\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"6973",leadTitle:null,fullTitle:"Advanced Evapotranspiration Methods and Applications",title:"Advanced Evapotranspiration Methods and Applications",subtitle:null,reviewType:"peer-reviewed",abstract:"Since the beginning of its formation approximately three billion years ago, the hydrosphere - as an envelope of the terrestrial ellipsoid - has remained constant from a quantitative point of view. The hydrosphere modifies only the ratio of the stretches of the planetary ocean and land, including the proportion of the states of water aggregation: gaseous, liquid, and solid.The hydrological cycle transports only a portion of the hydrosphere, repeats itself annually, and presents itself as a huge planetary plant that for billions of years has operated uninterruptedly on the basis of solar energy and gravity, providing freshwater resources for the maintenance and perpetuation of life beyond the planetary ocean.Water resources are highly influenced by the hydrologic cycle and play a role in agricultural economic development. However, as is shown by the Intergovernmental Panel on Climate Change report, the phenomena of changing climate and land use are set to exacerbate an already serious situation of water supply for various users. In this context, scientific investigations into the issue of the sustainable use of water are timely and important. Improvement of water management involves the accurate estimation of consumptive uses. The purpose of this book is to show the achievements of scientists and academicians all over the world in promoting and sharing new issues on various topics related to evapotranspiration.",isbn:"978-1-78985-812-9",printIsbn:"978-1-78985-811-2",pdfIsbn:"978-1-83962-110-9",doi:"10.5772/intechopen.73720",price:119,priceEur:129,priceUsd:155,slug:"advanced-evapotranspiration-methods-and-applications",numberOfPages:140,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"7c54751778dc2ff4a19cd84f1bf0c706",bookSignature:"Daniel Bucur",publishedDate:"April 3rd 2019",coverURL:"https://cdn.intechopen.com/books/images_new/6973.jpg",numberOfDownloads:6496,numberOfWosCitations:7,numberOfCrossrefCitations:9,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:26,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:42,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 9th 2018",dateEndSecondStepPublish:"June 18th 2018",dateEndThirdStepPublish:"August 17th 2018",dateEndFourthStepPublish:"November 5th 2018",dateEndFifthStepPublish:"January 4th 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"50794",title:"Prof.",name:"Daniel",middleName:"G",surname:"Bucur",slug:"daniel-bucur",fullName:"Daniel Bucur",profilePictureURL:"https://mts.intechopen.com/storage/users/50794/images/system/50794.jfif",biography:"Daniel Bucur is currently a professor of Land Improvement at the Pedotechnics Department, University of Applied Life Sciences and Environment in Iasi, Romania.\r\nHe completed his doctorate at the Technical University of Iasi in 1998.\r\nHis major research areas include water excess removal, irrigation, soil erosion control, climate changes, and sustainable land management. In recent years, he has been in charge of many national and international research projects, including Soil Erosion and Conservation Measures, Effect of Sewage Sludge Application on Quality Indices of Soil Vulnerable to Degradation, Sustainable Development of Soil Resources from the Areas with Drainage Works, and Impact of the Hydro-climatic and Pedo-geomorphological Risks on the Environment in Small Catchment.\r\nHe has published more than 150 papers in reviewed journals, 5 book chapters, and 9 books apart from more than 30 unreviewed papers and reports.",institutionString:"University of Applied Life Sciences and Environment in Iasi",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca",institutionURL:null,country:{name:"Romania"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"837",title:"Hydrology",slug:"hydrology"}],chapters:[{id:"63903",title:"Field-Scale Estimation of Evapotranspiration",doi:"10.5772/intechopen.80945",slug:"field-scale-estimation-of-evapotranspiration",totalDownloads:1149,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Evapotranspiration (ET) is a major component of the water cycle, which makes it an integral part of water resources management, especially in arid and semiarid environments. ET data are used for water management, irrigation scheduling, various modeling activities, and much more. Some areas of scarce water resources place limitations on water use, which are typically determined from various modeling approaches. As many models use ET as an input, or for validation, accurate ET data is essential to ensure accurate model outputs. In addition, most water management practices are done at the field scale; ET data of a similar scale is needed. Many ET measurement or estimation methods exist and vary widely in approach, instrumentation, complexity, and purpose. A lysimeter is considered the standard for ET measurement and is the most accurate. Other, more portable options are available, such as eddy covariance, scintillometer, Bowen ratio, and remote sensing, all capable of estimating actual field ET within approximately 30% of actual values. Although other methods may not be as accurate as a lysimeter, each has benefits in certain situations. Depending on the purpose, the level of accuracy may be suitable. ET estimation methods are constantly evolving, and accuracy should continually improve further.",signatures:"Jerry E. Moorhead",downloadPdfUrl:"/chapter/pdf-download/63903",previewPdfUrl:"/chapter/pdf-preview/63903",authors:[{id:"254304",title:"Dr.",name:"Jerry",surname:"Moorhead",slug:"jerry-moorhead",fullName:"Jerry Moorhead"}],corrections:null},{id:"64594",title:"Nonlinear Evapotranspiration Modeling Using Artificial Neural Networks",doi:"10.5772/intechopen.81369",slug:"nonlinear-evapotranspiration-modeling-using-artificial-neural-networks",totalDownloads:932,totalCrossrefCites:4,totalDimensionsCites:4,hasAltmetrics:0,abstract:"Reference evapotranspiration (ETo) is an important and one of the most difficult components of the hydrologic cycle to quantify accurately. Estimation/measurement of ETo is not simple as there are number of climatic parameters that can affect the process. There exists copious conventional (direct and indirect) and non conventional/soft computing (artificial neural networks, ANNs) methods for estimating ETo. Direct methods have the limitations of measurement errors, expensive, impracticality of acquiring point measurements for spatially variable locations, whereas the indirect methods have the limitations of unavailability of all necessary climate data and lack of generalizability (needs local calibration). In contrast to conventional methods, soft computing models can estimate ETo accurately with minimum climate data which have advantages over limitations of conventional ETo methods. This chapter reviews the application of ANN methods in estimating ETo accurately for 15 locations in India using six climatic variables as input. The performance of ANN models were compared with the multiple linear regression (MLR) models in terms of root mean squared error, coefficient of determination and ratio of average output and target ETo values. The results suggested that the ANN models performed better as compared to MLR for all locations.",signatures:"Sirisha Adamala",downloadPdfUrl:"/chapter/pdf-download/64594",previewPdfUrl:"/chapter/pdf-preview/64594",authors:[{id:"255750",title:"Dr.",name:"Sirisha",surname:"Adamala",slug:"sirisha-adamala",fullName:"Sirisha Adamala"}],corrections:null},{id:"64446",title:"Influence of Landsat Revisit Frequency on Time-Integration of Evapotranspiration for Agricultural Water Management",doi:"10.5772/intechopen.80946",slug:"influence-of-landsat-revisit-frequency-on-time-integration-of-evapotranspiration-for-agricultural-wa",totalDownloads:1006,totalCrossrefCites:1,totalDimensionsCites:4,hasAltmetrics:1,abstract:"The objective of this study was to explore the improvement in accuracy of estimates for evapotranspiration (ET) over complete growing seasons and monthly periods, when more frequent Landsat imagery is made available. Conversely, we explored the reduction in accuracy in ET estimates when frequency of Landsat imagery was reduced. The study was implemented by conducting a series of METRIC applications for two Landsat WRS path overlap areas, one in southern Idaho (paths 39 and 40) during 2000, and a second one in Nebraska (paths 29 and 30) during 2002, years when two fully functioning satellites, Landsat 5 and Landsat 7, were in orbit. The results indicated that high frequency imagery provided by two satellites covering a WRS path overlap was more able to capture the impacts of rapid crop development and harvest, and evaporation associated by wetting events. That data set simulated a nominal four-day revisit time. Three-simulated 16-day revisit data sets created using a single Landsat series for a single path were unable to produce monthly and growing season ET due to the lack of sufficient number of images to even begin the time-integration process. This emphasizes the need to maintain two Landsat satellites in orbit and the high value of four-day revisit times. Limiting the data set to one path and two satellites (eight-day revisit) underestimated growing season ET accordingly by about 8% on average. Error in monthly ET was relatively high when image availability was limited to that for an eight-day revisit. This is due to the importance of timing of images to identify key inflection points in the ETrF curves and to capture special events such as wetting events from irrigation and rain or from water stress or cuttings, as in the case of forage crops. Results suggest that a four-day revisit time as represented by the full-run (run 1) of our analysis provides robustness in the development of time-integrated ET estimates over months and growing seasons, and is a valuable backstop for mitigation of clouded images over extended periods.",signatures:"Ricardo Trezza, Richard G. Allen, Ayse Kilic, Ian Ratcliffe\nand Masahiro Tasumi",downloadPdfUrl:"/chapter/pdf-download/64446",previewPdfUrl:"/chapter/pdf-preview/64446",authors:[{id:"62376",title:"Mr.",name:"Ian",surname:"Ratcliffe",slug:"ian-ratcliffe",fullName:"Ian Ratcliffe"},{id:"207175",title:"Prof.",name:"Ayse",surname:"Kilic",slug:"ayse-kilic",fullName:"Ayse Kilic"},{id:"256001",title:"Ph.D.",name:"Ricardo",surname:"Trezza",slug:"ricardo-trezza",fullName:"Ricardo Trezza"},{id:"271318",title:"Dr.",name:"Richard",surname:"G. Allen",slug:"richard-g.