Specification of wear testing equipment.
\r\n\tIn sum, the book presents a reflective analysis of the pedagogical hubs for a changing world, considering the most fundamental areas of the current contingencies in education.
",isbn:"978-1-83968-793-8",printIsbn:"978-1-83968-792-1",pdfIsbn:"978-1-83968-794-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"b01f9136149277b7e4cbc1e52bce78ec",bookSignature:"Dr. María Jose Hernandez-Serrano",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10229.jpg",keywords:"Teacher Digital Competences, Flipped Learning, Online Resources Design, Neuroscientific Literacy (Myths), Emotions and Learning, Multisensory Stimulation, Citizen Skills, Violence Prevention, Moral Development, Universal Design for Learning, Sensitizing on Diversity, Supportive Strategies",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 14th 2020",dateEndSecondStepPublish:"October 12th 2020",dateEndThirdStepPublish:"December 11th 2020",dateEndFourthStepPublish:"March 1st 2021",dateEndFifthStepPublish:"April 30th 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Phil. Maria Jose Hernandez Serrano is a tenured lecturer in the Department of Theory and History of Education at the University of Salamanca, where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. in Education and Training in Virtual Environments by research with the University of Manchester, UK (2009).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"187893",title:"Dr.",name:"María Jose",middleName:null,surname:"Hernandez-Serrano",slug:"maria-jose-hernandez-serrano",fullName:"María Jose Hernandez-Serrano",profilePictureURL:"https://mts.intechopen.com/storage/users/187893/images/system/187893.jpg",biography:"DPhil Maria Jose Hernandez Serrano is a tenured Lecturer in the Department of Theory and History of Education at the University of Salamanca (Spain), where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. on Education and Training in Virtual Environments by research with the University of Manchester, UK (2009). She obtained a Visiting Scholar Postdoctoral Grant (of the British Academy, UK) at the Oxford Internet Institute of the University of Oxford (2011) and was granted with a postdoctoral research (in 2021) at London Birbeck University.\n \nShe is author of more than 20 research papers, and more than 35 book chapters (H Index 10). She is interested in the study of the educational process and the analysis of cognitive and affective processes in the context of neuroeducation and neurotechnologies, along with the study of social contingencies affecting the educational institutions and requiring new skills for educators.\n\nHer publications are mainly of the educational process mediated by technologies and digital competences. Currently, her new research interests are: the transdisciplinary application of the brain-based research to the educational context and virtual environments, and the neuropedagogical implications of the technologies on the development of the brain in younger students. Also, she is interested in the promotion of creative and critical uses of digital technologies, the emerging uses of social media and transmedia, and the informal learning through technologies.\n\nShe is a member of several research Networks and Scientific Committees in international journals on Educational Technologies and Educommunication, and collaborates as a reviewer in several prestigious journals (see public profile in Publons).\n\nUntil March 2010 she was in charge of the Adult University of Salamanca, by coordinating teaching activities of more than a thousand adult students. She currently is, since 2014, the Secretary of the Department of Theory and History of Education. Since 2015 she collaborates with the Council Educational Program by training teachers and families in the translation of advances from educational neuroscience.",institutionString:"University of Salamanca",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Salamanca",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"23",title:"Social Sciences",slug:"social-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"301331",firstName:"Mia",lastName:"Vulovic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/301331/images/8498_n.jpg",email:"mia.v@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6942",title:"Global Social Work",subtitle:"Cutting Edge Issues and Critical Reflections",isOpenForSubmission:!1,hash:"222c8a66edfc7a4a6537af7565bcb3de",slug:"global-social-work-cutting-edge-issues-and-critical-reflections",bookSignature:"Bala Raju Nikku",coverURL:"https://cdn.intechopen.com/books/images_new/6942.jpg",editedByType:"Edited by",editors:[{id:"263576",title:"Dr.",name:"Bala",surname:"Nikku",slug:"bala-nikku",fullName:"Bala Nikku"}],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:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],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:"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:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],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. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"69454",title:"Wear Behaviour of Aluminium Alloy 8011 with 4% Fly Ash Composites by Using Sensitivity Analysis",doi:"10.5772/intechopen.89554",slug:"wear-behaviour-of-aluminium-alloy-8011-with-4-fly-ash-composites-by-using-sensitivity-analysis",body:'\nMetal matrix composites (MMCs) occur as an essential category of material used in space and transportation industries. There is an inclusive in dropping the wear in demand to decrease the tradition of material properties and expenditure of energy. This controlling of wear should be considered cautiously from the idea of choosing the alloy composition, reinforcement and additionally the process techniques. The incorporation of hard reinforcement segments, particulates, fibres and whiskers has been capable of these composites through smart tribological characteristics [2, 8, 12, 13, 14, 15, 16, 17, 18].
