullTitle:"Developments in Corrosion Protection"},signatures:"Camila G. Dariva and Alexandre F. Galio",authors:[{id:"169261",title:"Dr.",name:"Camila",middleName:"G.",surname:"Dariva",slug:"camila-dariva",fullName:"Camila Dariva"},{id:"170138",title:"Dr.",name:"Alexandre",middleName:"Ferreira",surname:"Galio",slug:"alexandre-galio",fullName:"Alexandre Galio"}]}],mostDownloadedChaptersLast30Days:[{id:"76780",title:"Basics of Clay Minerals and Their Characteristic Properties",slug:"basics-of-clay-minerals-and-their-characteristic-properties",totalDownloads:1748,totalCrossrefCites:13,totalDimensionsCites:18,abstract:"Clay minerals such as kaolinite, smectite, chlorite, micas are main components of raw materials of clay and formed in presence of water. A large number of clays used to form the different structure which completely depends on their mining source. They are known as hydrous phyllosilicate having silica, alumina and water with variable amount of inorganic ions like Mg2+, Na+, Ca2+ which are found either in interlayer space or on the planetary surface. Clay minerals are described by presence of two-dimensional sheets, tetrahedral (SiO4) and octahedral (Al2O3). There are different clay minerals which are categorized based on presence of tetrahedral and octahedral layer in their structure like kaolinite (1:1 of tetrahedral and octahedral layers), smectite group of clay minerals (2:1 of tetrahedral and octahedral layers) and chlorite (2:1:1 of tetrahedral, octahedral and octahedral layers). The particle size of clay minerals is <2microns which can be present in form of plastic in presence of water and solidified when dried. The small size and their distinctive crystal structure make clay minerals very special with their unique properties including high cation exchange capacity, swelling behavior, specific surface area, adsorption capacity, etc. which are described in this chapter. Due to all these unique properties, clay minerals are gaining interest in different fields.",book:{id:"10949",slug:"clay-and-clay-minerals",title:"Clay and Clay Minerals",fullTitle:"Clay and Clay Minerals"},signatures:"Neeraj Kumari and Chandra Mohan",authors:[{id:"258132",title:"Dr.",name:"Chandra",middleName:null,surname:"Mohan",slug:"chandra-mohan",fullName:"Chandra Mohan"},{id:"352399",title:"Dr.",name:"Neeraj",middleName:null,surname:"Kumari",slug:"neeraj-kumari",fullName:"Neeraj Kumari"}]},{id:"51535",title:"An Introduction to Hydrogels and Some Recent Applications",slug:"an-introduction-to-hydrogels-and-some-recent-applications",totalDownloads:11606,totalCrossrefCites:66,totalDimensionsCites:133,abstract:"Hydrogels have existed for more than half a century, and today they have many applications in various processes ranging from industrial to biological. There are numerous original papers, reviews, and monographs focused on the synthesis, properties, and applications of hydrogels. This chapter covers the fundamental aspects and several applications of hydrogels based on the old and the most recent publications in this field.",book:{id:"5251",slug:"emerging-concepts-in-analysis-and-applications-of-hydrogels",title:"Emerging Concepts in Analysis and Applications of Hydrogels",fullTitle:"Emerging Concepts in Analysis and Applications of Hydrogels"},signatures:"Morteza Bahram, Naimeh Mohseni and Mehdi Moghtader",authors:[{id:"179718",title:"Prof.",name:"Morteza",middleName:null,surname:"Bahram",slug:"morteza-bahram",fullName:"Morteza Bahram"},{id:"185713",title:"Dr.",name:"Naimeh",middleName:null,surname:"Mohseni",slug:"naimeh-mohseni",fullName:"Naimeh Mohseni"},{id:"185714",title:"Dr.",name:"Mehdi",middleName:null,surname:"Moghtader",slug:"mehdi-moghtader",fullName:"Mehdi Moghtader"}]},{id:"70661",title:"Bioremediation Techniques for Polluted Environment: Concept, Advantages, Limitations, and Prospects",slug:"bioremediation-techniques-for-polluted-environment-concept-advantages-limitations-and-prospects",totalDownloads:2538,totalCrossrefCites:9,totalDimensionsCites:24,abstract:"Environmental pollution has been rising in the past few decades due to increased anthropogenic activities. Bioremediation is an attractive and successful cleaning technique to remove toxic waste from polluted environment. Bioremediation is highly involved in degradation, eradication, immobilization, or detoxification diverse chemical wastes and physical hazardous materials from the surrounding through the all-inclusive and action of microorganisms. The main principle is degrading and converting pollutants to less toxic forms. Bioremediation can be carried out ex-situ and in-situ, depending on several factors, which include but not limited to cost, site characteristics, type, and concentration of pollutants. Hence, appropriate bioremediation technique is selected. Additionally, the major methodologies to develop bioremediation are biostimulation, bioaugmentation, bioventing, biopiles, and