-allen",fullName:"Richard G. Allen"},{id:"271319",title:"Dr.",name:"Masahiro",surname:"Tasumi",slug:"masahiro-tasumi",fullName:"Masahiro Tasumi"}],corrections:null},{id:"64662",title:"An Advanced Evapotranspiration Method and Application",doi:"10.5772/intechopen.81047",slug:"an-advanced-evapotranspiration-method-and-application",totalDownloads:988,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Estimating evapotranspiration is an important component in the monitoring of agricultural and environmental systems. This chapter will focus on the developing evapotranspiration method using general meteorological data and Normalized Difference Vegetation Index (NDVI). The proposed model in this chapter will be refined by using both the complementary relationship and the Budyko framework. The relative evaporation parameter in the complementary relationship will be derived by using precipitation, potential evapotranspiration, and NDVI based on that the Budyko framework can support the complementary relationship. It is also important to determine whether the proposed model can compete and deliver accuracy similar to remote sending method in the aspect of application. The results in the first phase showed the proposed model could be a powerful methodology to estimate ET among the ground-based method. In the second phase, a nonlinear correction function was proposed to better describe the complementary relationship. We will also demonstrate that the use of ET is a better approach for drought estimations than considering reference ET. More importantly, the advantage of the proposed model is that it can comprehensively consider both effects of precipitation and vegetation information. Taken together, this chapter has extended our knowledge of ET to support water resource management.",signatures:"Homin Kim and Jagath J. Kaluarachchi",downloadPdfUrl:"/chapter/pdf-download/64662",previewPdfUrl:"/chapter/pdf-preview/64662",authors:[{id:"254043",title:"Dr.",name:"Homin",surname:"Kim",slug:"homin-kim",fullName:"Homin Kim"},{id:"254528",title:"Dr.",name:"Jagath",surname:"Kaluarachchi",slug:"jagath-kaluarachchi",fullName:"Jagath Kaluarachchi"}],corrections:null},{id:"65724",title:"Concept and Consequence of Evapotranspiration for Sustainable Crop Production in the Era of Climate Change",doi:"10.5772/intechopen.83707",slug:"concept-and-consequence-of-evapotranspiration-for-sustainable-crop-production-in-the-era-of-climate-",totalDownloads:1468,totalCrossrefCites:4,totalDimensionsCites:12,hasAltmetrics:0,abstract:"The chapter provides an inclusive information related to the adverse effect of climate change on sustainable crop production through understanding evaporation, transpiration as well as evapotranspiration. It is anticipated that water availability in arid and semi-arid regions across the world will decrease, due to lack of rainfall and increase the temperature which leads to increase in the dry areas. Since climate change will impact on soil water balance that leads to change in evaporation and plant transpiration. While, with the increasing temperature, lack of precipitation and soils water unavailability, crop production will likely to decrease through shortening the crop growth cycle. While soils with high water holding capacity and crop cultivars which are tolerant to adverse effect as well as the application of improved management strategies will be better to reduce the impact of drought. Similarly, if the irrigated areas will be expanded, the total crop production will be increased that ultimately lead to increase the food security of increasing population.",signatures:"Rajan Bhatt and Akbar Hossain",downloadPdfUrl:"/chapter/pdf-download/65724",previewPdfUrl:"/chapter/pdf-preview/65724",authors:[{id:"262523",title:"Dr.",name:"Rajan",surname:"Bhatt",slug:"rajan-bhatt",fullName:"Rajan Bhatt"},{id:"280755",title:"Dr.",name:"Akbar",surname:"Hossain",slug:"akbar-hossain",fullName:"Akbar Hossain"}],corrections:null},{id:"63629",title:"Evapotranspiration from Green Infrastructure: Benefit, Measurement, and Simulation",doi:"10.5772/intechopen.80910",slug:"evapotranspiration-from-green-infrastructure-benefit-measurement-and-simulation",totalDownloads:956,totalCrossrefCites:0,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Green infrastructure (GI) is a common solution for stormwater management in an urban environment, with attached environmental benefits like flood control, urban heat island relief, adaptations to climate change, biodiversity protection, air pollution reduction, and food production. Evapotranspiration (ET) controls the GI’s hydrologic performance and affects all related benefits. Essentially, ET constrains the turnover of moisture storage and determines the demand for supplemental irrigation and then the cost-effectiveness of a GI project. Considering the spatial heterogeneousness of an urban space and the GI’s multi-layer designs, the classic ET equations have challenges in representing the ET variations from GI units. The underperformance of the existing ET models is partly due to the lack of corresponding high-quality field observations for each GI type in various urban settings. This chapter, therefore, summarizes the current research progress and existing challenges regarding the benefit, measurement, and simulation of ET process from GI.",signatures:"Youcan Feng",downloadPdfUrl:"/chapter/pdf-download/63629",previewPdfUrl:"/chapter/pdf-preview/63629",authors:[{id:"254932",title:"Dr.",name:"Youcan",surname:"Feng",slug:"youcan-feng",fullName:"Youcan Feng"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"5209",title:"River Basin Management",subtitle:null,isOpenForSubmission:!1,hash:"09b5a27ccab9d67afa66f2f0a14fb1a4",slug:"river-basin-management",bookSignature:"Daniel Bucur",coverURL:"https://cdn.intechopen.com/books/images_new/5209.jpg",editedByType:"Edited by",editors:[{id:"50794",title:"Prof.",name:"Daniel",surname:"Bucur",slug:"daniel-bucur",fullName:"Daniel Bucur"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5860",title:"Current Perspective to Predict Actual Evapotranspiration",subtitle:null,isOpenForSubmission:!1,hash:"cfbe21ff67263a8b0346e4e79fef9ebd",slug:"current-perspective-to-predict-actual-evapotranspiration",bookSignature:"Daniel Bucur",coverURL:"https://cdn.intechopen.com/books/images_new/5860.jpg",editedByType:"Edited by",editors:[{id:"50794",title:"Prof.",name:"Daniel",surname:"Bucur",slug:"daniel-bucur",fullName:"Daniel Bucur"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"911",title:"Water Stress",subtitle:null,isOpenForSubmission:!1,hash:"639300ffd325d217a7b6ec2261ff26e0",slug:"water-stress",bookSignature:"Ismail Md. 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Transport lines of crude oil play an essential role in ensuring continuous supply of fuel, that is, providing flow assurance. As the maintenance cost of repairing and troubleshooting transport lines is very high, addressing issues related to flow assurance becomes critical in the petroleum industry. Crude oil consists of wax particles that are initially in the dissolved state and those get crystallized once the temperature of the pipe wall goes below certain temperature. The wax content in the crude oil is firstly in the dissolved form, and then it gets to precipitation and then gets crystallized causing accumulation across the pipe walls. This process is explained by molecular diffusion of wax particles toward the pipe wall when the temperature of the crude oil in bulk gets lower than wax appearance temperature (WAT) [1, 2, 3, 4, 5, 6, 7, 8].
\nWax deposition is a serious problem that causes reduction in the flow cross section, hence affecting flow assurance. In the subsea transport lines, the surrounding temperature drops very low which increases the crystallization, and wax deposition becomes more acute (as shown in \nFigure 1\n). With time, the crystallized wax particles get accumulated layer by layer and even can clog the pipe completely, which dramatically affect the maintenance work. Therefore, there are many methods used and studied by the industry as well research and development institutions in this direction to effectively find out the location of clog, to minimize the issue deposited wax, to remove the deposited layer of wax, and to predict the wax deposition inside the transport lines with time. All these efforts are taken to reduce wax deposition and mitigate in such a way that wax layer thickness can be predicted and addressed for maintenance once it reaches to a caution limit. Hence, predicting wax deposition can help in preventive maintenance and cost-effectiveness [9, 10, 11, 12, 13, 16].
\nSectional view of pipeline affected with acute wax deposition [
This book chapter will discuss methods which are used for wax clearance, prediction, and estimation. This book chapter will cover mainly four sections: introduction, wax deposition issues and solutions, wax estimation methods, and role of artificial intelligence in wax prediction. The first section introduces the basic theory behind the wax deposition as a process and explains the main factors that are affecting wax deposition. The second section discusses four different methods adopted to tackle the problems related to wax deposition. The four different methods, mechanical, thermal, chemical, and microbial methods, will be discussed highlighting general practice in the industry. Further, their advantages and limitations are added in the same section. The third section is consisting of broad discussion which includes comparison of direct and indirect measurement techniques. The direct techniques are highlighting information about the numerical wax deposition models used along with scientific measurement techniques. On the other hand, the indirect measurement techniques are discussed knowing the external probing and examining techniques that can provide information about wax layer deposition inside the pipe. Finally, the role of artificial intelligence is discussed with benefits associated with the use of mapping information and using fuzzy logic for effective wax prediction or in developing the existing wax numerical models. Lastly, a brief conclusion is provided to reflect recent literature and hot topics in this direction.
\nThe issue of wax accumulation is complex because many factors affect the wax deposition such as the wax concentration in crude oil, temperature of the surrounding, wax appearance temperature, pressure drop, viscosity of the oil, and bulk temperature of the oil. The main associated issues with wax deposition in crude oil transport lines are impact on flow assurance and a sudden clog that can lead to immediate actions of maintenance and repair. Deposited layer of wax can be observed as three sub-layers: the topmost layer is more granular and soft, the bottom layer is observed to have a strong bond with pipe wall and considered as close fitted layer, and the sandwiched layer in between the top and bottom has the mechanical impurities and high wax content. With time, the sedimented layers get hard and move from top layer stage to bottom layer stage, consequently reducing the effective flow cross section. The process of hardening of the bottom sedimented layer of wax is referred to as “aging.” Hence, it is crucial to understand that with time, the wax deposition can cause difficulty in the cleaning process [11, 12, 13, 16, 17, 18, 19, 20, 21, 22].
\nTo deal with wax deposition issues, conventionally in the industry, pigging process is used for cleaning the wax after inspecting externally or cleaning as a part of regular maintenance. In the pigging process (as shown in \nFigure 2\n), the deposited wax is scrapped by passing the pig device through the pipe such that its movement along the pipe causes its head to collect the deposited layer of wax. However, the pig device inserted has possibility to get stuck due to hard layer of wax or due to higher friction from the accumulated wax when cleaning longer distances. If the pig device gets stuck in between the pipe not near to inlet or outlet connections, then it becomes a hectic and complex issue to deal. In the following sections, common methods (mechanical, thermal, chemical, and microbial) are discussed in dealing with issues related wax deposition [16, 17, 18, 19, 20, 21, 22].
\nPigging process [
Mechanical removal of wax is considered as the oldest method used in the industry. This method includes the use of scrappers directly, use of scrappers in the tube, and use of “pig” device inside the pipe. Scrappers are used to scrap the tube wall and remove wax even when the well is under operation. Pipeline inspection gauge (pig) device is one of the broadly used old methods that have been used since a century in the industry [16, 17, 18, 19, 20, 21, 22].
\nBoth wax removal techniques are used for maintenance; however, they have disadvantages of plugging of perforation within the well when scrapping and when the pigging device gets stuck inside due to wax. All the mechanical methods are economical in comparison to other methods [16, 17, 18, 19, 20, 21, 22].