\nThese reinforcements will either be value-added ex-situ or created as in-situ composites within the dissolved. It is glowing well-known that in-situ supports stay of the many smart benefits appreciate wholesome boundary and extraordinary affection power through the medium, homogenously circulated minor parts within the matrix, extraordinary mechanical assets and low cost. In-situ ceramic mixtures appreciate Al2O3 [23], TiB2 [1] and TiC [24] widely occupied as reinforcements in aluminium fabricated composites.
\nAluminium ash composites can be synthesised through the liquid metal stir casting, compo casting (semi-solid processing), changed compo casting and squeeze casting techniques. The stir casting route product is well distributed, moderately agglomerate and consistent free fly ash particle composites [1, 2, 3, 4].
\nAA2011 matrix reinforced with SiC reinforcement (5 & 10%) produced by liquid metallurgy route. SiC abrasive wear rate is increased with increasing applied load, sliding distance and element size compared for Al2O3emery paper is used means the wear rate increase of element size, applied load decrease with increasing the sliding distance and at the same time, the abrasive size was more effective for both matrix and composite [5].
\nAA6061 with 1% of CNT reinforcement is produced by ball milling and spark plasma process and it is reported that the mild wear rate and friction coefficient are low compared to the monolithic 6061 alloy [6].
\nThe wear rate in a concession of weight loss per unit sliding distance, coefficient of friction and volume loss continues to achieve for the metal matrix composites. The results of the composite show higher wear resistance than matrix metal [7].
\nAA7075/graphite composite materials with (5–20) wt.% are fabricated by a liquid casting technique and pin on disc method to calculate the wear rate. The coefficient of friction is compact with the calculation of graphite content and stretched a minimum at 5 wt. % graphite content [8].
\nDevelops the mathematical typical is settled by mistreatment regression analysis methodology for the expectation of damage performance of the MMC besides sufficiency of the prototypical has been valid expending analysis of variance (ANOVA) methods. Finally, the inflation of parameter has in addition been done using style trained software package. The results ensure unprotected that response surface methodology (RSM) may be a smart tool for expectation of wear behaviour below combined sliding and rolling action [9].
\nDevelops a regression prototypical is valid by normal mathematics code SYSTAT 12 and normal arithmetic tools equivalent to analysis of variance (ANOVA) and student’s t-check. It has been found that establishing the regression prototypical is also effectively wished to calculate the wear rate at 95% confidence level and expected trends are mentioned with the assistance of worn surface morphologies. The results of the composite show higher wear of the metal matrix [10].
\nDevelops a mathematical model to predict the wear rate of AA6061/ (0−10%) ZrB2 in-situ composites. The factors thought of area unit sliding speed, sliding distance, traditional load and mass fraction of ZrB2 particles. The impact of those factors on the damage proportion of the made-up composite is examined and additionally, the expected trends are mentioned by perceptive the injury surface morphologies [11].
\nSensitivity analysis of the process parameters of gas metal arc welding process of welding speed, voltage and current. Based on the result, we represent the success of the processing parameters and showed that the change of process parameters influences the bead width and bead height with further strong penetration [19].
