Status of energy density in nanodielectric composite investigation
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",isbn:"978-1-80356-678-8",printIsbn:"978-1-80356-677-1",pdfIsbn:"978-1-80356-679-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"6dcb071a2e978694b6b1cb9c20afc1a3",bookSignature:"Prof. Hai-Zhi Song",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11494.jpg",keywords:"Electric Field Effect, Nano-Materials, Electric Field Design, Antenna, Microelectronics, Optoelectronics, Electric Field Stimulation, Brain and Nerve, Electric Field Imaging, Atomic Electric Field, Space Science, Climate",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 22nd 2022",dateEndSecondStepPublish:"May 26th 2022",dateEndThirdStepPublish:"July 25th 2022",dateEndFourthStepPublish:"October 13th 2022",dateEndFifthStepPublish:"December 12th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"a month",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"A pioneering researcher in the fields of new materials, optoelectronic devices, and quantum information processing, appointed vice director of the Science and Technology Committee of SWITP, author/co-author of more than 170 research papers, and holder of 40 patents.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"196114",title:"Prof.",name:"Hai-Zhi",middleName:null,surname:"Song",slug:"hai-zhi-song",fullName:"Hai-Zhi Song",profilePictureURL:"https://mts.intechopen.com/storage/users/196114/images/system/196114.jpg",biography:"Curriculum Vitae\n\nName: Hai-Zhi Song \nGender: male\nDate of Birth: Oct. 20, 1968\nPlace of Birth: Shanxi, China\nAffiliation and Address: \nSouthwest Institute of Technical Physics\nNo.7, Section 4, Renminnan Road, Chengdu 610041, China\nAnd\nInstitute of Fundamental and Frontier Sciences,\nUniversity of Electronic Science and Technology of China,\nNo. 4, Section 2, Jianshebei Road, Chengdu 610054, China\n\nWork Phone: +86-28-68180751, +86-28-83208728\nMobile Phone: +86-158-28239155\nFax: +86-28-83201896\nE-mail: hzsong1296@163.com, hzsong@uestc.edu.cn\n \nEducation \nSept, 1990 – July, 1995:Peking University, PhD, Thesis “Visible luminescence of porous silicon and its mechanism”, Researches on hydrogen-influenced Schottky diodes and silicon-based light-emitting materials. \nSept, 1986 – July, 1990:Nanjing University, Bachelor of Science, Thesis “Study of refractory metal silicides”, Research on Ohmic contact of semiconductors.\n\nWork Experience \nJuly, 1995 – Sept. 1997: Nanjing University, Nanjing, China, Postdoctoral Researcher, Research on silicon-based light-emitting materials. \nOct, 1997 – Sept. 1998: Catholic University Leuven, Leuven, Belgium, Visiting free Researcher, Research on amorphous semiconductors. \nOct, 1998 – Sept. 2001: Tsukuba University, Tsukuba, Japan, Assistant Professor, Research on semiconductor quantum dots. \nOct, 2001 – March 2012: Fujitsu Lab. Ltd., Atsugi, Japan, Researcher/Senior Researcher, Researches on Semiconductor Quantum Dots for Quantum Information, Semiconductor Optoelectronic Materials and Devices. \nApril, 2012 – March 2014: University of Tokyo, Tokyo, Japan, Senior Researcher, Researches on Quantum Information Processing Devices. \nApril, 2014 – now: Southwest Institute of Technical Physics, Chengdu, China, Professor, Researches on Semiconductor Optoelectronic Materials and Devices. \nJune, 2015 – now: University of Electronic Science and Technology, Chengdu, China, Professor, Researches on Nanoscaled Semiconductors and Quantum Information Processing Devices.\n \nAchievements\nSystematically studied the property of porous silicon materials and verified their mechanism; found green and ultraviolet luminescence, and clarified the multiple luminescence mechanisms of nanocrystalline-silicon embedded in SiO2, which is valuable to silicon-based optoelectronic integration; realized enhanced hole mobility in amorphous silicon, verified the existence of deep trap states in amorphous selenium, providing ways to improve amorphous optoelectronic materials. \nDiscovered lateral coupling between self-assembled quantum dots (QDs) and their tuning effect to 2D electron gas; illustrated and deeply explained the metal-insulator transition in 2D ordered QD arrays, all of which are worth in optoelectronic application of semiconductor QDs. \nDeveloped Sb-free technique to double the InAs/GaAs QD density and suppress the atomic interdiffusion, helped producing 1.3 um QD lasers, which won Japanese national prizes and had been merchandized; developed 1.06 um quantum-well lasers, which have been used to produce pure-green lasers robust against high temperature. \nFound a way to access buried QDs by scanning tunneling microscope; achieved a way to prepare diluted QDs by post-annealing and clarified its mechanisms; invented a technique to control the size and site of QDs by atomic-force microscopy lithography, and an apparatus to detect single electron spin states by optically-detected magnetic resonance; designed a few types of micropillar cavities applicable to realize 1.55 um highly-efficient, even coherent (strongly coupled) InAs/InP QD single photon sources; produced fiber-integrated photon-entangled sources, all of which are very useful to the applications of QDs in quantum information processing. \nDeveloped focal-plane single-photon avalanche detectors, providing central devices for 3D laser detecting and ranging system; explored antimonide middle- and long-wavelength infrared detectors and the surface plasmon enhancement effect in such detectors; advanced the acetone-sensing function of Eu-doped SnO2 nano-belt; found Nickle Phosphide serving as a good catalyst in hydrogen-producing. Realized a series of optoelectronic quantum devices for quantum information processing, such as fiber-integrated photon-pair-entangler, chiplet heralded single photon emitter, fiber quantum memories, quantum number generator, etc.\n\nHonor and Group Memberships \nSelected Scholar of the Recruitment Program of Global Experts, China\nEditorial member of “Laser Technology”\nEditorial member of “Journal of Electronic Science and Technology”\nEditorial member of “Internal J. Mat. Sci. Appl”\nMember of APS (American Physics Society)\nMember of OSA (Optical Society of America)\nPermanent Member of China Physical Science and Technology\nPermanent Member of the Chinese Optical Society\nTechnical committee member of PIERS, organizing a series of “quantum information processing and devices” sessions\nTechnical committee member of ICICM",institutionString:"Southwest University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Southwest University",institutionURL:null,country:{name:"China"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"20",title:"Physics",slug:"physics"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"453623",firstName:"Silvia",lastName:"Sabo",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/453623/images/20396_n.jpg",email:"silvia@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"8356",title:"Metastable, Spintronics Materials and Mechanics of Deformable Bodies",subtitle:"Recent Progress",isOpenForSubmission:!1,hash:"1550f1986ce9bcc0db87d407a8b47078",slug:"solid-state-physics-metastable-spintronics-materials-and-mechanics-of-deformable-bodies-recent-progress",bookSignature:"Subbarayan Sivasankaran, Pramoda Kumar Nayak and Ezgi Günay",coverURL:"https://cdn.intechopen.com/books/images_new/8356.jpg",editedByType:"Edited by",editors:[{id:"190989",title:"Dr.",name:"Subbarayan",surname:"Sivasankaran",slug:"subbarayan-sivasankaran",fullName:"Subbarayan Sivasankaran"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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Various DC-AC, AC-AC conversions are needed for solar and wind farms, while primary and secondary electrochemical devices are needed for transportation and telecommunication applications. In addition, military equipment and transport have been actively moving toward more electric systems. High energy density, power density and high temperature components are desired for applications such as active armor, electrochemical guns, directed energy weapons, more electric aircraft, electric launch platform, all electric warships, and so on (Sarjeant 1998, DARPA 2004). Passive components such as capacitors have been a limiting factor in full implementation of high energy density electrical systems. Figure 1 shows the development history of capacitor technology. Every generation of capacitors are primarily credited to the innovation and engineering of new dielectric materials. The advances of such a passive component and active components have provided great foundation for future power electronics and electric power.