\nThermal methods are basically used to adhere to temperature medium or maintain temperature of crude oil for reducing wax buildup. Some of the common ways used are hot oiling or hot watering, cold flow, and surface coating. Using hot oil or hot water (temperature in range 65–105°C) pumping in the transport line, the deposited wax is melted. Using solid resin particles that are having melting point more than WAT in the oil facilitating slurry flow, the wax is prevented to deposit toward the wall. Using surface coating of thermal insulation material (like plastic), the wax inhibition is achieved shielding the drop of crude oil temperature. All these methods can be used; however, there is limitation of using hot oil or hot water depending on the heat capacity of the oil [16, 17, 18, 19, 20, 21, 22].
\nChemical methods are basically using chemical inhibitors, and these are added in the oil to reduce the wax deposition. These chemical wax inhibitors can be classified into three: detergents, dispersants, and wax crystal modifiers. Surface activators are the detergents and dispersants that sustain wax particles as suspended and dispersed such that reducing the wax particles to adhere to each other or the pipe walls or any solid surface. The surface activation agents also modify the solid surface of the pipe reducing shear and interaction of wax particles on the wall of the transport line. Other types of surfactants also modify the solubility by solubilizing nucleus and avert agglomeration of wax particles. Wax crystal modifiers are also referred to as pour point depressors because they allow the flow of oil at a minimum temperature at its own density and given conditions. Wax crystal modifiers have same structure as that of the wax particle, and they coprecipitate occupying on the crystal lattice of wax particle forming hydrocarbon chains. In this manner, they also act as encumber in the growth of wax crystals as they reduce the possibility of wax crystals to form 3D structures. All these wax inhibitors are effective but must be used before crude oil bulk temperature is above its WAT [16, 17, 18, 19, 20, 21, 22].
\nMicrobial method of treating wax is not common; however, it was found effective in few field testings. The action of bacterial culture is producing the biosurfactant which is reported to facilitate as wax inhibitor. The bacterial strains such as
Methods discussed earlier including mechanical, thermal, chemical, and microbial methods were to reduce and clean the wax deposited inside the transport lines. Those methods served as the final solution for cleaning. However, as a part of mitigation and carrying out preventive maintenance, there is a need to have methods that can serve for estimating the wax buildup. This section is focused toward discussion of methods that can help in wax estimation. The direct measurement techniques are discussed to show the benefits associated with information process and about the numerical wax deposition models used along with scientific measurement techniques. The indirect measurement techniques are discussed to show how nondestructive testing can provide information through external probing and examining externally to know about wax layer deposition inside the pipe. Hence, this section is critically for knowing how the estimation is carried out regarding the wax layer thickness inside the crude oil transport line. Further, these are significant in avoiding sudden shutdowns due to blockage or complete closure with wax inside the pipe which can result in immediate maintenance cost [23].
\nDirect measurement techniques help in estimating the deposited wax layer thickness based on numerical assessment of deposition models as mentioned in the literature. The information about the parameters that are added to the model dependent on wax properties are measured using scientific measurement techniques. In other words, the estimation of the deposited wax layer is through wax deposition model but coupled with output obtained from scientific measurement. This section explains various wax deposition models highlighted in the literature and pointing out the most suitable model based on the assessment. In addition, different scientific measurement techniques are explained with respect to capability of each technique based on properties measured [23].
\nMathematical modeling approach is widely adopted in order to predict and monitor wax deposition either through numerical estimation directly or using software that has back-end mathematical model. In this section, some of the main highlighted wax deposition models from the literature are discussed along with respective equations. Wax deposition models are developed based on consideration of assumptions and selection of parameters. Four main models are discussed in this book chapter: film mass transfer model (FMTM), equilibrium model (EM), Matzain’s model (MM), and Venkatesan’s model (VM) [24, 25, 26, 27, 28].
\nFMTM is developed based on the mass and heat transfer assumptions considering both transfers occur independently. EM is developed based on thermodynamic equilibrium along with consideration of concentration gradient in the model unlike FMTM. MM is a modified model of EM making it more effective by including the diffusion equation as empirical correlation as well as including the factors related to shear stripping and trapped oil factors. VM is developed mainly considering shear effect with two coefficients along with quantification of mass flux in the model [23, 29, 30, 31, 32, 33, 34, 35, 36].
\nAmong these four models, MM was found to be self-sufficient due to its fitting with experimental data. Also, it was due to consideration of oil entrapment and correlation of hear stripping effect in the model. The results obtained by testing MM model are shown in \nFigure 3\n [23, 29, 30, 31, 32, 33, 34, 35, 36].
\nMatzain’s model comparison to experimental results [
The mathematical equations governing with respect to all these four models are mentioned below [23, 29, 30, 31, 32, 33, 34, 35, 36]:
\nwhere \n
where \n
where \n
Scientific measurement techniques are coupled to wax deposition models practically because these techniques assist in providing information that is necessarily required as inputs for providing the output which is predicting the wax deposition thickness. Most dominating measurement techniques used in the industry for obtaining properties of crude oil samples are discussed here, which include near-infrared scattering (NIR), small-angle X-ray scattering (SAXS), X-ray diffraction (XRD), controlled stress rheometer (CSR), and cross-polarized microscope (CPM) [23].
\nNIR is using the property of light scattering considered in a colloidal solution and obtains the physical properties. The near-IR range wavelength (low IR wavelength) attenuation spectra provide accurate results for obtaining WAT. The measurement deviation of ±2.5°C is observed when comparing the results obtained by CPM. This technique is effective with high-resolution results analyzed in 55 nm size window. It is also applicable if the oil sample is almost opaque to find out the WAT through delineation of radiation attenuation [37].
\nInvestigations carried out studying SAXS help in obtaining the radii of gyration. This technique can be used to study different fractions of crude oil at different operation temperatures. X-ray scattering at small angle can have issues related to low intensity. SAXS experimental results can be compared to calculations of scattering length density using chemical composition. This technique is applicable to obtain the size from radius of gyration and power law exponents providing details about physical properties of the crude oil sample [38, 39, 40, 41, 42, 43, 44, 45].
\nThis diffraction technique provides information about the crystal size of the wax by scattering in the time domain. XRD can help in understanding the wax structure capturing the wax deposition and aging. When using XRD it is important to understand that crystal size can also affect the diffraction. When the size of the crystal is below 0.1 μm, broadening of the diffraction peaks can be observed, and this broadening is as twice of the given angle. However, when the size of the crystal is above 0.1 μm, the diffraction characterizes Darwin width the same as the given angle of diffraction. XRD is suitable for characterizing crude oil samples studying the solid-solid transitions; hence, this method is effective in determining the crystal structure. But XRD has limitations in understanding the liquid-solid equilibrium, that is, identifying the crystallization from liquid to solid [46, 47, 48, 49, 50].
\nThis technique utilizes the application of controlled stress on the sample with arrangement of parallel plate to obtain the strain exerted. In this manner, steady stress and steady deformation are obtained, and measurement of viscoelastic properties of the wax sample is achieved. For measurement, it is important to make sure that two parallel plates are set properly. The difficult part in measurement is that during measurement and when applying stress, it is crucial to make sure that the top layer does not slip. Slippage can affect the results, and when the wax weight percent is above 5%, slippage can be more prevailing when taking measurement [51, 52, 53, 54, 55, 56].
\nWhen analyzing the impact of cooling on the crude oil microstructure, CPM can be used. It can help in measuring WAT because cooling rate provided to the crude oil sample can be controlled and morphology can be observed with time. CPM provides information about the wax precipitation as wax appearance can be noticed with high resolution in small-size dimensions up to 0.5 μm. The volume of the sample stored for testing is very small, and CPM is sensitive to film thickness of sample which is dependent on the concentration of the sample [57, 58, 59, 60, 61, 62, 63, 64].
\nIndirect measurements here are referred to techniques which are evaluating the wax deposition experimentally by assessing physical quantities such as volume, temperature, pressure, electric capacitance, and ultrasonic signals. Change in volume is evaluated such that the resulted difference is the volume fraction of deposited wax. Similarly, the difference in pressure is also considered accounting for deposited wax. Both methods are intrusive, hence limiting its application in the industry. Therefore, nondestructive techniques are to interest which includes techniques that use temperature sensing, electrical capacitance measurement, and ultrasonic assessment [23]. Firstly, applying the temperature-based techniques, thermal sensing utilizes the heating pulse applied externally, and its transient response can assist in real-time assessment and monitoring of wax deposition [65, 66]. The investigation by [67] collected information about wax thickness inside the transport pipeline by observing the acoustic signals after providing the heat pulse externally. Signals obtained were Fourier transformed to observe frequency domain and extract information correlating to deposited wax layer thickness. Secondly, electrical capacitance measurement widely known in the literature as electric capacitance tomography (ECT) is effective in providing high-quality images by applying complex algorithms. ECT examined on the nonmetallic transport pipe experimentally showed that online monitoring of wax deposition can be achieved. Thirdly, ultrasonic measurement technique is also applied externally, and the information provided by the decaying time of ultrasonic signals can be correlated to deposited thickness of wax. Overall, many investigations are in the direction of exploring capabilities of ECT; however, few studies focused on nondestructive testing related to temperature-based prediction and related to ultrasonic decay time measurements [68, 69, 70].
\nThe trend of research and development in the oil and gas industry is shifting toward utilization of artificial intelligence (AI) algorithms and machine learning concepts. Based on the respective operating conditions, making the systems equipped with AI can enhance the decision-making capabilities. Some of the commonly used AI algorithms are evolutionary algorithm (EA), artificial neural network (ANN), swarm intelligence (SI), and fuzzy logic (FL). More than one AI algorithm can be applied if needed. Adaptive Neural Fuzzy Inference System (ANFIS) provided information about best has condensation ratios using ANN and FL assessment helped in continuous optimization of wax deposition model [23]. Among the recent studies, the work of [71] used statistical model considering the dependent and independent variables for wax deposition prediction. The dependent variable considered is viscosity of the crude oil, whereas the independent variable considered is pressure. By plotting pressure/viscosity versus pressure plot (as shown in \nFigure 4\n), the linear boundary limits were kept, and if the actual plot goes above the upper limit, it implicates high potential of wax deposition. The ANN model was developed based on backpropagation neural network (BNN). BNN uses two loops, a forward and a backward loop. The forward loop helps in processing the information inputs to outputs, whereas the backward loop does opposite from output to input. The backward loop processes information along with the weight error correction to take as input to forward loop. In the manner, the continuous operation of forward to backward and backward to forward loops, backpropagation algorithm gets trained. Hence, BNN is also referred as learning algorithm due its adjusting weights confined in the neural network.