\nThe effect of welding on flux cored arc welding material of 317 L on a structural steel plate. The process parameters used in the experiment were welding current, speed and nozzle to plate distance. Sensitivity analysis has been applied to find out the process parameters with the most influence on the bead geometry. Sensitivity analysis shows that bead width, dilution, area of penetration and coefficient of internal shape are mostly affected by the change of process parameters [20].
\nWe investigated the sensitivity analysis of weld bead parameters such as bead width, bead height and penetration to variations in current, voltage and speed in the submerged arc welding process. The result shows that bead width is more sensitive to voltage and speed variations than bead height and penetration [21].
\nSensitivity analysis to predict the tensile strength of friction stir welded alloy AA7039 aluminium. Based on the result they concluded, it was found that the sensitivities of rotational speed cause large changes in tensile strength when the RPM increases compared with welding RPM and axial force [22].
\nIn the present work, an attempt is made to develop a mathematical model to calculate the wear rate of (AA8011-4% FA) composite and analyse the impact of normal load (N), time (min) and sliding velocity (m/sec) on wear rate and coefficient of friction. Interestingly, mathematical models are conducted consistent with central composite rotatable utilised. Mathematical models are established to predict the impact of method constraints on the responses. Finally, the sensitivity results are compared and verified with the experimental result.
\nThe tribological properties of the samples were assessed using a dry sliding wear test for different number of specimens by using a pin-on-disc machine as shown in Table 1.
\nS.No. | \nDescription | \nDetails | \n
---|---|---|
1. | \nMake & Model | \nDUCOM, TR-20LE-PHM-400 | \n
2. | \nNormal load range | \n5–15 N | \n
3. | \nDisc speed | \n100–1000 rpm | \n
4. | \nPre-set timer range | \n5–15 min | \n
5. | \nWear disc thickness | \n8 mm (EN 31 steel (C, 1.00%; Si, 0.20%; Mn, 0.50%; Cr, 1.40%; hardness of 60 HRC) | \n
6. | \nWear disc track diameter | \n90 mm | \n
7. | \nSpecimen pin diameter | \n10 mm | \n
8. | \nPin length | \n50 mm | \n
9. | \nASTM standard | \nG99 | \n
10. | \nMeasurement and real-time data acquisition | \nWear, friction, COF and temperature. | \n
Specification of wear testing equipment.
Figure 1 reveals that the surface of each pin and disc is clean with a soft paper soaked in resolving before actual testing. The fabric loss from the composite surface is measured employing exactness electronic balancing machine with an accuracy of ±0.0001 g. Wear rate is unique of the maximum significant criteria on behalf of formative the pin-on-disc operation. Higher wear rate is always preferred in such operations. Wear rate was measured from the weight loss. Each trial was repeated twice and the average weight loss was taken for the analysis of wear rate. In this study, the machining performance and the mathematical model were evaluated using Eqs. (1) and (2). Tables 2 and 3 show that the setup of the experiment was designed on the idea of the central composite design (CCD) technique.
\nPin-on –disc wear experimental setup.
Parameter | \n−1 | \n0 | \n1 | \n
Load (N) | \n5 | \n10 | \n15 | \n
Time (min) | \n5 | \n10 | \n15 | \n
Sliding velocity(m/sec) | \n1.5 | \n3 | \n4.5 | \n
Process parameters and their levels.