Dielectric materials are categorized into organic polymers, inorganic ceramics, filled and unfilled polymeric thin films, ceramic films and nanodielectric composites. Polymer dielectric films show very high dielectric strength (>300kV/mm), lower dielectric loss (<0.01), and adequate mechanical flexibility. Single-phase polymers eliminate complexities arising from mixing and dispersion that are prevalent in the multi-phase systems, but, they are subject to low dielectric permittivity or constant (<4) and working temperature (<200C). Ceramic dielectrics tend to have very high dielectric permittivity (>100) but relatively low dielectric strength (<50kV/mm). Increasing their dielectric strength is one of the challenges and goals in modern day dielectrics research. The presence of grain boundaries, porosity, impurities, surface defects, and chemical deterioration causes ceramic dielectrics to fail at relatively low field stresses (<10kV/mm). In addition, many ceramic dielectrics have high dissipation factor (>0.01), high capacitance loss (>30%) under high voltage stress (>3kV/mm) and/or piezoelectric effect associated with the ferroelectric type ceramics. Thin film dielectrics are an important area leveraging either polymer or ceramic materials. The thin films are usually in nanometer to submicron in thickness with very high breakdown strength. But, they are primarily useful for low voltage and small size microelectronic application. Scalability, reliability, cost and power level are great concerns.
Evolution of capacitor technology has a close relationship with materials development (
Nanodielectric composites belong to a new type of materials engineered for improved functions, performance for dielectrics and electrical insulation (Lewis 1994). Certain ceramic materials can be selected to blend with polymers to provide synergy between the high breakdown strength polymer and high permittivity ceramic materials as shown in Figure 2. A number of research areas are being actively pursued to fully explore the advantages of the functional composites.
Relationship between breakdown strength and dielectric permittivity of various dielectric materials (
With properly selected nanoceramic fillers, dielectric properties of the polymers can be improved due to the absence of grain boundaries in ceramics. The chapter will review existing high performance polymer dielectrics and current nanodielectric engineering efforts. The effect of nanofiller type and size, distribution, particle-polymer interface and the host polymers will be discussed. A better understanding of the issues and fundamental mechanisms in nanodielectric engineering will lead to the successful development and implementation of high performance nanodielectrics.
An insulator is a material that resists the flow of electrons due to a full valence band and a large band gap between the valence band and the next higher state. A dielectric material is an electrical insulator that can be polarized by an applied electric field. The internal electric charges only slightly shift from their average equilibrium position resulting in dielectric polarization. Ceramics and polymer-based dielectrics are the most common materials of industrial interests, where the control of the storage and dissipation of electric and magnetic energy in these materials are of great interest. The following section discusses high performance dielectric materials with a focus on high temperature and high energy density capability.
Generally speaking, voltage stress, complex dielectric permittivity, breakdown strength, mechanical strength, thermal stability and conductivity are the primary interests for dielectric materials. In many cases, these properties are mutually dependent. For example, the rise in temperature in the core of the capacitor causes an increase in dielectric losses and partial discharge, which can lead to premature failure. Often the self-generated internal heat can overshadow the ambient temperature, which then can lead to a more rapid degeneration and earlier failure. To meet the needs of high temperature devices and equipment,, a number of high temperature ceramics and polymer dielectrics have received intensive investigations. This includes Polycarbonate (PC), Polyphenylene sulfide (PPS), Fluorene Polyester (FPE), Diamond like carbon (DLC), polyetherimide (PEI), Polyetheretherketone (PEEK), Polyimide (Kapton® PI), Polytetrafluoroethylene (Teflon®), AlN, AlON, TiO2, etc. For applications at or above 200C, very limited choice of polymers and ceramic materials are available. Table 1. shows a number of high performance polymer and thin film dielectric materials being of interest for capacitor applications (Tan et al 2005). High temperature polymer films such as fluoropolyester (FPE), polyetherimide (PEI), and polytetrafluoroethylene (PTFE) not only meet the 200C temperature requirement, but also have potential to be further engineered for higher performance. While stable to 260C, PTFE has poor mechanical stability, difficulty in metallization and lower breakdown strength (<400kV/mm). FPE films are restricted to low energy density due to low permittivity. PEI has been successfully melt extruded into 5µm film. A great combination of dielectric, thermal and mechanical properties offers a good polymer matrix for inorganic fillers.
Ceramic dielectrics are very good high temperature dielectric materials by nature. They can be categorized into linear and nonlinear dielectrics by polarization mechanism as shown in Figure 3. Linear dielectric ceramics have lower dielectric permittivity and relatively higher breakdown strength than nonlinear material. The nanostructured TiO2 is one example of the category, which has received intensive investigations due to the high energy density projection for capacitor applications [Chao et al 2010].
High temperature dielectric films for capacitor applications (
Dielectric material classifications in terms of their polarization mechanism (
Nonlinear dielectric ceramics exhibit a dipolar orientation mechanism resulting in a very high dielectric constant, but intend to fail easily at relatively low field stresses due to the grain boundary and inherent piezoelectric / electrostrictive effects. Ferroelectric and antiferroelectric materials are representative of this category. Their properties have been exploited by Sandia National Lab and other research labs to fabricate high energy density capacitors for neutron generators and power electronic inverter applications (Tuttle et al 2001, Campbell et al 2002). The hybridization of linear and nonlinear dielectric ceramics was also investigated by TRS Technologies, The Pennsylvania State University and Naval Research Laboratory (Hackenberger et al 2010, Gorzkowski et al 2007). Glass was added into ferroelectric ceramics to improve sintering behavior and to reach high density, however, the defects and microstructural control in the process created difficulties in reaching high breakdown strength and energy density.
In thin film dielectric approaches, ceramic films received great interest not only for their high breakdown strength appropriate for capacitor applications, but also their high dielectric permittivity needed for high frequency devices and memory applications. Among them are diamond-like carbon (DLC), aluminum oxynitride (AlON), stronium bismuth tantalate, and barium titanate, which are usually deposited on metal foil, Si wafers, or ceramics (Wu et al 2005, Bray et al 2006, Xu et al 1998, Kaufman et al 2010). The thin film technology is generally subjected to lower dielectric permittivity, low film quality in scale-up processes, processing difficulties and therefore low voltage applications.