\nPrediction of potential to wax deposition [
More concise modeling is observed from work of [72] which consisted of ANN mathematical model for predicting rate of wax deposition. After observing that deposition rate of wax experimentally to be nonlinear, Kolmogorov theorem was applied; it virtually approximates nonlinear function to linear using two-layer ANN with certain error limit. The mapping structure for predicting wax deposition rate is shown below in \nFigure 5\n. The input variables (viscosity, shear stress, temperature gradient, and concentration gradient) and output variable is wax deposition rate. Comparison of the results with determined set showed that linear regression model was having correlation of 0.78, whereas ANN model had 0.97.
\nStructure of ANN model for wax deposition rate prediction [
The work of [73] used ANFIS model to predict thickness of deposited layer of wax considering single-phase turbulent flow. Five-layered ANFIS model was considered consisting of input variable as Reynolds number, wax concentration (%), time, temperatures (outside, inside, and pipe wall), and temperature-driven force (ratio of gradient temperature wall and outside to bulk temperature of the oil). As shown in the \nFigure 6\n, the ANIFS model has five layers, and respective equations governing output of the model are mentioned below. The first-order fuzzy logic is applied using if/then rule. Considering if “
Structure of ANFIS model for wax thickness prediction [
In the first-layer equation representation, the combination can be calculated as its membership degree (
In the second-layer equation representation, it can be shown with product of the membership degrees:
\nIn the third-layer equation representation, the calculation of the weighted ratio from each variable with respect to total weight is
\nIn the fourth layer equation representation, the adaption is achieved at this layer identifying this layer as defuzzification layer, where the learning rule is applied on this layer (i.e., minimizing the error). The summation of the weight applied with function is a resultant referring to the output layer, which is the fifth layer:
\nThe prediction of the deposited thickness of wax using this model resulted in close agreement with experimental values. The mean square error values comparing to experimental results was to three digit accuracy (0.00077034) and high value of correlation (0.9858).
\nIn brief, this chapter explores different methods used in the industry and research for predicting and monitoring wax deposition. The information discussed introduces the process of wax deposition and wax deposition models as a theoretical background. Observing the recent literature, the role of artificial intelligence is discussed which is to serve in effective and precise prediction of wax deposition. Hence, artificial intelligence for application of nondestructive data collection assessment helps in developing the wax deposition models to incorporate the updated oil sample information periodically to ensure that the wax predictions are reliable.
\nThe authors would like to acknowledge the funding support provided by United Arab Emirates under the grant numbers 31N265 and 31R168.
\nThe authors have no conflict of interest.
The authors would like to thank the United Arab Emirates University for providing necessary research facilities.
\nFirst MOSFET measurements at liquid Helium temperature have been reported as soon as in late 1960s [1, 2, 3], leading to some remarkable discoveries like the integer quantum Hall effect [4]. Since then, many works have been published on the electrical characteristics of MOSFETS down to 4.2 K [5, 6, 7]. The interest of operating electronic circuits at cryogenic temperatures has been demonstrated a few decades ago, and relies on the performance improvement and/or on the necessity to have electronics in cryogenic environment [5, 6, 7]. With the emerging field of quantum computing, for which read-out and control electronics of the quantum bits (qubits) is required in the proximity of the qubit itself, the study of CMOS devices at low and very low temperature, well below 100 K, has received a renewed attention [8, 9, 10]. In particular, qubit control requires high-frequency and large-bandwidth signals, as well as low-power electronics to be compatible with the cooling power of modern refrigerators [11, 12, 13, 14]. Circuits fabricated from advanced nodes CMOS are good candidates to fulfill the specifications for quantum computing applications [15, 16, 17, 18].
Key advantages of operating at low temperatures include the better electrical performance of MOSFETs, with higher carrier drift velocity and so higher on-state drain current and transconductance, steeper subthreshold slope, lower leakage current [6, 19]. Some works have studied bulk MOSFETs operation at cryogenic temperature emphasizing in particular kink behavior and freeze-out effects in those devices [7, 15, 20, 21, 22, 23, 24]. Recently, outstanding characteristics have been demonstrated at 4.2 K on advanced CMOS technologies [19, 25, 26, 27], in particular for Fully Depleted Silicon-On-Insulator (FDSOI) [28, 29, 30, 31, 32]. Ultrathin film FDSOI devices (with typically silicon thickness less than 10 nm) are immune to kink effects [33], and freeze-out has finally little impact on the DC characteristics of MOSFETs in advanced technologies [34]. Apart from the performance itself of the circuits at these low temperatures, and the figures of merit for analog or digital applications, specific attention to power dissipation has to be brought as well, as the available cooling power is limited in cryostat, and depends of the different cooling stages (typically ≈1 W at 4 K and less than 1 mW below 100mk) [11].
In that context, FDSOI technology offers a significant advantage over other available technologies, as it allows designing low power electronics, threshold voltage tunability thanks to its back bias ability, and offers low variability due to the undoped channel [35]. Extensive electrical characterization of advanced CMOS devices at deep cryogenic operation, including device electrostatics, carrier transport, mismatch and variability, or self-heating, is thus seriously needed.
Numerical issues appears with the modeling and simulation of MOSFETs at cryogenic and deep–cryogenic temperatures, in particular due to energy kBT approaching zero in equations and the extremely small intrinsic carrier density [34, 36]. Besides these difficulties, accurate models must correctly include, among other things, the temperature dependence of the main electrical parameters, such as carrier mobility, saturation velocity, threshold voltage, …, as well as thermal effects [37, 38]. On the other hand, new physical phenomena appear as the device temperature decreases that need to be characterized and properly modeled [19].
Because these aspects are essential for the development of compact models and robust design tools, this chapter presents a review of recent results obtained on 28 nm FDSOI transistors operated down to deep cryogenic temperatures. More specifically, we first discuss in Section 2 the major device electrical properties in terms of transfer characteristics and MOSFET parameters versus temperature. Then, we describe in Section 3 the self-heating phenomena, which could alter the FDSOI device performances. The matching and variability properties of scaled transistors limiting the analog applications are then addressed in Section 4. The development of compact model necessary for FDSOI circuit design at deep cryogenic temperatures is presented in Section 5. Finally, in Section 6, we illustrate the operation of elementary circuits at very low temperatures regarding inverter delay and oscillator frequency.
In this section, we present the measurement of the main electrical properties of FDSOI devices operating down to 4.2 K, such as the capacitance and charge control characteristics, the drain current Id(Vg) transfer curves as well as the main MOSFET parameters (threshold voltage Vth, subthreshold swing, mobility).
The measurements were performed on 28 nm FDSOI MOSFETs with silicon film thickness tsi = 7 nm and buried oxide (BOX) thickness tBOX = 25 nm from STMicroelectronics. NMOS and PMOS transistors were processed from (100) handle substrate, with <100> − oriented channel, and a high-κ/metal gate Gate-First architecture (Figure 1) [39]. Regular-Vth (RVT) and low-Vth (LVT) transistors are available through a doped back plane (NWELL or PWELL, with typically NA,D = 1018 cm−3) below the BOX. Thin (GO1, with equivalent oxide thickness EOT = 1.1 nm) and thick oxide (GO2, EOT = 3.2 nm) devices have been characterized using a cryogenic probe station down to 4.2 K.
Schematics of 28 nm FDSOI N- and PMOSFETs with regular-VTH (RVT) and low-VTH (LVT) flavors. Forward and reverse back biases (FBB and RBB) can be applied depending on the doping of the back plane.
The electrostatic charge control of FDSOI devices has been characterized by split C-V measurements with a conventional LCR meter. To this end, the gate-to-channel capacitance Cgc = dQi/dVg, with Qi the inversion charge in the channel, has been measured at 500 kHz frequency on large area N and P MOS devices as a function of the front gate voltage Vg with body bias Vb = 0 V for several temperatures down 4.2 K (Figure 2). As can be seen, the Cgc(Vg) curves are almost temperature independent above threshold, whereas a strong improvement of the turn-on behavior is obtained at low temperature, related to the subthreshold slope increase. These characteristics have been well reproduced by Poisson-Schrodinger simulations (see
Cgc(Vg) characteristics (solid lines) for N- and PMOS GO1 and GO2 devices from 300 K down to 4.2 K, at VB = 0 V. the Cgc(Vg) 1D-PS modeling is shown in symbols (frequency = 1 MHz, AC level = 40 mV, W = L = 9 μm). After Cardoso
The influence of the AC level (Vosc) of the LCR meter oscillator used during Cgc measurements at 4.2 K has been studied and is reported in Figure 3a. Indeed, due to the strong non linearity of the Qi(Vg) curves in subthreshold region at very low temperature, the turn-on behavior of the Cgc(Vg) curve below threshold is not well captured for a too large AC level (here 40 mV, currently used at T = 300 K). However, for an AC level of 1 mV, getting closer to the thermal voltage kBT/q at 4.2 K, where kB is the Boltzmann constant and q the magnitude of the electron charge, the turn-on behavior of Cgc(Vg) below threshold is well accounted for. These results can be well modeled by integrating the ideal Cgc(Vg) curve over one period of the AC signal, providing the measured capacitance Cgc,meas as follows [42]:
Experimental (a) and modeled (b) Cgc(Vg) characteristics for NMOS GO1 devices (W = L = 10 μm) at 4.2 K for two AC levels: 40 mV (red solid lines) and 1 mV (blue dashed lines).
where δVg(t) = Vosc.sin(2πt/Tp) is the AC signal of period Tp (Figure 3b).
The Id(Vg) transfer characteristics of same devices have been measured in linear region (Vd = 50 mV) for various temperatures and are shown in Figure 4. As usually observed in cryo-electronics for bulk CMOS devices [7], the drain current above threshold is highly increased due to mobility improvement of both electrons and holes, resulting from the suppression of phonon scattering. Similarly, the turn-on behavior of the curves below threshold is greatly improved as the temperature is lowered.
Id(Vg) characteristics for GO1 N and P MOS devices for various temperatures obtained in linear region (Vd = 50 mV).