bioattenuation provided the environmental factors that decide the completion of bioremediation. Bioremediation is the most effective, economical, eco-friendly management tool to manage the polluted environment. All bioremediation techniques have its own advantage and disadvantage because it has its own specific applications.",book:{id:"9343",slug:"trace-metals-in-the-environment-new-approaches-and-recent-advances",title:"Trace Metals in the Environment",fullTitle:"Trace Metals in the Environment - New Approaches and Recent Advances"},signatures:"Indu Sharma",authors:[{id:"301262",title:"Associate Prof.",name:"Indu",middleName:null,surname:"Sharma",slug:"indu-sharma",fullName:"Indu Sharma"}]},{id:"18275",title:"Modeling and Identification of Parameters the Piezoelectric Transducers in Ultrasonic Systems",slug:"modeling-and-identification-of-parameters-the-piezoelectric-transducers-in-ultrasonic-systems",totalDownloads:10078,totalCrossrefCites:3,totalDimensionsCites:5,abstract:null,book:{id:"201",slug:"advances-in-ceramics-electric-and-magnetic-ceramics-bioceramics-ceramics-and-environment",title:"Advances in Ceramics",fullTitle:"Advances in Ceramics - Electric and Magnetic Ceramics, Bioceramics, Ceramics and Environment"},signatures:"Pawel Fabijanski and Ryszard Lagoda",authors:[{id:"13086",title:"Dr.",name:"Pawel",middleName:null,surname:"Fabijański",slug:"pawel-fabijanski",fullName:"Pawel Fabijański"}]},{id:"60680",title:"Environmental Contamination by Heavy Metals",slug:"environmental-contamination-by-heavy-metals",totalDownloads:16114,totalCrossrefCites:178,totalDimensionsCites:384,abstract:"The environment and its compartments have been severely polluted by heavy metals. This has compromised the ability of the environment to foster life and render its intrinsic values. Heavy metals are known to be naturally occurring compounds, but anthropogenic activities introduce them in large quantities in different environmental compartments. This leads to the environment’s ability to foster life being reduced as human, animal, and plant health become threatened. This occurs due to bioaccumulation in the food chains as a result of the nondegradable state of the heavy metals. Remediation of heavy metals requires special attention to protect soil quality, air quality, water quality, human health, animal health, and all spheres as a collection. Developed physical and chemical heavy metal remediation technologies are demanding costs which are not feasible, time-consuming, and release additional waste to the environment. This chapter summarises the problems related to heavy metal pollution and various remediation technologies. A case study in South Africa mines were also used.",book:{id:"6534",slug:"heavy-metals",title:"Heavy Metals",fullTitle:"Heavy Metals"},signatures:"Vhahangwele Masindi and Khathutshelo L. Muedi",authors:[{id:"225304",title:"Dr.",name:"Vhahangwele",middleName:null,surname:"Masindi",slug:"vhahangwele-masindi",fullName:"Vhahangwele Masindi"},{id:"241403",title:"M.Sc.",name:"Khathutshelo",middleName:"Lilith",surname:"Muedi",slug:"khathutshelo-muedi",fullName:"Khathutshelo Muedi"}]}],onlineFirstChaptersFilter:{topicId:"14",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82539",title:"Pulsed Laser Deposition of Transparent Conductive Oxides on UV-NIL Patterned Substrates for Optoelectronic Applications",slug:"pulsed-laser-deposition-of-transparent-conductive-oxides-on-uv-nil-patterned-substrates-for-optoelec",totalDownloads:0,totalDimensionsCites:0,doi:"10.5772/intechopen.105798",abstract:"Transparent conductive oxide (TCO) electrodes are key components in the fabrication of optoelectronic devices such as organic photovoltaic cells (OPVs) or organic emitting devices (OLEDs). Pulsed laser deposition (PLD) results in TCO coatings with adequate optical and electrical properties, the preservation of the target chemical composition in the transferred films being the major advantage of this technique. Furthermore, the performance of the optoelectronic devices can be enhanced by patterning the TCO electrodes. Indium tin oxide (ITO) remains the most popular TCO due to its high conductivity and transparency. The scarcity of the indium resources encouraged the efforts to find an alternative to ITO, a promising candidate being Al-doped ZnO (AZO). Therefore, this chapter is focused on PLD deposition of TCO films (ITO and AZO) on patterned glass substrates prepared by ultraviolet nanoimprint lithography (UV-NIL) for obtaining transparent electrodes with improved characteristics, which further can be integrated in optoelectronic applications.",book:{id:"11484",title:"Thin Film Deposition - Fundamentals, Processes, and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11484.jpg"},signatures:"Marcela Socol, Nicoleta Preda, Carmen Breazu and Oana Rasoga"},{id:"81742",title:"Collagen-Based Biomaterial as Drug Delivery