Energy density of a dielectric material is defined by ε0 ε Ε2/2, where (0 is the permitivity of free space, ε is the permitivity of the dielectric material, and E is the breakdown strength of the dielectric material. Either raising breakdown strength or dielectric permittivity will result in the increase of the energy density. A survey of dielectric materials has revealed that the combination of high breakdown strength and permittivity are not commonly found in dielectric materials (Figure 4). There have been two different research approaches to increase energy density so far. Increasing the breakdown strength appears to be emphasized the most because of the square relationship in the above definition. However, a great deal of difficulties has occurred particularly in the scale-up of materials. An increasing trend of effort is to seek high dielectric permittivity materials while maintaining breakdown strength of the dielectrics. This effort also finds similarity in other applications such as transistor gate, non-volatile ferroelectric memory, integral capacitors, and transmission lines.
Figure 5 shows the trend of increasing the dielectric permittivity that has been studied for microelectronics, power electronics and electronic actuation (Tan et al 2006). Some ceramics can have the dielectric permittivity of up to 103, but they need better engineering to augment breakdown strength. Some highly preferred polymers also show high dielectric permittivity, high breakdown strength and mechanical flexibility. However, a concern to be addressed is the increased dielectric loss. GE has developed some polymer dielectrics that have good combination of dielectric properties and self-clearing capability, which can extend the film life at voltages near the breakdown field of the films.
A survey of dielectric materials with dielectric properties (
Dielectrics of interests to microelectronics and electrical systems (
Recently, various governmental agencies have been actively seeking dielectric materials that can offer a high energy density (>20J/cm3) and high temperatures (200°C) for pulsed power and power conditioning applications. Figure 6 shows a theoretical design rule for dielectric films to meet a 25 J/cm3 energy density requirement. If a dielectric material can have a dielectric permittivity of 10-20, a film with reasonable breakdown strength and thickness can be the great candidate for the purpose. If using a ceramic material with a permittivity of higher than 60, much lower breakdown strength is required. The film in need can be more than 10um in thickness. Currently, many institutions are actively investigating on dielectric films. The high performance dielectric materials have become one of the strategic thrust in material technology.
Relationship of energy density, film thickness, dielectric constant and breakdown strength (
Polymer based nanodielectric materials have been investigated over the last 10 years forhigh energy density capacitors and higher corona resistant electrical insulation (Aulagner 1995). Various investigators have pointed to the challenges of increasing breakdown strength. For example, a homogeneous dispersion of ferroelectric BaTiO3 particles were prepared in polypropylene with an in-situ polymerization process (Guo et al 2007). Although the permittivity was increased by almost 3x over polypropylene, the breakdown strength was decreased. The breakdown mechanism was found very complicated particularly for the multi-phase systems. The mismatch of different phases and the various non-uniformity and defects introduced from processing were of critical consideration.
Depending on the filler-matrix interface, the size and shape, and agglomeration of fillers, the charge carriers are facing a more complicated environment under an electric field. Figure 7 shows a typical nanoparticle distribution in a Ultem™ polyetherimide (PEI). Agglomeration and aggregation are a great issue for the dry particles (left). Better particle dispersion could be achieved if solution prepared nanoparticles (right) are used, however, many desired materials are not available in solution or in large quantity. This would be an area of interest that need more in-depth investigations.
Transmission electron microscope images of dry and wet nanoparticles in Ultem™ (
In order to leverage the synergy of ceramic fillers and polymer matrices, the composite material physics should be better understood. Tan et al investigated the nano-filled polyetherimide films and emphasized the critical role of interface for the dielectric phenomenon (Tan et al 2007). In this study, Tan reported that ceramic fillers of higher breakdown strength do not impart the composite more endurance to high electrical field. Figure 8 depicts the dependence of intrinsic breakdown strength of various dielectric materials on their energy band gap. A linear increase with increasing band gap is well established. Yet data previously presented suggests that such a linear relationship cannot be correspondingly translated into nanofilled composites. A plausible explanation is that poor particle dispersion, agglomeration, voids or defects occurring at the particle-polymer interface might over shadow the positive effect of the nanoparticles.
Tan proposed a interfacial transition model as shown in Figure 9. The dielectric permittivity is increasing from polymer matrix to fillers through a complicated interfacial region. Even inside the interfacial region, the dielectric permittivity could include a free volume (void) layer and transition layer (surface state). In these low dielectric permittivity zones, field stress are higher and could trigger localized discharge causing free carrier movement. If a good dispersion and interface are established, this type of local failure might not happen. According to Qi et al (Qi et al 2004), the critical field (Eb) depends on the dielectric permittivity (ε), thermal conductivity (κ) of the dielectric with thickness (d): Eb~ln(4εκ/σ0d2). Higher dielectric permittivity and thermal conductivity associated with ceramic fillers help extend breakdown resistance. If localized heating occurs, it can be either dissipated in oxide fillers or more quickly transported through the samples due to shortened time permittivity (d2/4κ). The thermal excitation of charges is thus restricted and breakdown strength is increased. It can be conjectured that better ceramic particle dispersion and elimination of interfacial defects could even improve the breakdown strength beyond that of the polymers. Improvments in particle processing, stabilization and dipersion control are required to realize this goal.
Band gap dependence of breakdon strength of various dielectric materials (
A chematic model of the transition interface for a dielectric inclusion in a polymer (
The increase in dielectric permittivity of polymer through addition of inorganic fillers is also challenging. For a 0-3 composite consisting of high permittivity ceramic and polymer, the logarithmic rule is most closely obeyed.
The volume fraction (ϕ) of fillers randomly distributed in the composite cannot exceed the value for percolation point to avoid low mechanical strength and dielectric strength. Therefore, the permittivity of the composites (ε’composite) is limited by the polymer permittivity (ε’matrix). Filling a polymer matrix with high permittivity particles was known to result in a limited increase in permittivity of the resulting composite. For example, a ferroelectric type of ceramic materials having a dielectric permittivity of several hundred at room temperature is shown in Figure 10. A significantly high dielectric permittivity peak was dramatically suppressed when added to a polymer (polyetherimide). How to synergize filler and polymer, still remains a question.
The temperature dependence of the dielectric permittivity of lead zirconate ceramics and particle filled polyetherimide polymer (
GE found that the higher permittivity base polymer is favored for achieving higher permittivity in nanodielectric composites. Figure 11 shows the pronounced effect in the engineering the dissimilar dielectrics. When dealing with fillers with various the shape, surface chemistry, dispersion, polarity, it still requires ingeneous thought and understanding of the physical properties of the materials. Figure 12 shows a number of materials with a wide range of dielectric permittivity and dielectric loss. Some of them are good candidates not only for their dielectric properties but also for their processing capability. With properly selected polymer matrices and inorganic fillers, one can expect to achieve good overall properties.
The effect of the polymer matrix on the composite permittivity augmentation (
Dielectric properties of various dielectric materials. High dielectric permittivity generally exhibits high dielectric loss (
The simplest 2-phase system is a nanosphere filled polymer composite, which has received extensive investigation. A common phenomenon of such kind of composites is represented by Figure 13, where particles are of 20-100nm in diameter. It is not surprising that semiconductive and partially oxidized aluminum fillers lead to lower breakdown strength. However, for the insulative fillers with a breakdown strength of greater than 200kV/mm, the composite breakdown strength does not show much difference. It appears to slightly increase and saturate in the range of 500-600kV/mm for all insulating particles. It is interesting to see that ceramic particles of higher breakdown strength do not impart the composites more endurance to high electrical field.