The threshold voltage Vth of the devices has been extracted by the constant current method (
(a) Experimental Vth extracted on NMOS GO1 transistor (W = 1 μm, L = 24 nm) as a function of T at VDS = 50 mV and 0.9 V, and at Vb = 0 V and 1.4 V. (b) Modeled Vth vs. T for Vds = 50 mV and Vb = 0 and 1.4 V. After Cardoso et al. [
with
An important feature of FDSOI devices is the strong Vth control allowed by the back bias, which is not possible in FinFET and NW architectures, and very limited in bulk MOS devices [7], especially in forward biasing. Typical dependence of Vth with back bias are illustrated in Figure 6 for both P and N MOS FDSOI devices of various flavors and gate oxide thicknesses (GO1 and GO2), at T = 4.2 K and T = 300 K. As can be seen from this figure, it appears that the threshold voltage control with back biasing (ΔVth/ΔVb) is insensitive to temperature down to cryogenic conditions, and that Vth can be decreased to values close to zero volt. Interestingly, this makes it possible to operate the FDSOI devices at deep cryogenic temperatures with very small supply voltage (≈0.1–0.2 V), enabling low power dissipation.
Measurements of Vth vs. Vb for N- and P-type, RVT and LVT, GO1 (a) and GO2 (b) MOSFETs, at 300 K and 4.2 K, VDS = 50 mV. As T is decreased, Vb can be used to shift Vth back to its value at room temperature. After Cardoso et al. [
Another important parameter in FET operation is the so called subthreshold slope, S = dln(Id)/dVg, or its inverse the subthreshold swing SS, which characterizes the turn-on efficiency of the MOSFET below threshold. Typical subthreshold swing SS (mV/dec) variations with drain current in weak inversion region are shown in Figure 7a, revealing a plateau from which an average subthreshold swing can be extracted and plotted versus temperature (Figure 7b). Indeed, the subthreshold swing SS is varying linearly with temperature down to 25-30 K before plateauing around 10-20 mV/decade at deep cryogenic temperatures. The SS(T) linear behavior is usual for all FET devices and simply related to the Maxwell-Boltzmann statistics prevailing in weak inversion where SS = kT/q.(Cox + Cb + Cit)/Cox, Cit being the interface trap density capacitance [7]. The SS(T) plateau is generally attributed to the presence of an exponential tail of subband states, likely due to potential-fluctuations-induced disorder [46, 47, 48] and that minimizes the drain current turn-on efficiency at deep cryogenic temperatures.
Extracted subthreshold current
Finally, the effective carrier mobility μeff is investigated as being a driving parameter of MOSFET in linear region. In Figure 8a and b are illustrated typical mobility variations with inversion charge
Experiments and analytical model of μeff vs. Ninv for NMOS GO1 (a) and GO2 (b), varying T. power law exponent (c) vs. T for N and PMOS. After Cardoso
where μm stands for an amplitude mobility value close to the maximum one, θ1 and θ2 are the first and second order attenuation coefficients and n is a power law exponent varying between ≈2 and ≈3 as the temperature is changed from 300 K down to 4.2 K, as illustrated in Figure 8c. It should be noted that this mobility law
As was already mentioned, a specific feature of FDSOI devices is their operation in forward back biasing condition, enabling a significant lowering of the threshold voltage as illustrated in Figure 9a for T = 4.2 K. Interestingly, for sufficiently large Vb, the drain current measured at low Vd and very low temperatures (here T = 4.2 K) is increasing above back channel threshold before to decrease significantly and then to increase again well above front channel threshold. Actually, this decrease of the drain current just happens when the front channel is opening and has been attributed to a reduction of the mobility due to remote inter-subband scattering (IS) as well explained in [50]. To better understand this behavior, we have computed the drain current of the back channel after subtraction of the front channel component, taken as being the one in absence of back channel formation i.e. when Vb = 0 V (see Figure 9b). This assumption has been validated by Poisson-Schrodinger simulation (not shown here). Doing the same with Cgc(Vg) characteristics for various Vb’s, the inversion charge in the back channel has also been computed after integration of capacitance vs. Vg as is usual in split C-V technique (Figure 10a). As a result, note that the back channel charge is plateauing after the opening of front channel. The effective mobility in the back channel has been computed and plotted versus inversion charge density in the back channel or versus the front one as shown in Figure 10b and c. As can be seen, μeff first increases with the back channel inversion charge density before to decrease as the back channel charge saturates (Figure 10b). Instead, μeff in back channel decreases with the front channel inversion charge, which clearly indicates that the opening of the front channel is responsible for the back channel mobility decrease. This is precisely the signature of remote inter-subband scattering, which happens when carriers in the back interface 2D subband can interact with the front interface 2D subband. In this situation, some carriers at the back interface can experience scattering mechanisms in the front interface due to the overlap of the back and front subband wave functions. It should be mentioned that this phenomenon of inter-subband scattering is canceling out when the temperature is increased (T > 50 K) due to thermal broadening as well as when the drain voltage is increased due to the averaging over the channel of the conductance by integration over space [50].
a) Id(Vg) characteristics at 4.2 K for various Vb (= 0, 2 V, 4 V) and b) Back channel Id(Vg) curves after subtraction of front channel component also shown in green dashed line.
a) Ninv vs. Vg for Back channel for Vb = 2 V, 4 V. Green curve shows Ninv(Vg) for front channel at Vb = 0. b) Back channel μeff vs. back channel Ninv and c) Back channel μeff vs. front channel Ninv for Vb = 2 V, 4 V. Green curves show front channel μeff vs. front channel Ninv at Vb = 0 V.
In FDSOI devices or multi-gate field effect transistors like FinFETs and nanowire FETs, low thermally conductive materials such as the buried oxide (BOX) or the thin Si layer constituting the channel hinder the dissipation of the heat generated in the drain side. Consequently, the channel temperature can significantly rise when the device is in ON operation. This self-heating effect (SHE) can in turn severely affect the device performance, by reducing the carrier mobility, shifting the threshold voltage [51] or degrading the device reliability [52, 53], with implications to IC design. SHE has been widely studied for room temperature operation of circuits [54]. The thermal effects play a more fundamental role in cryogenic electronics – operating at various temperature stages with different available cooling powers –, as the temperature increase due to SH can be of the same order or even higher than the ambient temperature [55]. Furthermore, at very low temperature (well below 1 K), the cooling power drops down drastically (typically, 1 W at 1 K, 1 mW at 100mK) and thermal management thus becomes an additional constraint.
In this regard, the study of self-heating effects at cryogenic temperatures provides valuable information for performance optimization. In addition, to be accurate at cryogenic temperatures, models must take into account these thermal effects, as the device temperature can deviate significantly from the ambient one.
The experimental evaluation of self-heating was performed by using the conventional DC technique based on gate resistance thermometry [56]. In this method, the gate dielectric layer is thin enough to assume that the temperature of the channel is equal to that of the gate electrode. Inset of Figure 11 shows the typical 2-terminal gate structure that we used to measure the gate resistance RG. RG is measured between two contacts G1 and G2 using an LCR-meter. By varying the ambient temperature Tamb from 4.2 K up to 300 K, we record the change in the electrical gate resistance as a function of the input power P = IDS × VDS. The temperature increase ΔT is deduced from RG values at zero power (and so without SHE). Then the differential thermal resistance, RTH* = ∂ΔT/∂P|Tamb can be defined. This differential thermal resistance relates the change of ΔT due to a change in power dissipation P at a given Tamb [55].
(symbols) Differential thermal resistance RTH* measured as a function of the device temperature Tdevice = Tamb + ΔT from 450 K down to 4.2 K, with a corresponding numerical fitting curve (line).
In Figure 11 we have plotted the differential thermal resistance measured on an ultrathin film FDSOI transistor (tSi = 11 nm) as a function of the device temperature defined as Tdevice = Tamb + ΔT. All the RTH* data acquired for various ambient temperatures and dissipated power values merge into a single RTH* versus Tdevice curve, which thus provides a complete description of the temperature dependence of the thermal resistance for a given device. The thermal resistance depends mainly on the device geometry W and L, as well as on the BOX thickness, but not significantly on the Si film thickness in the 7 nm to 24 nm range typical of FDSOI devices (Figure 12) [55].
Thermal resistance RTH* versus device temperature, for wide and ultrathin FDSOI MOSFETs. After Triantopoulos
Our results show that in thin film devices, the thermal resistance RTH* of the device is strongly temperature dependent, especially at very low temperature, as illustrated in Figures 11 and 12. As the device temperature decreases from 300 K down to 4 K, RTH* is multiplied by 3 to 6. In FDSOI devices, the BOX tends to confine the heat in the channel, and therefore the total thermal resistance depends on both the thermal conductivity of Si and SiO2, which have different temperature dependence and magnitude (Figure 13). RTH* follows the temperature dependence of the inverse of the silicon dioxide thermal conductivity in the whole range of explored temperatures [57].
Thermal conductivity data versus temperature for bulk and Si-layer compared to that for bulk and SiO2-layer. After Triantopoulos
Besides considerations over the dominant thermal path in the device, the RTH* vs. Tdevice plot can be used into thermal model in order to reconstruct the channel temperature increase ΔT as a function of operating ambient temperature Tamb and input power P using the following expression,
Substituting a given analytical expression of RTH*(Tdevice) in Eq. (4) the value of ΔT at each Tamb and for each value of dissipated power can be calculated (Figure 14). This leads in particular to a nonlinear temperature increase of the device with the dissipated power. In this specific low temperature environment, the device temperature can significantly increase and thus highly deviate from the ambient temperature, depending on the applied gate and drain voltages, as illustrated in Figures 15 and 16.
Calculated channel temperature increase ΔT (line) as a function of the dissipated power P using
(a) IDS vs. VGS measured on NMOS at Tamb = 4.2 K and VDS = 0.9 V for different gate lengths, and (b) corresponding device temperature, Tdev vs. VGS.
(a) IDS vs. VDS measured at Tamb = 4.2 K on NMOS with L = 60 nm for different VGS values, and (b) corresponding Tdev. Vs. VDS.
The device mismatch is a key property to be known for the development of transistor compact models and the design of electronic circuits [58, 59, 60]. This section presents variability results obtained on FDSOI MOSFETs down to 4.2 K. To this end, an integrated on-chip matrix of individually addressable transistors has been used to increase the sample size statistics.