Module",slug:"collagen-based-biomaterial-as-drug-delivery-module",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.103063",abstract:"In the field of medicine, controlled drug delivery has become a major challenge due to inefficiency of drug at critical parameters such as permeability, solubility, half-life, targeting ability, bio- & hemocompatibility, immunogenicity, off-target toxicity and biodegradability. Since several decades the role of drug delivery module has been a crucial parameter of research and clinical observations to improve the effectiveness of drugs. Biomaterials- natural or artificial are mainly used for medical application such as in therapeutics or in diagnostics. Among all the biomaterials, collagen based-hydrogels/ films/ composite materials have attracted the research and innovations and are the excellent objects for drug delivery, tissue engineering, wound dressings and gene therapeutics etc. due to high encapsulating capacity, mechanically strong swollen structural network and efficient mass transfer properties. Substantial developments have been performed using collagen-based drug delivery systems (DDS) to deliver biomolecules with better efficacy. In spite of significant progress, several issues at clinical trials particularly targeting of intracellular molecules such as genes is still a challenge for researchers. Experimental results, theoretical models, molecular simulations will boost the fabrication/designing of collage-based DDS, which further will enhance the understanding of controlled delivery/mechanism of therapeutics at specific targets for various disease treatments.",book:{id:"10945",title:"Collagen Biomaterials",coverURL:"https://cdn.intechopen.com/books/images_new/10945.jpg"},signatures:"Amit Kumar Verma"},{id:"82472",title:"Characterisation and Application of Nickel Cubic Boron Nitride Coating via Electroless Nickel Co-Deposition",slug:"characterisation-and-application-of-nickel-cubic-boron-nitride-coating-via-electroless-nickel-co-dep",totalDownloads:8,totalDimensionsCites:0,doi:"10.5772/intechopen.105364",abstract:"The chapter describes the characterisation and application of nickel cubic boron nitride (Ni-CBN) coatings using the electroless nickel co-deposition method. Two different types of substrates were used, that is, high-speed steel (HSS) and carbide. The characterisation of Ni-CBN coating was conducted using Field Emission Scanning Electron Microscope (FESEM) JSM-7800F coupled with Energy-Dispersive X-ray (EDX). As for the application, coated end mill cutting tools were inserted into DMU 50 CNC machine to conduct the machining testing. Cutting speed, feed rate, and depth of cut were chosen for the Taguchi L9 3-level factors. Taguchi analysis was employed to determine the optimal parameters for the Ni-CBN (HSS) surface finish. The ANOVA evaluation was used to identify the most significant effect on surface finish parameters. The FESEM images prove that the nano-CBN powders were embedded in the Ni-CBN coatings and are uniformly distributed. The findings show Ni-CBN-coated tool life is 195 minutes compared to the uncoated is 143 minutes. The surface roughness, Ra values using Ni-CBN-coated tools ranges between 0.251 and 0.787 μm, whereas the uncoated tools Ra values between 0.42 and 1.154 μm. It can be concluded that Ni-CBN HSS cutting tools reduce tool wear and extend tool life. The Taguchi optimum machining condition obtained is 1860 RPM spindle speed, 334 mm/min feed rate, and 2 mm depth of cut.",book:{id:"11762",title:"Characteristics and Applications of Boron",coverURL:"https://cdn.intechopen.com/books/images_new/11762.jpg"},signatures:"Norsilawati Ngah, Nor Bahiyah Baba, Nor Azinee Said, Mohd Habir Ibrahim and Na’ain Shari"},{id:"82482",title:"Boron-Based Cluster Modeling and Simulations: Application Point of View",slug:"boron-based-cluster-modeling-and-simulations-application-point-of-view",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.105828",abstract:"Among sub-nanometer clusters, boron-based clusters and their atom-doped counterparts have attracted great attention due to their mechanical, physical, and chemical properties as well as their applications. Molecular dynamics (MDs) simulations and ab initio methods, including density functional theory (DFT) calculations, have been used to understand the physical and chemical properties of different materials. Much research has recently been conducted by using various methods to determine the different properties of boron clusters. In this chapter, we briefly introduce the relevant modeling and simulation methods, then review very recent theoretical researches on the application of small boron clusters, such as gas sensors, electrodes, H2 storage, drug delivery, and catalytic applications.",book:{id:"11762",title:"Characteristics and Applications of Boron",coverURL:"https://cdn.intechopen.com/books/images_new/11762.jpg"},signatures:"Nasim Hassani, Mohammad Reza Hassani and