The breakdown strength (BDS) of PEI composites increases with increasing BDS of 5 wt% various particles (
When particles are smaller than 10nm, the interfacial fraction becomes dominant, which might result in interesting physical phenomena. Figure 18 depicts the increasing role of particle interface with decreasing the particle sizes (Raetzke et al 2006). With a 5 vol% particle added in a polymer, the 10nm particles will result in 40 vol% interfaces. The 5nm particles result in 95 vol% interface of the composites. When the fillers are spheroid, more complicated filler-matrix interaction and physical properties are to be expected, which requires more investigations.
The particle size dependence of the particle interfacial contribution at various loading concentration (
In order to increase the dielectric constant of the composites, researchers mostly are interested in high permittivity nonlinear fillers like BaTiO3 and TiO2. Because of the difficulty in synthesizing very fine and clean particles, researchers have added high concentration of particles. Table 2 summarized the accomplishment of several representative researches. The maximum energy density from the nanodielectric engineering so far is 15 J/cc. Recently, very fine TiO2 nanoparticles were synthesized using self-assembling method (Tuncer et al 2009). The breakdown strength of the composites was increased by >20%. Therefore, better results could be achieved after optimizing nanoparticle size, surface chemistry and processing method of composites.
For polar fillers that have high permittivity, one needs to consider their polar interaction with the matrix. The alignment of dipoles in the long-range order is known to give high permittivity due to collective response to the electric field such as those in ferroelectric materials (Figure 15). Correspondingly, it also results in lower breakdown strength. For example, dipolar ordering in PVDF polymer is relatively interrupted due to randomness in molecular chain configuration as opposed to those in ferroelectric ceramics. This is why PVDF ferroelectric polymer intrinsically has lower dielectric permittivity (11) but higher breakdown strength (>400kV/mm). Similarly, controlling dipolar ordering within short-range in nanodielectrics could result in both higher permittivity and breakdown strength. A balanced value in the two parameters would be eventually beneficial to the achievement in a high energy density. Such a complex dielectric system requires not only experimental verification, but also computer modeling and simulation for conceptual guidance.
Researcher | Polymer | Filler | Process | Energy Density (J/cm3) |
PennState | PVDF-CTFE PVDF-TrFE | -BaTiO3 -PMNPT | Mixing | 7 15 |
Georgia Tech | PVDF-HFP | Treated BaTiO3 | Mixing | 6 |
Northwestern Univ. | PP | BaTiO3 | In-situ polymerization | 9 |
GE | CR | Clean BaTiO3 | Mixing | 15 |
Status of energy density in nanodielectric composite investigation
Due to the complexity of the composite microstructures, computational methods are usually required for study of the realistic multi-component microstructures (Ang et al 2003, Tuncer 2005, Todd et al 2005, Zhou et al 2008). Boundary integral technique (Azimi et al 1994, Cheng et al 1997) and finite element method (Ang et al 2003, Zhou et al 2008) are the commonly used computational methods, which numerically solve for interface charge density distribution and spatial potential distribution, respectively, in composite systems.
Dipolar ordering model for nanofilled polymer composites (
To avoid the complication associated with them, phase field method is recently employed to perform computational studies of composite materials (Zhang et al 2007, Wang 2010). It calculates heterogeneous distributions of polarization, charge density, local electric field, and effective dielectric permittivity of the composites, where inter-phase boundary conditions are automatically satisfied without explicitly tracking inter-phase interfaces. The phase field model of dielectric composites9 is formulated in terms of polarization vector field P(r). The total system free energy
where
The effective susceptibility tensor eff of the composite is determined according to the anisotropic constitutive relation:
where
where
Wang et al (2011) did a series of computation for a composite containing well-dispersed particles of 20% volume fraction arranged into square or quasi-hexagonal lattices. The particle interactions are assumed not strong enough to significantly reduce the depolarization effect of individual particles, and the composites exhibit moderate effective susceptibility in all directions. However, with particles aligned into chains, particles strongly interact through electrostatic forces in the chain direction to effectively reduce the depolarization effect of the particles in each chain, resulting in significantly improved composite susceptibility along the chain direction as shown in Figure 16. In the transverse direction, on the other hand, the strong chain-chain interactions enhance the depolarization effect, leading to decreased composite susceptibility in direction perpendicular to the chains. Therefore, the composites exhibit significant susceptibility anisotropy, which results from the microstructure anisotropy of the filler particle arrangement. In fact, alignment of filler particles into chains establishes pseudo-1-3 connectivity as compared to 0-3 connectivity of dispersed particles (Newnham 1978), which forms continuous paths of high-susceptibility phase and enhances filler particle polarization under external electric field applied along the chain direction.
Effect of filler particle microstructural arrangement on effective composite susceptibility in composites composed of 20% volume fraction of circular fillers with dielectric susceptibility
Because of the issues associated with high volume fraction such as poor filler dispersion, porosity and defected filler-matrix interfaces, an appropriate range of filler volume fraction should be designed. To investigate the effect of filler particle volume fraction on the effective dielectric permittivity of composites, composites composed of different volume fractions of same-sized circular fillers with the same dielectric susceptibility
where V is filler phase volume fraction. It is found that at low volume fraction (e.g., V<30%) eff is closer to the lower bound
Effect of filler particle volume fraction on effective composite susceptibility in composites composed of fillers with dielectric susceptibility
Synthesis of higher permittivity composites by mixing the various fillers (ceramic, semiconductor, metallic, organic) has been conducted over the last decade. Engineering the existing materials requires advances in designing, processing, forming and manufacturing technologies. Successes require (i) higher dielectric permittivity polymers, (ii) higher dielectric permittivity ceramics, (iii) proper dispersion, and (iv) good interfacial adhesion between filler and polymer. Fillers that have been studied include ceramics (SiO2, Al2O3, TiO2, BaTiO3, SrTiO2, PZT, PMN-PT, PLZT, MgO, Mica, layered silicate, Si3N4, BN), semiconductors (Si, SiC, ZnO, varistor particles, SnO2, antimony modified SnO2), metals (Al, Cu, Ag, Co-ACAC, Pt), carbon (black, CNTs). Great deal of knowledge has been acquired about nanodielectrics, however, there is still no breakthrough in increasing dielectric strength and permittivity simultaneously. In order to enhance dielectric properties, mechanical flexibility and thermal stability, further investigations are required, which can include the following subjects.
What types of ceramic fillers and polymers are able to synergize the dielectric properties?
What type of fillers, their size, shape and orientation are the best choice for composites?
What kinds of filler surface chemistry promote filler dispersion and better properties?
How does filler interface contribute to dielectric properties?
How effective are the insulation coated nanofillers on dielectric property augmentation?
What needs to be done to develop the polymers having high dielectric permittivity, high temperature and also low dielectric loss?
How does the nanodielectric engineering improve both dielectric properties and thermal conduction properties?
Support from GE Energy Conversion AT program and DARPA DSO under Contract FA9451-08-C-0166 are greatfully acknowledged.