The measurements were performed on both N- and P-type transistors fabricated using the same 28 nm FDSOI technology as those described in
Id
Figure 17 shows typical drain current Id(Vg) characteristics for short channel N-type MOS transistors, at 300 K and at 4.2 K. Twenty four devices were measured for each MOS type at low drain voltage (|Vd| = 50 mV) to illustrate device variability. In Figure 18 the logarithmic scaled Id(Vg) emphasizes the subthreshold oscillation variability at low and high drain voltage (Vd = 50 mV and 0.9 V), at 4.2 K. The oscillations observed in the subthreshold current are a known signature of short channel MOSFETs operating at deep cryogenic temperatures, and could result from the presence of impurities in the channel [63, 64]. The threshold voltage was extracted following the constant current criterion, at Id = 10−7 W/L (A). Such current level represents the standard value used for Vth extraction, and it is well above the region where the oscillations are mainly identified, as highlighted in Figure 18 by a dashed line.
Id(Vg) curves for 24 short channel (L = 28 nm) N-type LVT MOSFETs at 4.2 K, at Vd = 50 mV and 0.9 V. After Cardoso et al. [
Figure 19 shows the Pelgrom plots of the standard deviation of ∆Vth, σΔVT, for NMOS devices (similar results have been obtained for PMOS). It can be seen that σΔVT well follows the area scaling linear dependence with respect to 1/
Pelgrom plot of threshold voltage variability σΔVT for NMOS at Vd = 50 mV and 0.9 V, 4.2 K (left) and 300 K (right). After Cardoso et al. [
Figure 20 shows the threshold voltage individual mismatch parameter, AΔVT = σΔVT
AΔVT versus 1/
The drain current variability, σ(ΔId/Id), has also been directly measured on the 28 nm FDSOI transistors studied here and their variations with gate voltage overdrive are shown in Figure 21 for 300 K and 4.2 K. As is usual, σ(ΔId/Id) is maximized below threshold before to decrease in strong inversion, where it might slightly increase again due to the contribution of access resistance Rs variability [66]. Actually, these variations can be very well fitted by the model of Eq. (5) developed for room temperature:
Measured and modeled σ(ΔId/Id) variations with Vgt = Vg-Vth. σΔRs varies from 0 to 8% of Rs = 377 Ω.μm (T = 300 K) and 266 Ω.μm (T = 4.2 K). After Cardoso
where gm is the transconductance and gd is the output conductance. In this model, the drain current variability is controlled by three matching parameters related respectively to the threshold voltage, σΔVT, the gain factor σ∆β/β (β = W/L.Cox.μ0, with μ0 being the low-field carrier mobility) and to the access resistance σ∆Rs. Typical matching parameters extracted from the drain current modeling, as well as their respective contributions are summarized in Figure 22. It indicates that there is a slight degradation of variability at low temperature and that the matching is mainly dominated by threshold voltage variability in weak inversion and by gain factor and access resistance mismatch at strong inversion.
Summary of matching performance and respective parameter contributions at 300 K (RT) and 4.2 K (LT) for NMOS (W = 1.39 μm, L = 28 nm). After Cardoso et al. [
In previous sections, we focus our efforts on understanding individual device physics and variability at cryogenic temperature. In this section, we present typical Poisson-Schrodinger simulation results for the capacitance and charge control in FDSOI structures operated down to deep-cryogenic temperatures and their application for building up an analytical compact model for charge and drain current in FDSOI MOSFET including back biasing effect.
Poisson-Schrodinger (PS) simulations were conducted after solving self-consistently the Schrodinger and Poisson equations given below:
with H the Hamiltonian, E the system energy,
where kBT is the thermal energy, F0 is the zero-order Fermi-Dirac integral function, Ef the Fermi level, Ei,j the subband energy,
It should be noted that in order to compute the PS equations down to very low temperature (1 K), special truncation caution has been taken to avoid numerical overload in the F0 Fermi integral function accounting for Fermi-Dirac statistics. PS simulations were also possible at 0 K by replacing the F0 Fermi-Dirac integral function by a Heaviside function, thus mimicking the fully degenerate metallic statistics.
The 1D FDSOI structure used for PS simulation is depicted in Figure 23, showing the band diagram across the stack and typical electron density profile in the channel obtained at T = 4 K for a given bias condition.
Typical band diagram and electron distribution from PS simulation for a FDSOI structure (Vg = 1 V, tox = 1 nm, tbox = 25 nm, tsi = 7 nm, Vb = 0 V, T = 4 K). After Aouad et al. [
Figure 24 demonstrates the variations of the inversion charge Qi in the Si film as a function of front gate voltage Vg with Vb = +3 V, obtained from PS simulations for various temperatures between 0 K and 60 K. A strong increase of the subthreshold slope with temperature dropping can be noticed, reaching infinity at T = 0 K, which is an interesting feature for transistors operating at such low temperatures.
Inversion charge Qi(Vg) calculated for different temperatures (tox = 1 nm, tbox = 25 nm, tsi = 7 nm, Vb = +3 V). After Aouad et al. [
Figure 25 shows the inversion charge control by field effect through the variations of the gate-to-channel capacitance Cgc(Vg) = dQi/dVg with front gate voltage for various back gate biases Vb. The onset of the back inversion channel for Vb = +3 V is evidenced by an additional plateau in the Cgc(Vg) curve, followed by the front channel opening. This effect clearly demonstrates the capacitive coupling, through the silicon channel, between the front gate and the back channel inversion layer, which leads to a lower capacitance.
Cgc(Vg) curves for different back biases Vb (tox = 1 nm, tbox = 25 nm, tsi = 10 nm, T = 4 K). After Aouad et al. [
The impact of temperature on the Cgc(Vg) characteristics is shown in Figure 26, clearly revealing the rounding of the curves with temperature rise above T = 10 K.
Cgc(Vg) curves for different temperatures (tox = 1 nm, tbox = 25 nm, tsi = 10 nm, Vb = +3 V). After Aouad et al. [
Following the PS simulation results, an analytical model has been established considering that front and back channel charges can be evaluated separately at each interface within a single subband approximation with energy level of a triangular potential well [41]. The coupling between the front and back channels is realized owing to the silicon channel capacitance Csi and the charge sheet approximation with Fermi-Dirac statistics.
In this case, the charge conservation equations at front and back interfaces are expressed by:
where the front and back interface 2D charge densities read,
where Vs1 (Vs2) is the front (back) interface surface potential, Cox (Cbox) the front (back) oxide capacitance, Csi the silicon film capacitance. The front and back electric field are given by:
with the Airy subband potential shift
Typical Qi(Vg) and Cgc(Vg) characteristics obtained by this Airy-based analytical model are presented in Figures 27 and 28, along with the PS simulation results. As can be seen, the compact model provides a good agreement with PS data, emphasizing its physical consistency in terms of charge and capacitance.
Qi(Vg) curves obtained from PS simulations (solid lines) and analytical modeling (dashed lines) for various Vb = −3, 0, +3 V (T = 4 K, tox = 1 nm, tbox = 25 nm, tsi = 10 nm). After Aouad et al. [
Cgc(Vg) curves obtained from PS simulations (solid lines) and analytical modeling (dashed lines) for various parameters Vb = −3, 0, +3 V (T = 4 K, tox = 1 nm, tbox = 25 nm, tsi = 10 nm). After Aouad et al. [
The total drain current in the channel can then computed, within the gradual channel approximation, by integrating the channel conductance between source and drain for the front and back channel and by adding their contribution as:
where Uc is the quasi Fermi level shift between source and drain common to both channels, Qi1,2 = q.Ninv1,2 are the front and back inversion charges obtained from Eq. (11) and μeff1,2 are the front and back channel effective mobility evaluated separately using Eq. (3). In absence of inter-subband scattering, the drain current calculated using Eqs. (3) and (14) does not exhibit a decrease for Vb = 4 V when the front channel is opening, in contrast to the experimental results discussed in
Drain current vs. front gate voltage Vg: Experimental (red solid line) and modeled with IS (dashed blue line) and modeled without IS (green dashed line) for Vb = 4 V and 0 V at T = 4.2 K.
Experimental (red solid line) and modeled (dashed blue line) back channel mobility μeff vs. front channel inversion charge density Ninv1 for Vb = 4 V at T = 4.2 K. model parameters: A = 0.45, b = 0.55 and c = 1.5 × 1012/cm2.
Although operational cryo-CMOS circuits have been demonstrated down to 30 mK [17, 30, 68, 69, 70], unfortunately no mature models are yet available to accurately predict the behavior of passive and active devices at cryogenic temperatures [71, 72]. Due to this lack of compact models at cryogenic temperatures, designers are faced to a blind-design procedure, which reduces the optimization of cryogenic integrated circuits [12, 30, 32, 58, 73, 74, 75]. Using the extensive electrical characterizations of single FDSOI transistors at cryogenic temperatures, it is however possible to already design efficient circuits.
Among them oscillators are essential building blocks in many digital and analog circuits. They are required for example to generate a clock signal in the control circuit of quantum computers [30, 76], and so must be also efficient at cryogenic temperature. Here we have electrically characterized ring oscillator (RO) fabricated from 28 nm-FDSOI technology [30, 77]. Figure 31a shows the delay per stage of a 101-stages RO as a function of temperature from 300 K down to 4.2 K. Without any back-biases applied on the MOSFETs composing the inverter stages, decreasing the temperature results in slowing down the RO. This can be explained by the threshold voltage shift at cryogenic temperature, which leads to a decrease of the effective current evaluated from the single characteristics of NMOS and PMOS transistors.
(a) Delay per stage versus temperature of a 101-stages RO (L = 34 nm, WNMOS = 420 nm, WPMOS = 600 nm) for different supply voltages VDD = 0.8, 1, and 1.2 V showing the RO slowing down due to the increase of VTH at low temperature. (b) Delay per stage versus temperature for VDD = 0.8, 1, and 1.2 V in the case of compensated VTH. The RO speeds up at low temperature due to the carrier mobility enhancement (from Bohuslavskyi et al. [
The effective drive current IEFF, which is a measure of the current drive of the MOSFET during switching and correlates well to circuit delay, can be defined for a single inverter as [78],
with
where
Figure 32a shows the evolution of IEFF as a function of temperature, in the case where no VBG is applied. We observed that IEFF decreases with temperature, and this decrease is stronger as VDD is decreased (3 decades degradation from 300 K to 4.2 K for VDD = 0.8 V). This IEFF variation is linked with the temperature dependence of IDS-VGS curves. A zero-temperature coefficient point (ZTC), corresponding to a gate voltage for which the drain current exhibits no temperature dependence, is systematically observed on the measured IDS vs. VGS curves, as illustrated in Figure 33 and already evidenced in Figures 4 and 17 [79]. For |VGS| < |VZTC| the drain current decreases as T decreases (∂IDS/∂T|VGS = cte > 0), whereas for |VGS| > |VZTC| the drain current exhibits an opposite temperature behavior (∂IDS/∂T|VGS = cte < 0).