Run | \nLoad (N) | \nTime (min) | \nSliding velocity (m/sec) | \nWR (g/min) × 10−5 | \nCOF (μ) × 10−2 | \n
---|---|---|---|---|---|
1 | \n5 | \n5 | \n1.5 | \n342 | \n55.9 | \n
2 | \n15 | \n5 | \n1.5 | \n434 | \n37.2 | \n
3 | \n5 | \n15 | \n1.5 | \n372 | \n49.7 | \n
4 | \n15 | \n15 | \n1.5 | \n484 | \n54.2 | \n
5 | \n5 | \n5 | \n4.5 | \n428 | \n36.9 | \n
6 | \n15 | \n5 | \n4.5 | \n534 | \n33.4 | \n
7 | \n5 | \n15 | \n4.5 | \n454 | \n26.6 | \n
8 | \n15 | \n15 | \n4.5 | \n585 | \n45.2 | \n
9 | \n5 | \n10 | \n3.0 | \n422 | \n52.3 | \n
10 | \n15 | \n10 | \n3.0 | \n535 | \n52.3 | \n
11 | \n10 | \n5 | \n3.0 | \n398 | \n34.4 | \n
12 | \n10 | \n15 | \n3.0 | \n434 | \n37.5 | \n
13 | \n10 | \n10 | \n1.5 | \n380 | \n38.3 | \n
14 | \n10 | \n10 | \n4.5 | \n466 | \n24.9 | \n
15 | \n10 | \n10 | \n3.0 | \n430 | \n38.2 | \n
16 | \n10 | \n10 | \n3.0 | \n435 | \n38.0 | \n
17 | \n10 | \n10 | \n3.0 | \n434 | \n38.4 | \n
18 | \n10 | \n10 | \n3.0 | \n430 | \n38.6 | \n
19 | \n10 | \n10 | \n3.0 | \n432 | \n38.4 | \n
20 | \n10 | \n10 | \n3.0 | \n432 | \n38.2 | \n
Design of experiment matrix and wear characteristics.
Source | \nDF | \nAdj SS | \nAdj MS | \nF-value | \nP-value | \n
---|---|---|---|---|---|
Model | \n9 | \n61981.7 | \n6886.86 | \n1458.03 | \n0.000 | \n
Linear | \n3 | \n3875.2 | \n1291.72 | \n273.47 | \n0.000 | \n
Square | \n3 | \n6472.3 | \n2157.45 | \n456.76 | \n0.000 | \n
2-Way interaction | \n3 | \n390.4 | \n130.12 | \n27.55 | \n0.000 | \n
Error | \n10 | \n47.2 | \n4.72 | \n\n | \n |
Lack-of-fit | \n5 | \n26.4 | \n5.28 | \n1.27 | \n0.401 | \n
Pure error | \n5 | \n20.8 | \n4.17 | \n\n | \n |
Total | \n19 | \n62029.0 | \n\n | R2 = 99.92% | \n
Analysis of variance table for wear rate.
Source | \nDF | \nAdj SS | \nAdj MS | \nF-value | \nP-value | \n
---|---|---|---|---|---|
Model | \n9 | \n1400.34 | \n15.559 | \n2051.03 | \n0.000 | \n
Linear | \n3 | \n490.29 | \n163.429 | \n2154.31 | \n0.000 | \n
Square | \n3 | \n535.42 | \n178.475 | \n2352.64 | \n0.000 | \n
2-Way interaction | \n3 | \n374.63 | \n124.878 | \n1646.13 | \n0.000 | \n
Error | \n10 | \n0.76 | \n0.076 | \n\n | \n |
Lack-of-fit | \n5 | \n0.54 | \n0.108 | \n2.45 | \n0.174 | \n
Pure error | \n5 | \n0.22 | \n0.044 | \n\n | \n |
Total | \n19 | \n1401.10 | \n\n | R2 = 99.95% | \n
Analysis of variance table for COF.
The factorial portion of CCD is a full factorial design with all mixtures of the factors at three levels (+1, 0, −1) and composed of the eight-star points and six central points (coded level 0), which were the centre between the high and the low levels. The star points were at the face-centre of the cube portion that corresponds to an α value of 1 and this sort of design was usually known as the “face-centred CCD.” The tests were conducted using stipulated conditions in keeping with the face-centred CCD with 20 experimental observations at three independent input variables [15, 16, 17, 18].
\nYU is the response.
\nxi (1, 2 … k) is the coded levels of k numerical variables.
\nb0—endless term.
\nbi—linear term.
\nbii—quadratic term.
\nbij—interaction term.
\nIn this study, a second-order polynomial was selected to develop empirical equations to represent responses (wear rate and COF) in terms of controllable variables such as normal load (A), time (B) and sliding velocity (C). The final response equations were developed for wear rate and COF using the experimental results.