Rust diseases are the fungal diseases of plants, mainly grasses, caused by fungi. They affect the aerial plant parts especially leaves but can also attack stems and even flowers and fruits. They bear complex life cycles that require two alternative unrelated hosts. Rusts produce spore pustules which vary in color according to the rust species. About 7000 rust species are known to affect a variety of host plants globally. They can cause a wide range of symptoms depending upon the host species like the formation of Galls or swellings on the branches, formation of Canker on the trunks, and formation of Spores on the surface of the leaf. Leaf rust is also known as bown rust due to the brown color of circular urediniospores on the surfaces of the leaf of the crop. Yellow rust or stripe rust is characterized by the yellow color of stripes on the surfaces of the leaf. Stem rust is also brown and characterized by the patches of brown color on the surface of stems. Many approaches are being deployed to combat the problem of these diseases which involves accurate phenotyping which means characterization of the diseases at field level followed by genotyping to find out the genes responsible for its cause. Many germplasm resources are being explored and screened by scientists worldwide to find new sources of resistance. Precision phenotyping is the key requirement to achieve the goals. So far there are manual interventions involved to screen these diseases. But manual scoring of these diseases is a cumbersome job in large pre-breeding and breeding programs. Therefore, there is a strong need for high precision phenomics which involves imaging using high-quality cameras or equipment followed by image analysis using newly developed software and tools. In today’s era of artificial intelligence, it is possible to explore high-end phenomics to achieve better yields of important crops like wheat. Many machine learning and deep learning models have been tested and tried to analyze and characterize wheat fungal diseases [1, 2, 3, 4]. One of the main reasons for the popularity of these techniques is the use of GPUs (graphics processing units). The classification tools, computer vision, and GPUs are combined in a single framework called deep learning [5]. Deep learning-based models have been used in the various applications of agriculture for end-to-end learning. With the use of GPUs, deep learning can give a better solution to the given problem in a shorter time [6]. The process of building such models is computationally challenging but using GPU power becomes very easy [7, 8]. Fungal diseases have been identified using image processing techniques on different horticulture and agriculture crops. Various feature extraction and classification algorithm have been used to detect the different types of fruits, vegetables, and cereal crops.
Among the various rust diseases, soybean-, coffee-, and wheat-rusts are the most damaging diseases. Therefore, the constant efforts are being done worldwide, to combat this problem. Wheat is one of the staple food crops in addition to rice and maize. The total area under wheat in the world is around 220 million hectares with a production of 772.64 million metric tons (2020–2021). Wheat rusts especially leaf rust, stem rust, and yellow rusts are major fungal diseases that affect the production of the wheat crop throughout the world particularly in South Asian countries [9]. As per the prediction of the Food and Agricultural Organization (FAO) of the United Nations, wheat production might not be fulfilled the requirement in near future due to rapid population growth [10, 11]. In this chapter we discuss the usefulness of deep learning-based algorithms to identify rust using wheat as a case study.
Human perception is based on the interaction between the brain and the eye. On the other hand, computer vision system (CVS) is used to emulate human vision for gathering information without physical interaction [12, 13].
It is also defined as the process of automatic acquisition, and analysis from image data. CVS emulates the dynamic vision system whose operation is very transparent and natural. The data is processed in various stages such as capturing, processing, and analysis of images. Figure 1 depicts the steps involved during image processing. In the first stage, image acquisition and pre-processing are involved. The images can be acquired using high-resolution cameras and sensors. Further, the images are pre-processed through data cleaning, background removing, adding/removing noise, and also enhancing the quality of images. In the second stage, the images are segmented. The segmentation process involves extracting only important and useful information from the whole image that further helps in the discrimination of classes. In the third stage, the high-level analysis is performed in which direct emphasis is done on the recognition (objects) and interpretation (making results). In a CVS, the following attributes contribute to decision-making: shape, color, texture, and also size. Figure 2 depicts the utilization of various artificial intelligence algorithms in plant disease detection. These algorithms are further divided into machine learning and deep learning-based classifiers. The description of these algorithms is illustrated in the coming subsections.
Steps of image processing techniques.
Description of machine learning and deep learning algorithm used for plant disease detection.
Classification is the process of dividing the dataset into different categories or groups by adding labels. Nowadays, the machine learning and deep learning approaches are performing well for classifying the algorithm images based on their category. Following are the machine learning algorithms which are used to classify plant disease and are based on supervised learning. Supervised learning is a type of learning where labels (category of images) are given along with input images.
It is the machine learning algorithm used for classification and calculated by
It is the algorithm of machine learning which comes under supervised learning to solve regression and classification-based problems. The decision tree is the graphical representation of pre-defined rules along with the solution. The graph of the decision tree has two types of nodes: one is decision nodes and another is leaf nodes. Additionally, the edges store the information of the answers to the questions, and leaf nodes store the actual output. In Sabrol and Kumar [16], Chopda et al. [17] and Rajesh et al. [18], the authors reported appreciable results in plant disease classification and recognition.
Support vector machine (SVM) is a very popular classifier used in statistical learning. The classifier aims to discriminate the classes from each other. In SVM, a hyperplane is used to discriminate one class from another. Those points which are close to the hyperplane are referred to as support vectors. The task of the SVM is to classify the different categories based on some features. Additionally, this algorithm performs well in extreme classes. Let us consider, color, texture, shape are some features of a particular plant. If we consider two features such as color and texture to classify diseased and healthy leaves. To classify them, the optimal decision boundary is required. Optimal decision boundaries could result in greater misclassification for the new instance. Therefore, the boundary support vectors are very important than all the training examples. This algorithm works well for linearly separating data points whereas in some cases if the data points are not linearly separable then 2-dimensional (2D) feature spaces are converted into 3-dimensional feature spaces. But the only problem is that it is computationally very expensive. In addition to that, it provides kernel function which can reduce the computational cost to convert 2D feature space to 3-dimensional feature space. Using kernel function the dot product is performed between two vectors. Especially, this is used to transform non-linear to linear transformation space. Various popular kernel functions are polynomial, radial basis, sigmoid kernels used to change 2D data to high dimensional feature space. Choosing the best kernel is a non-trivial task and is a hyper-parameter that can be selected by performing various experiments on the data. The main benefit of using SVM is that it is memory efficient and effective for high-dimensional feature space data.
It is the special type of machine learning algorithm used for classification. The researchers have been working on artificial neural networks (ANNs) since the beginning of the 1980s [19]. ANNs are a special type of classification algorithm and their structure is inspired by the human brain. ANNs takes input from the external world in the form of feature vector or patterns. Each input value is multiplied by their corresponding weights that are summing with the bias value. Further, the result is mapped to the activation function (binary, sigmoid) and produced the output. Other than these algorithms, there are various algorithms available that reported appreciable results in image recognition such as Random Forest, Naive Bayes, many more. Initially, we started with the study of traditional computer vision approaches used for plant disease detection. Plant disease can be caused by fungi, bacteria, and viruses from which fungi are the common disease organism. It is the type of disease that can be formed by taking energy from plants. The fungal disease has been identified using image processing techniques on different horticulture/agriculture crops [20]. To detect the different types of fruits, vegetable, commercial, and cereal crops that have been utilized using various feature extraction and classification algorithms. They achieved appreciable classification accuracy to identify the disease from horticulture/agriculture crops. Han et al. proposed a novel technique for feature extraction using super-pixel and marker-controlled segmentation methods for the classification of yellow rust and septoria diseases. They have used SVM and ANN for these disease classifications. Their experimentation concludes that SVM classifiers outperformed well than ANN classifiers for the classification of disease [21]. Su et al. experimented with the detection of fungal yellow rust disease on wheat crops. The author collected RGB images with a high-resolution camera and there are a total of three different classes present in region of interest (RoI) as rust, healthy, and background. To monitor the yellow rust, they used the U-Net deep learning architecture and the results were compared with the Random Forest algorithm. They found that U-Net-based segmentation outperformed spectral images. In their work, the average precision of 81.06%, recall of 90.10%, and F1-score of 84.00% have been achieved to segment the disease from spectral images [22]. An application of Fuzzy C-Means clustering has been proposed as the model to identify the wheat leaf disease [23]. In their work, they extracted inter- and intra-class features and further combined them to build a model for identifying the different wheat plant diseases. Although the traditional machine learning-based techniques are performing well for image classification, still there are certain limitations such as it requires manual feature extraction and is only suitable for small datasets, which may lead to the over-fitting problem [23, 24].