(a) Effective current IEFF measured on single NMOS and PMOS transistors (L = 34 nm, WNMOS = 210 nm, WPMOS = 300 nm) for different supply voltages VDD = 0.8, 1, and 1.2 V; the effective current decreases as the temperature is reduced. (b) IEFF versus temperature for VDD = 0.8, 1, and 1.2 V in the case of compensated VTH; in that case the effective current increases as the temperature is reduced (from Bohuslavskyi et al. [
(a) Drain current IDS measured on single NMOS and PMOS transistors (L = 30 nm, WNMOS=WPMOS = 210 nm) as a function of gate voltage VGS for different temperature from 300 K down to 4.2 K. a zero temperature coefficient (ZTC) point for which the drain current (IDS) is independent of the temperature is evidenced for NMOS and PMOS.
It is worth noticing that for the pMOS the ZTC point is located at higher |VGS| (≈1.1 V) compared to the nMOS devices (≈0.7 V). The IEFF temperature dependence is mainly driven by the region with positive T-dependence ∂ IDS/∂T|VGS = cte,
If a back bias VBG is applied, it is possible to shift the threshold voltage back to its room temperature value (Figure 6). In that configuration, the drain current IDS increases with the temperature decrease whatever VGS and VDS values, due to mobility and saturation velocity improvement with T decrease at a given |VGS-VTH| overdrive gate voltage (see
Oscillating frequency as a function of VCO voltage for a VCO RO (L = 28 nm). Forward Back-biasing increases maximal frequency (from Guevel et al. [
Power as a function of supply voltage VDD and back bias voltage VBGRO (L = 28 nm). Forward back-biasing decreases power for same frequency (from Guevel et al. [
A review of recent results obtained on 28 nm FDSOI transistors operated down to deep cryogenic temperatures has been presented. First, the main device electrical properties in terms of gate capacitance and charge control and drain current transfer characteristics have been discussed along with the temperature dependence of the major MOSFET parameters (threshold voltage, subthreshold swing and mobility). Then, the self-heating phenomena were characterized in details, providing valuable information about the actual device temperature versus power dissipation, as well as the thermal resistance that limits the heat dissipation in the FDSOI structure, especially at low temperature. The matching properties have then been studied owing to threshold voltage and drain current statistical variability analysis, revealing that the mismatch in FDSOI transistors only increases of about 30–40% at deep-cryogenic temperatures. Besides, Poisson-Schrodinger simulations have been carried out with success down to zero Kelvin, giving access to valuable information about the gate charge control in FDSOI structures versus temperature, and, providing physical insight to the development of compact model mandatory for FDSOI circuit design at deep cryogenic temperatures. Finally, the operation of elementary circuits such as ring oscillators and voltage controlled oscillators has been demonstrated in terms of inverter delay and clock frequency down to deep-cryogenic temperatures.
This work highlights the powerful advantage of FDSOI over bulk technology, led by the back biasing capability. It offers in particular an efficient way to manage power consumption and performance, thus mitigating thermal effects, which are crucial aspects in cryo-electronics.
This work was partially supported by the French Authorities within the frame of NANO2022 project, and by the ERC Synergy QuCube (Grant No. 810504 — QUCUBE — ERC-2018-SyG), and EU H2020 RIA project SEQUENCE (Grant No. 871764).
We believe financial barriers should not prevent researchers from publishing their findings. With the need to make scientific research more publicly available and support the benefits of Open Access, more and more institutions and funders are dedicating resources to assist faculty members and researchers cover Open Access Publishing Fees (OAPFs). In addition, IntechOpen provides several further options presented below, all of which are available to researchers, and could secure the financing of your Open Access publication.
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\\n\\nThe first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
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\\n\\nFor Authors who are unable to obtain funding from their institution or research funding bodies and still need help in covering publication costs, IntechOpen offers the possibility of applying for a Waiver.
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\\n\\nWhile providing support and advice to all our international Authors, waiver priority will be given to those Authors who reside in countries that are classified by the World Bank as low-income economies. In this way, we can help ensure that the scientific work being carried out can make an impact within the worldwide scientific community, no matter where an Author might live.
\\n\\nThe application process is open after your submitted manuscript has been accepted for publication. To apply, please fill out a Waiver Request Form and send it to your Author Service Manager. If you have an official letter from your university or institution showing that funds for your OA publication are unavailable, please attach that as well. The Waiver Request will normally be addressed within one week from the application date. All chapters that receive waivers or partial waivers will be designated as such online.
\\n\\nDownload Waiver Request Form
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\n\nThe first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
\n\nHowever, as Open Access becomes a more commonly used publishing option for the dissemination of scientific and scholarly content, in addition to institutions, there are a growing number of funders who allow the use of grants for covering OA publication costs, or have established separate funds for the same purpose.
\n\nPlease consult our Open Access Funding page to explore some of these funding opportunities and learn more about how you could finance your IntechOpen publication. Keep in mind that this list is not definitive, and while we are constantly updating and informing our Authors of new funding opportunities, we recommend that you always check with your institution first.
\n\nFor Authors who are unable to obtain funding from their institution or research funding bodies and still need help in covering publication costs, IntechOpen offers the possibility of applying for a Waiver.
\n\nOur mission is to support Authors in publishing their research and making an impact within the scientific community. Currently, 14% of Authors receive full waivers and 6% receive partial waivers.
\n\nWhile providing support and advice to all our international Authors, waiver priority will be given to those Authors who reside in countries that are classified by the World Bank as low-income economies. In this way, we can help ensure that the scientific work being carried out can make an impact within the worldwide scientific community, no matter where an Author might live.
\n\nThe application process is open after your submitted manuscript has been accepted for publication. To apply, please fill out a Waiver Request Form and send it to your Author Service Manager. If you have an official letter from your university or institution showing that funds for your OA publication are unavailable, please attach that as well. The Waiver Request will normally be addressed within one week from the application date. All chapters that receive waivers or partial waivers will be designated as such online.
\n\nDownload Waiver Request Form
\n\nFeel free to contact us at funders@intechopen.com if you have any questions about Funding options or our Waiver program. If you have already begun the process and require further assistance, please contact your Author Service Manager, who is there to assist you!
\n\nNote: All data represented above was collected by IntechOpen from 2013 to 2017.
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These interventions remain at the discretion of physicians, particularly surgeons, whose treatment decisions are influenced by several medical factors, unfortunately not without bias. The stigma associated with substance use disorder is prevalent, which leads to significant biases, even in the healthcare system. This bias is heightened when IVDU patients require repeat valve replacement surgeries for IE due to continued drug use. Patients who receive a valve replacement and continue to use illicit drugs intravenously often return to their medical providers, months to a few years later, with a reinfection of their bioprosthetic valve; such patients require additional surgeries which are at the center of many ethical discussions due to high mortality rates, for many complex medical and social reasons, associated with continuous chemical dependency after surgical interventions. This chapter examines the ethics of repeat heart valve replacement surgery for patients who are struggling with addiction. Considerations of justice, the fiduciary therapeutic relationship, and guiding ethical principles justify medically beneficial repeat heart valve replacement surgeries for IVDU patient populations.",book:{id:"6556",slug:"advanced-concepts-in-endocarditis",title:"Advanced Concepts in Endocarditis",fullTitle:"Advanced Concepts in Endocarditis"},signatures:"Julie M. Aultman, Emanuela Peshel, Cyril Harfouche and Michael S.\nFirstenberg",authors:[{id:"64343",title:"Dr.",name:"Michael S.",middleName:null,surname:"Firstenberg",slug:"michael-s.-firstenberg",fullName:"Michael S. Firstenberg"},{id:"227150",title:"Ms.",name:"Emanuela",middleName:null,surname:"Peshel",slug:"emanuela-peshel",fullName:"Emanuela Peshel"},{id:"229719",title:"Dr.",name:"Julie",middleName:"M.",surname:"Aultman",slug:"julie-aultman",fullName:"Julie Aultman"},{id:"232060",title:"Mr.",name:"Cyril",middleName:null,surname:"Harfouche",slug:"cyril-harfouche",fullName:"Cyril Harfouche"}]},{id:"43498",doi:"10.5772/54928",title:"Treatment of Coronary Artery Bypass Graft Failure",slug:"treatment-of-coronary-artery-bypass-graft-failure",totalDownloads:4819,totalCrossrefCites:4,totalDimensionsCites:7,abstract:null,book:{id:"3542",slug:"artery-bypass",title:"Artery Bypass",fullTitle:"Artery Bypass"},signatures:"M.A. Beijk and R.E. Harskamp",authors:[{id:"164896",title:"Dr.",name:"Marcel",middleName:"A.",surname:"Beijk",slug:"marcel-beijk",fullName:"Marcel Beijk"},{id:"165094",title:"Dr.",name:"Ralf",middleName:null,surname:"Harskamp",slug:"ralf-harskamp",fullName:"Ralf Harskamp"}]}],mostDownloadedChaptersLast30Days:[{id:"80213",title:"Evolution of Heart Transplantation Surgical Techniques",slug:"evolution-of-heart-transplantation-surgical-techniques",totalDownloads:277,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Organ transplantation has kindled the human imagination since the beginning of time. Prehistorically, transplantation appeared as mythological stories: from creatures with body parts from different species, the heart transplant between two Chinese soldiers by Pien Ch’iao, to the leg transplant by physician Saints Cosmas and Damian. By 19th century, the transplantation concept become possible by extensive contributions from scientists and clinicians whose works had taken generations. Although Alexis Carrel is known as the founding father of experimental organ transplantation, many legendary names had contributed to the experimental works of heart transplantation, including Guthrie, Mann, and Demikhov. The major contribution to experimental heart transplantation before the clinical era were made by a team lead by Richard Lower and Norman Shumway at Stanford University in the early 1960s. They played the vital role in developing experimental and clinical heart transplantation as it is known today. Using Shumway biatrial technique Christiaan Barnard started a new era of clinical heart transplantation, by performing the first in man human-to-human heart transplantation in 1967. The techniques of heart transplant have evolved since the first heart transplant. This chapter will summarize the techniques that have been used in clinical heart transplantation.",book:{id:"11236",slug:"heart-transplantation-new-insights-in-therapeutic-strategies",title:"Heart Transplantation",fullTitle:"Heart Transplantation - New Insights in Therapeutic Strategies"},signatures:"Samuel Jacob, Anthony N. Pham and Si M. Pham",authors:[{id:"439327",title:"Prof.",name:"Samuel",middleName:null,surname:"Jacob",slug:"samuel-jacob",fullName:"Samuel Jacob"},{id:"439329",title:"Prof.",name:"Si M.",middleName:null,surname:"Pham",slug:"si-m.