Mehdi Neek-Amal"},{id:"82145",title:"Slope Casting Process: A Review",slug:"slope-casting-process-a-review",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.102742",abstract:"Semi solid processing is a near net shape casting process and one of the promising techniques to obtain dendritic free structure of metals. Semi solid casting gives numerous advantages than solid processing and liquid processing. Semi solid casting process gives, Laminar flow filling of die without turbulence, Lower metal temperature, Less shrinkage, Less porosity, Higher mechanical properties. Semi solid casting process is industrially successful, producing a variety of products with good quality. Slope Casting process is a simple technique to produce semi solid feed-stoke with globular microstructure and dendrite free structure castings. Slope casting process depends on different process parameters like slope length, slope angle, pouring temperature etc. The present study mainly focuses on review of various explorations made by researchers with different process parameters of the Slope casting process and explain the mechanisms that lead to microstructural changes which leads to good mechanical properties.",book:{id:"11119",title:"Casting Processes",coverURL:"https://cdn.intechopen.com/books/images_new/11119.jpg"},signatures:"Mukkollu Sambasiva Rao and Amitesh Kumar"},{id:"82363",title:"High Entropy Thin Films by Magnetron Sputtering: Deposition, Properties and Applications",slug:"high-entropy-thin-films-by-magnetron-sputtering-deposition-properties-and-applications",totalDownloads:9,totalDimensionsCites:0,doi:"10.5772/intechopen.105189",abstract:"Surface coating is of a great interest to increase the performances of the materials and extend its lifetime. High entropy films (HEFs) become the hot spot for developing surface engineering applications due to their good performances. They are reported to have superior properties such as good corrosion, wear resistance and excellent high temperature oxidation. Various deposition techniques have been exploited to fabricate HEFs such as laser cladding, spraying, sputter deposition and electrochemical deposition. These techniques are known to be an easy process to achieve a rapid quenching. Magnetron sputtering is seen as the most efficient methods to deposit the HEFs. Different gas can be used to prepare the ceramic materials. Besides, the deposition parameters reveal a strong influence on the physicochemical properties of HEFs. Working pressure, substrate temperature, bias voltage and gas mixture flow ratios have been reported to influence the morphology, microstructure, and functional properties of HEFs. The chapter overviews the development of the recent HEFs prepared by magnetron sputtering technique. First, it describes the principal of the technique. Then, it reports the classes of HEFs followed by the effect of the deposition parameters on their different properties. 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Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. 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Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. 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Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"15",type:"subseries",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11411,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,series:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983"},editorialBoard:[{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",institutionString:null,institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}},{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",slug:"azhar-rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",slug:"sergey-sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},onlineFirstChapters:{paginationCount:17,paginationItems:[{id:"82184",title:"Biological Sensing Using Infrared SPR Devices Based on ZnO",doi:"10.5772/intechopen.104562",signatures:"Hiroaki Matsui",slug:"biological-sensing-using-infrared-spr-devices-based-on-zno",totalDownloads:4,totalCrossrefCites:0,totalDimensionsCites:0,authors:[{name:"Hiroaki",surname:"Matsui"}],book:{title:"Biosignal Processing",coverURL:"https://cdn.intechopen.com/books/images_new/11153.jpg",subseries:{id:"7",title:"Bioinformatics and Medical Informatics"}}},{id:"82122",title:"Recent Advances in Biosensing in Tissue Engineering and Regenerative Medicine",doi:"10.5772/intechopen.104922",signatures:"Alma T. 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Thus all studies on metabolism will be considered for publication.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation"},{id:"18",title:"Proteomics",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"July 5th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:320,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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