Convolutional neural network (CNN) is a popular neural network, designed for solving computer vision problems. The architecture of CNNs is shown in Figure 3. The images are represented in the form of pixel values. In the convolution layers, the operation of convolution is performed i.e., the kernel is slide over the input image after choosing the padding and stride values at each layer. Thereafter, the power of non-linearity is to give the non-linear mapping with the input images in such a way that after the non-linear mapping it becomes linearly separable. ReLU activation function is used to change all the negative values to positive values. With this, the pooling layer is used to down sample the different feature maps for getting the most prominent features i.e., the convolution layer performs these triplet operations like convolution followed by ReLU and ReLU followed by pooling one after another. These triplets operations are typically stacked one after another and also based on these triplets, the depth of the neural network has been defined. After these layers, the network is followed by one or more fully connected layers which are responsible for classification.
The basic architecture of CNNs.
To build the CNN model, all the above-mentioned parameters play a very important role. To build the custom CNN model, the numbers of convolution layers, max-pool layer, number of filter values, filter size, stride, padding, number of fully connected layers need to be specified. Increasing the number of convolution layers will produce different feature maps and also increasing the fully-connected layers increase the training time of the model. Although, the custom CNN model reported appreciable accuracy. The process of creating a custom CNN model takes more time. Therefore, the concept of transfer learning comes into the picture. Transfer learning is a concept of deep learning where the weights of pre-trained models are reused for a new problem. Every year, there is a competition held on the ImageNet dataset. Many researchers developed new models to classify the different objects of the ImageNet dataset and reported good classification accuracy and reduced error rate. There are variants of transfer learning models such as ResNet, GoogleNet, and EfficientNet varied in terms of the number of layers, filter size, number of filters used, stride, padding, and so on. Some of the few models are elaborated as given below:
Modern deep learning architectures are significantly popular to solve agriculture-related problems. Sladojevic et al. developed a CNNs based model for plant disease classification. The model recognized 13 different types of plants. In their work, they used 30,880 images in the training and 2589 images for validation and reported a classification accuracy of 96.30% [25]. Zhang et al. proposed a deep learning model for the detection of rust disease of wheat crop from hyperspectral images. In their work, they automate the process of detecting yellow rust-captured images from unmanned aerial vehicle (UAV). Yellow rust is a fungal disease that can cause 100% loss for the wheat crop. The author used the Inception-ResNet model for feature extraction and reported the highest accuracy of 85.00% when compared with the random forest that was 77.00% [26]. A deep learning model has been built for grading wheat stripe rust disease [27]. In their work, they used different mobile devices to capture images and build their dataset, referred WSRgrading. It contained 5242 wheat leaf images at six different levels. They build and proposed the model by adding an attention layer in the pre-trained DenseNet model and build a new model named as C-DenseNet which has been reported a good classification accuracy of 97.99%. Genaev et al. classify the rust disease from the wheat crop. In their work, they used the CGIAR dataset, containing three classes (healthy wheat, leaf rust, and stem rust). They implemented the DenseNet transfer learning model and reported the F1-score and AUC of 0.90 and 0.98, respectively [28]. Jia et al. in proposed the model for detection and segmentation of fruit features for optimal harvesting of apples using Mask R-CNN. ResNet model was used as the backbone of this network. The model was tested on 120 images and reported precision and recall rates of 97.31% and 95.70%, respectively [29]. The shortage of the wheat disease dataset motivated the researchers to create the dataset which should be publicly available for all [30]. They are motivated to collect more data that will help the research community for conducting the research competitions on wheat diseases classification. Finally, they attempted to prepare their WFD2020 dataset which contains 2414 images. They performed their experiments using the EfficientNet CNN-based model and reported 94.20% classification accuracy.
In the recent decade, deep learning techniques are highly utilized for image processing. Deep learning models are producing appreciable results than machine learning methods [31]. Figure 4 depicts the utilization of computer vision approaches (i.e., old machine learning methods and modern deep learning approaches) for the wheat crop. These statistics have been built based on work done from the period (2015 to July 2021) for classifying most of the wheat crop diseases. Deep learning approaches include CNN-based architecture such as VGG16, ResNet, Faster R-CNN, and so on. In different circumstances, the traditional machine learning approaches include SVM, Random Forest, and so on. The analysis concludes that the modern deep learning architectures have been utilized more for classifying most of the wheat crops diseases as compared to traditional machine learning approaches.
Year-wise statistics publication of wheat disease detection.
There are standard datasets that are publicly available for research experimentation in the computer vision and image processing domain, such as PASCAL VOC [32], ImageNet [33], IMDB-Wiki [34], CIFAR [35], and PlantVillage [36]. CGIAR dataset is one of the dataset publicly available on https://www.kaggle.com/shadabhussain/cgiar-computer-vision-for-crop-disease [37]. This dataset was further distributed in three different classes of wheat rust i.e., healthy wheat, leaf rust, and stem rust. A sample of each class is shown in Figure 5. Most of the images in this dataset were collected by CIMMYT and its partners from Ethiopia and Tanzania. Additionally, a few images were sourced from the Google image database. The images in this dataset have the specific characteristics like (i) all are colored (ii) mixed format, (iii) different orientation, (iv) variable quality, and captured with different resolutions. The datasets are already classified into two categories i.e., 876 images and 610 images for training and testing, respectively. From the training dataset (i.e., 876 images) a total of 863 images have been filtered and considered for training the model. In the present study, the 863 images dataset was further split for training and validation in the ratio of 3:1 (i.e., 75% data in training and 25% into validation). Table 1 describes the class-wise distribution of this dataset. It is a challenging task to build an efficient model that is capable to classify all three classes of images accurately.
Sampled images of (a) healthy wheat plant, (b) leaf rust, and (c) stem rust.
Class label | Images | Training set | Validation set |
---|---|---|---|
Healthy wheat | 142 | 105 | 36 |
Leaf rust | 345 | 258 | 86 |
Stem rust | 376 | 283 | 95 |
Total images | 863 | 646 | 217 |
Class-wise distribution of image dataset.