-pham",fullName:"Si M. Pham"},{id:"451575",title:"Mr.",name:"Anthony N.",middleName:null,surname:"Pham",slug:"anthony-n.-pham",fullName:"Anthony N. Pham"}]},{id:"70032",title:"Coronary Artery Bypass Grafting: Surgical Anastomosis: Tips and Tricks",slug:"coronary-artery-bypass-grafting-surgical-anastomosis-tips-and-tricks",totalDownloads:1424,totalCrossrefCites:0,totalDimensionsCites:3,abstract:"The definite feature of coronary artery disease is the focal narrowing in the vascular endothelium, and this leads to the decrease in the flow of blood to the myocardium. Atherosclerotic plaque is the main lesion. These patients can present with chest pain (angina or myocardial infarction) and need further workup noninvasively and invasively for the management. The main reasons for myocardial revascularization can be: (1) relief from symptoms of myocardial ischemia; (2) reduce the risks of future mortality; (3) to treat or prevent morbidities such as myocardial infarction, arrhythmias, or heart failure. Coronary artery bypass grafting (CABG) is the surgical technique of cardiac revascularization. In 1910, Dr. Alexis Carrel described a series of canine experiments in which he devised means to treat CAD by creating a “complementary circulation” for the diseased native coronary arteries. No clinical translation occurred at the time, but he was awarded the Nobel Prize in Medicine. Experimental refinements of coronary arterial revascularization, including the use of internal thoracic artery (ITA) grafts, were later reported by Murray and colleagues, Demikhov, and Goetz and colleagues in the 1950s and early 1960s. Dr. Rene Favaloro performed his first coronary bypass operation in May 1967 with an interposed saphenous vein graft (SVG) and shortly thereafter used aortocoronary bypasses sutured proximally to the ascending aorta. The stenosed segment is bypassed using an arterial or venous graft. Left internal thoracic artery is the most commonly used artery, and long saphenous vein is the most commonly used vein for the coronary artery grafting to reestablish the blood flow to the compromised myocardium. This can be performed with or without the help of cardiopulmonary bypass machine and also with or without arresting the heart. These techniques are called as on-pump beating or on-pump arrested and off-pump beating coronary artery bypass grafting surgery. Distal and proximal anastomoses are usually performed in an end-to-side manner, but in the case of doing sequential grafting, side-to-side anastomosis is also performed proximal to the end-to-side anastomosis. In this chapter we are going to discuss the coronary artery bypass grafting tips and tricks in details.",book:{id:"9060",slug:"the-current-perspectives-on-coronary-artery-bypass-grafting",title:"The Current Perspectives on Coronary Artery Bypass Grafting",fullTitle:"The Current Perspectives on Coronary Artery Bypass Grafting"},signatures:"Mohd. Shahbaaz Khan",authors:[{id:"278633",title:"Dr.",name:"Mohd. Shahbaaz",middleName:null,surname:"Khan",slug:"mohd.-shahbaaz-khan",fullName:"Mohd. Shahbaaz Khan"}]},{id:"65984",title:"Low Flow Low Gradient Severe Aortic Stenosis: Diagnosis and Treatment",slug:"low-flow-low-gradient-severe-aortic-stenosis-diagnosis-and-treatment",totalDownloads:2301,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Approximately 40% of patients with aortic stenosis (AS) show discordant Doppler-echocardiographic parameters with aortic valve area (AVA) <1 cm2 and/or index iAVA <0.6 cm2/m2 (consistent with severe AS) and the mean gradient (MG) <40 mmHg, consistent with mild/moderate AS. Accurate diagnosis of true severe low flow low gradient AS versus pseudo-severe aortic stenosis is important for prognosis and optimal timing for intervention. Doppler echocardiography using intravenous low dose dobutamine challenge is widely used for differentiating pseudo-severe from true severe aortic stenosis. However, relying on echocardiography alone may have limitations in accurate diagnosis. Reliable diagnosis using echocardiography is dependent on multiple factors like the angle of interrogation of the aortic jet, the assumption that the LVOT area is circular in cross section, optimal echo windows, the presence of underlying subclinical coronary artery disease prior to dobutamine challenge etc. In this chapter, we describe non-invasive and invasive strategies to assess the aortic valve using dobutamine stress. Direct measurement of gradients across the aortic valve while estimating the change in cardiac output and aortic valve area with increments of dobutamine infusion dose is complementary, safe and useful when conventional echocardiography techniques are inconclusive. Finally, the chapter describes effective strategies of treatment for low gradient severe aortic stenosis, including the role for diagnostic balloon valvuloplasty, in the era of transcatheter valve replacement (TAVR).",book:{id:"8218",slug:"aortic-stenosis-current-perspectives",title:"Aortic Stenosis",fullTitle:"Aortic Stenosis - Current Perspectives"},signatures:"Faeez Mohamad Ali, Vindhya Wilson and Rajesh Nair",authors:[{id:"280651",title:"Dr.",name:"Rajesh",middleName:null,surname:"Nair",slug:"rajesh-nair",fullName:"Rajesh Nair"},{id:"280829",title:"Dr.",name:"Faeez",middleName:null,surname:"Mohamad Ali",slug:"faeez-mohamad-ali",fullName:"Faeez Mohamad Ali"},{id:"290351",title:"Dr.",name:"Vindhya",middleName:null,surname:"Wilson",slug:"vindhya-wilson",fullName:"Vindhya Wilson"}]},{id:"59547",title:"Left Ventricular Assist Device Infections",slug:"left-ventricular-assist-device-infections",totalDownloads:1480,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Left ventricular assist device (LVAD) infections are important causes of morbidity and mortality in patients who receive these mechanical circulatory supports as a bridge to transplantation (BTT) or as destination therapy (DT) (for individuals who are not candidates for cardiac transplant). Infections are more common among persons who received pulsatile flow LVADs as opposed to newer continuous flow (CF) devices. Other risk factors for infection include obesity, renal failure, depression and immunosuppression. An LVAD infection increases the risk of infections in persons who undergo cardiac transplantation. Infections include percutaneous site, driveline, pump pocket and pump/cannula infections; sepsis, bacteremia, mediastinitis and endocarditis. Diagnosis is achieved by monitoring LVAD flow parameters and observing typical clinical and laboratory manifestations of infection. Imaging such as PET-CT or SPECT-CT imaging can be helpful to establish a diagnosis of pump pocket infection. Echocardiography may aid in detecting native valve endocarditis and thrombus associated with the LVAD. The most common pathogens include Staphylococcus, Corynebacterium, Enterococcus, Pseudomonas and Candida spp. Treatment requires targeted antimicrobials plus surgical debridement of infected tissue and device components. In cases of pump/cannula/LVAD endocarditis, especially if fungal pathogens or Mycobacterium chimaera are involved, LVAD removal/reimplantation vs. transplant is necessary, combined with extended antimicrobial therapy.",book:{id:"6556",slug:"advanced-concepts-in-endocarditis",title:"Advanced Concepts in Endocarditis",fullTitle:"Advanced Concepts in Endocarditis"},signatures:"Marion J. Skalweit",authors:[{id:"186717",title:"Associate Prof.",name:"Marion",middleName:null,surname:"Skalweit",slug:"marion-skalweit",fullName:"Marion Skalweit"}]},{id:"60658",title:"Humoral Rejection in Cardiac Transplantation: Management of Antibody-Mediated Rejection",slug:"humoral-rejection-in-cardiac-transplantation-management-of-antibody-mediated-rejection",totalDownloads:1097,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"After a successful heart transplantation, fundamental keys to achieve good results in the long term are to establish immunosuppressive therapy in the postoperative period in an appropriate manner and to ensure continuity of follow-ups. Despite the fact that these stages are maintained perfectly, patients may face one or more rejection episodes. T-cell-mediated acute cellular rejection of the cardiac allograft has well-established treatment algorithms, whereas antibody-mediated rejection (AMR) is challenging to diagnose, and its treatment varies between centers. Investigators reported that AMR is among the most important factors to improving long-term outcomes. Improved understanding of the roles of acute and chronic AMR has evolved in recent years following a major progress in the technical ability to detect and quantify recipient antihuman leukocyte antigen (HLA) antibody production. Recently, a study of the immunobiology of B cells and plasma cells that pertains to allograft rejection and tolerance has emerged. There are some questions regarding the classification of AMR, the diagnostic approaches, and the treatment strategies for managing. In this chapter, we are discuss the effector mechanisms that are used by antibodies to eliminate antigens and clinical experience about AMR and its treatment with a discussion about the latest articles.",book:{id:"6558",slug:"heart-transplantation",title:"Heart Transplantation",fullTitle:"Heart Transplantation"},signatures:"Umit Kervan, Dogan Emre Sert and Nesrin Turan",authors:[{id:"227772",title:"Prof.",name:"Umit",middleName:null,surname:"Kervan",slug:"umit-kervan",fullName:"Umit Kervan"},{id:"243592",title:"Dr.",name:"Dogan Emre",middleName:null,surname:"Sert",slug:"dogan-emre-sert",fullName:"Dogan Emre Sert"},{id:"243593",title:"Dr.",name:"Nesrin",middleName:null,surname:"Turan",slug:"nesrin-turan",fullName:"Nesrin Turan"}]}],onlineFirstChaptersFilter:{topicId:"984",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:141,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:"Shenzhen Technology University",institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda R.",middleName:"R.",surname:"Gharieb",fullName:"Reda R. Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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Valarmathi",profilePictureURL:"https://mts.intechopen.com/storage/users/69697/images/system/69697.jpg",institutionString:"Religen Inc. | A Life Science Company, United States of America",institution:null},{id:"205081",title:"Dr.",name:"Marco",middleName:"Vinícius",surname:"Chaud",fullName:"Marco Chaud",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDGeQAO/Profile_Picture_1622624307737",institutionString:null,institution:{name:"Universidade de Sorocaba",institutionURL:null,country:{name:"Brazil"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/209393",hash:"",query:{},params:{id:"209393"},fullPath:"/profiles/209393",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()