Deep learning is a popular methodology used for image processing. In deep learning models, features are extracted automatically and little human intervention is required to train the model. Deep learning models are quite efficient to discover the internal structure or patterns of high-dimensional data. However, directly processing the original images leads to inappropriate recognition results, therefore, it is necessary to pre-process the images before feeding them to the model. Pre-processing involves e.g. resizing, enhancing, or removing noise of the input images. It is worth mentioning that CNNs perform better for image recognition and classification. There are various transfer learning models which are based on CNNs like AlexNet, VGG16, GoogleNet, and Inception V3, that are pre-trained on the ImageNet dataset. ImageNet is the standard dataset that contains 1000 different categories of objects. CNN’s based transfer learning models reported appreciable results to classify 1000 different objects present in the ImageNet dataset. In the present study, the VGG16 model has been utilized and the architecture is depicted in Figure 6. This model is the composition of 16-layers (13 convolution layers, and 3 fully connected layers). In this model, the images are processed in standard size i.e., 224 × 224. The reason for resizing the fixed image size is to extract the uniform or equal feature maps at the end of the convolution process. This model used a fixed size of kernel i.e., 3 × 3. Sometimes, the kernel is referred to as a filter that is responsible for extracting features from the given images. These extracted patterns or features might be horizontal edges, vertical edges, and a combination of both. Initially, a convolution process has been performed to extract the features, and thereafter the classification is done. In the convolution operation, the kernel/filter is sliding over the image starting from the top left to the bottom right corner to extract the features.
The architecture of VGG16 for wheat rust disease detection.
The movement of the kernel is either pixel-wise or by skipping some pixels using stride values. If the stride value is 1 then the movement of the kernel is shifted by one pixel after another and if the stride value is 2, then the movement of the kernel is shifted by two-pixel values during the operation of convolution. The convolution layers are used to identify the pattern or features from the images which further help in discriminating the classes. The initial layers extract the general features like edges and the subsequent layers extract the domain-specific features. Each convolution block is followed by the max-pool layer which is used for down-sampling the feature maps. In this process, the dimensionality of the image is reduced by retaining the most prominent feature. At the end of the convolution layers, different feature maps are generated as an output. These feature maps are further flattened and mapped with a fully connected layer in the classification module. Here, the model has a feature vector of size 4096 neurons also referred to as dense layer. This feature vector is further passed to the next dense layer of the same size. Finally, the last layer neurons are fully connected to the output neurons by using the soft-max activation function. However, in the current study, we considered the three classes classification problem. Therefore, the output layer changed to three classes using the soft-max probability function. The actual learning starts from data using forward and backward passes. In the forward pass, input neurons are multiplied with the weight values and also apply the activation function as ReLU. ReLU activation function adds non-linearity to the model i.e., all the negative pixel-values become positive after passing through it. On the other hand, in backward pass back-propagation is used to minimize the loss value. In this process, weights and biases are getting updated from the last to the initial layer by calculating the gradients at each layer using a convolution operator.
To summarize this model, the important and noticeable point is that this model has a total of 14,789,955 parameters but 75,267 are trainable parameters and the rest are non-trainable, the reason is that using transfer learning, the already trained weights have been used during building the model. Therefore, the model is trained in less time with fewer number parameters.
Hyper-parameter tuning is the backbone of any deep learning model. Finding the best parameters is a very tedious task, it needs many experiments to be performed while building the model. Hyper-parameters include learning rate, batch size, loss function, number of epochs, and optimizer is usually considered for tuning the model. To build the classification model for three classes each hyper-parameter is considered within a specific range. In this way, several experiments have been performed to build an efficient model. After performing some experiments with the variation in the given hyper-parameters, it was concluded that model accuracy is highly dependent on the batch size, learning rate, number of epochs, and size of the dataset. In the present study, the following hyper-parameters has been utilized:
As discussed in Section 3.1 image dataset of wheat disease classification has been utilized to train the model. We used the online google colab platform with GPU support. Among the performed experiments, we discuss the best one, which produces the highest training accuracy. Table 2 illustrates the training and validation accuracies obtained at different epochs (varied from 10 to 90) along with their loss values. Here, the training accuracy starts with 81.42% on 10 epochs and ends up with 99.54% on 80 epochs. We continued to compute the accuracy for the 90 epochs also but did not get any significant improvement in training accuracy. Although more experiments could be performed by increasing the number of epochs, the accuracy obtained at epoch 80 was quite promising. On the other hand, the validation accuracy fluctuating between 74.76% and 79.05% at different epochs, as shown in Figure 7. Similarly, it was observed that the training loss decreases at every increasing step of the epoch (from 10 to 80). Beyond that, the loss has started to increase. In contrast, the validation loss is fluctuating between 0.60 and 0.65 up to 40 epochs. Then, after 70 epochs it starts increasing rapidly (Figure 8).
Epochs | Training accuracy (in %) | Validation accuracy (in %) | Training loss | Validation loss |
---|---|---|---|---|
10 | 81.42 | 74.76 | 0.50 | 0.65 |
20 | 91.02 | 79.05 | 0.33 | 0.61 |
30 | 95.05 | 77.14 | 0.22 | 0.61 |
40 | 96.59 | 78.10 | 0.18 | 0.61 |
50 | 97.06 | 76.67 | 0.15 | 0.56 |
60 | 97.99 | 78.10 | 0.12 | 0.56 |
70 | 98.61 | 74.29 | 0.09 | 0.66 |
80 | 99.54 | 77.14 | 0.07 | 0.74 |
90 | 99.23 | 74.76 | 0.08 | 0.82 |
Comparison of training accuracy, validation accuracy, and training loss, and validation loss at different epochs.
Representation of the comparison of training and validation accuracy.
Representation of the comparison of training and validation loss.
To test the performance of the trained model, we performed the test experiments on the validation data (i.e., 25% of the total dataset). In this way, a total of 36 sample images of healthy leaf, 87 sample images of leaf rust, and 94 sample images of stem rust have been considered. The evaluation of the testing results was done using a confusion matrix. Figure 9 illustrates the accuracy and confusion with other intra-classes, wherein, it is shown that leaf rust class samples are confused with stem rust class samples due to less variation between classes.
Confusion matrix at epoch = 80.
To summarize this book chapter, different machine learning and deep learning-based models have been discussed to solve plant disease classification and detection problems. Considering a case study of wheat rust diseases, a deep learning-based model is proposed to classify the different wheat rust diseases using a pre-trained VGG16 model. Based on the CGIAR dataset with three classes (stem rust, leaf rust, and healthy wheat), the proposed model has been optimized and produced the classification accuracy of 99.54%, and when evaluated on unseen data it gave a validation accuracy of 77.14%. This model will further help farmers or experts to diagnose disease in the early stages. Although these models give good training accuracy, they were not appropriate to classify stem- and leaf rust when result plot on confusion metrics. This is due to the fact that some images in this dataset contained multiple diseases, meaning that one image contained the features of both leaf- and stem rust. Detection and classification of the wheat rust disease in the early stages lead to high yield at the production level [38]. In the future, we will extend this work by collecting real-time images of wheat rust disease and also incorporating object detection-based algorithms such as Yolov3, Faster R-CNN, and Mask R-CNN [39] to exactly localize the location of the disease in the image.
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Its effects on the economy (and also on the wider society) are well researched, yet still not completely. Corruption thus inhibits economic growth and affects business operations, employment and investments. It also reduces tax revenue and the effectiveness of various financial assistance programs. The wider society is influenced by a high degree of corruption in terms of lowering of trust in the law and the rule of law, education and consequently the quality of life (access to infrastructure, health care). There also does not exist an unambiguous answer as to how to deal with corruption. Something that works in one country or in one region will not necessarily be successful in another. This chapter tries to answer at least a few questions about corruption and the causes for it, its consequences and how to deal with it successfully.",book:{id:"6487",slug:"trade-and-global-market",title:"Trade and Global Market",fullTitle:"Trade and Global Market"},signatures:"Štefan Šumah",authors:[{id:"228073",title:"Mr.",name:"Stefan",middleName:null,surname:"Sumah",slug:"stefan-sumah",fullName:"Stefan Sumah"}]},{id:"55499",title:"Human Resources Management in Nonprofit Organizations: A Case Study of Istanbul Foundation for Culture and Arts",slug:"human-resources-management-in-nonprofit-organizations-a-case-study-of-istanbul-foundation-for-cultur",totalDownloads:2294,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The aim of this study is to investigate the efficiency and importance of human resources management in nonprofit organizations. The understanding was included to the literature as personnel management at the beginning of the twentieth century and it turned into an approach as human resources management in the 1980s. It could be observed that many organizations, which deem the human as the most critical stakeholder, adopt a traditional way of personnel management in operating human resources. The employees play a key role in the success of an organization. For this reason, subjects such as recruitment, training, development, career management, performance appraisal, occupational health, and safety are the fundamental functions of human resources management. The study examines to what extent these roles are evaluated through a case study. The subject matter of the study is the most powerful culture and art foundation in Turkey. Compared to many other nonprofit organizations, the foundation actively performs a variety of services within a year worldwide. 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Social marketing strategies can also be used to promote behavioral change and help individuals transform their lives, achieve well-being, and adopt prosocial behaviors. In this chapter, we seek to analyze with a netnographic study, how SNS are being employed by nonprofits and nongovernment organizations (NGOs) to enable citizens and consumers to participate in different programs and activities that promote social transformation and well-being. A particular interest is to identify how organizations are using behavioral economic tactics to nudge individuals and motivate them to engage in prosocial actions. By providing an understanding on how SNS can provide an adequate environment for the design of social marketing strategies, we believe our work has practical implications both for academicians and marketers who want to contribute in the transformation of consumer behavior and the achievement of well-being and social change.",book:{id:"6583",slug:"marketing",title:"Marketing",fullTitle:"Marketing"},signatures:"Alicia De La Pena",authors:[{id:"196878",title:"Dr.",name:"Alicia",middleName:null,surname:"De La Pena",slug:"alicia-de-la-pena",fullName:"Alicia De La Pena"}]}],onlineFirstChaptersFilter:{topicId:"4",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82405",title:"Does Board Structure Matter in CSR Spending of Commercial Banks? 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Also, board meetings do not have any significant connection with CSR spending. For control variables, factors, such as firm size and leverage, tend to promote the CSR spending of commercial banks, while profitability has no such relationship. As for the sectoral distribution of CSR funds, we found that although the absolute amount of CSR expenditures by banks has increased substantially over the years, they are primarily limited to health, education, natural disasters, and humanitarian activities. These findings are expected to have significant policy implications.",book:{id:"11602",title:"Corporate Social Responsibility",coverURL:"https://cdn.intechopen.com/books/images_new/11602.jpg"},signatures:"Bishnu Kumar Adhikary and Ranjan Kumar Mitra"},{id:"82395",title:"Toward a Better Understanding of Green Human Resource Management’s Impact on Green Competitive Advantage: A Conceptual Model",slug:"toward-a-better-understanding-of-green-human-resource-management-s-impact-on-green-competitive-advan",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.105528",abstract:"Today, green human resource management (GHRM) has become a key business strategy where HRM plays an active role in the ongoing green movement. Thus, the topic of GHRM is of growing interest among management scholars. However, despite the theoretically important role of GHRM, relatively small number of research has been discovered so far about how GHRM, in companies striving to achieve environmental sustainability, could help them gain a green competitive advantage (GCA). Thus, based on the resource-based view (RBV) arguments, the main objective of this paper is to develop a conceptual model of the relationship between GHRM and green competitive advantage through green knowledge, green values, and green commitment. This model is expected to provide a strategic map that could be utilized by the practitioners and managers so that GHRM implementation can be more effective in contributing to green competitive advantage. Overall, the present article extends knowledge on the resource-based view by contributing to the literature on GHRM and its interactions with the main assets that lead to green competitive advantage.",book:{id:"11602",title:"Corporate Social Responsibility",coverURL:"https://cdn.intechopen.com/books/images_new/11602.jpg"},signatures:"Hosna Hossari and Kaoutar Elfahli"},{id:"82394",title:"Learning by Doing Active Social Learning",slug:"learning-by-doing-active-social-learning",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.105523",abstract:"Project-based learning and future-based pedagogy are important and effective tools for teaching and learning in the twenty-first century. They are especially suited to instilling social activism among students, which is extremely valuable in today’s multicultural society. This study examined the impact of such learning among Arab and Jewish students and teachers in Israel. Following a collaborative program on social activism, in which students from different sectors worked together via digital platforms and face-to-face encounters, the impact of the program and its pedagogical tools were examined. The program, called Living in a Multicultural Society, reflects the mosaic of different people and communities, living side by side yet separated by religion, culture, and language. Through this program, students who may not have otherwise met worked together to learn, research, and create. This study was conducted using the mixed-method approach, whereby the qualitative data were gathered via interviews, and the quantitative data were collected through questionnaires. The findings show that this project-based learning program led to significant encounters, understandings, and co-operations between different sectors, and to meaningful end-products relating to social activism. 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He completed a one-year Post-Doctoral Fellowship awarded by the DFAIT (Foreign Affairs and International Trade Canada) at the Institute of Biomedical Engineering of the University of New Brunswick (Canada) in 2010. Currently, he is Professor in the Faculty of Electrical Engineering (UFU). He has authored and co-authored more than 200 peer-reviewed publications in Biomedical Engineering. He has been a researcher of The National Council for Scientific and Technological Development (CNPq-Brazil) since 2009. He has served as an ad-hoc consultant for CNPq, CAPES (Coordination for the Improvement of Higher Education Personnel), FINEP (Brazilian Innovation Agency), and other funding bodies on several occasions. He was the Secretary of the Brazilian Society of Biomedical Engineering (SBEB) from 2015 to 2016, President of SBEB (2017-2018) and Vice-President of SBEB (2019-2020). 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Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}},{id:"338856",title:"Mrs.",name:"Nur Alvira",middleName:null,surname:"Pascawati",slug:"nur-alvira-pascawati",fullName:"Nur Alvira Pascawati",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universitas Respati Yogyakarta",country:{name:"Indonesia"}}},{id:"441116",title:"Dr.",name:"Jovanka M.",middleName:null,surname:"Voyich",slug:"jovanka-m.-voyich",fullName:"Jovanka M. 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Dr. Chen\\'s research interests include bioactive compounds, chromatography techniques, in vitro culture, medicinal plants, phytochemicals, and plant biotechnology. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/18273",hash:"",query:{},params:{id:"18273"},fullPath:"/chapters/18273",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)}()