\nThis mathematical prototypical has been achieved to reproduce the independent, quadratic and interactive effects of some machining parameters on the machined for wear rate (WR) and coefficient of friction (COF). The empirical relationship for correlating the WR and COF the thought of dry sliding wear method parameters is obtained as follows:
\nEq. (3) and (4) develop empirical models based on the composite desirability optimisation technique. Figures 2 and 3 show that the experimental values and predicted values from the mathematical model are scattered on both sides and close to 45° line, which further proves the adequacy of the model.
\nStandard probability plot for wear rate.
Standard probability plot for COF.
The normal percentage point of F-ratio dispersal for 95% confidence limit is 5.05 as shown in Tables 4 and 5. The F -values (1.27 and 2.45) aimed at deficiency of adequate are lesser than the normal value. Thus WR and COF the prototypically stand adequate.
\nAfter eliminating the non-significant terms in Eq. (3) and (4), the final response equations for wear rate and coefficient of friction are given below.
\nFrom the developed mathematical Eqs. (5) and (6) to be used for the estimation of wear rate and coefficient of friction, the sensitivity equations are obtained by differentiating Eqs. (5) and (6) with respect to process parameters of load (A), time(B), and sliding velocity (C) as given below.
\nResults of WR and COF for sensitivities of load, time, sliding velocity on WR and COF are presented in Tables 6 and 7.
\nLoad (N) | \nB = 10 min, C = 3 m/sec | \n||
---|---|---|---|
\n\n | \n\n\n | \n\n\n | \n|
5 | \n−34.6796 | \n15.4504 | \n56.1380 | \n
10 | \n−30.1700 | \n14.4100 | \n48.8500 | \n
15 | \n−25.6604 | \n13.3696 | \n41.5620 | \n
Wear rate for sensitivities of process parameters.
Load (N) | \nB = 10 min, C = 3 m/sec | \n||
---|---|---|---|
\n\n | \n\n\n | \n\n\n | \n|
5 | \n−16.4708 | \n0.7805 | \n16.7171 | \n
10 | \n−14.6610 | \n0.6390 | \n10.8160 | \n
15 | \n−12.8512 | \n0.4975 | \n4.9149 | \n
COF for sensitivities of process parameters.
The influence of response surface plot for wear rate is shown in Figure 4. Wear rate increases with the increase of applied load, but the time also increases when wear rate increases. The frictional heat can result in excessive deterioration of the composite transfer from materials to the disc counterpart. The increase of applied load inspiration to disproportionate damage of the composite and thus results shows an increase in wear rate.
\nEffects of load and time on the wear rate.
In Figure 5 shows that the impression residues, the disc considerable develops worn and guarded material derives currently contact with the pin, scratching of the disc shallow origins the ploughing and later friction coefficient increase with a period of impression. After the period of impression the expansion of irregularity and alternative constraints might influence a particular solid value. Therefore, the standards of friction coefficient endure endless for the repose. This can be described by the mechanism that with the wear occurrence below process, supplementary fly ash particles are made with the effect of sliding velocity, consequently, the being of the fly ash disturbs at the connected area and intersection asset and so subsidises directly to the complex level of friction coefficient.
\nInfluence of time and sliding velocity on COF.
Figure 6 highlights the sensitivity analysis of load for WR and COF. Sensitivity analysis of load for WR and COF is negative for lower load values and it is negative as the value of sensitivity of load increases. Figure 7 illustrates the time sensitivity. Time sensitivity for WR is positive for lower load values and it is positive as the value of sensitivity of load increases and COF remains unchanged. Figure 8 reveals sliding velocity sensitivity. Sliding velocity sensitivity for WR is positive and increases with increase in load. COF is positive with increase in load.
\nSensitivity analysis results of load.
Sensitivity analysis results of time.
Sensitivity analysis results of sliding velocity.
\n
The developed regression prototypical is with success match the nearest predicted wear rate of cast AA 8011- 4% fly ash composites at 95% confidence level within the range of investigation.
The wear rate of composite compound values strongly depends on the investigational limits, that is, normal load, time and sliding velocity.
Wear rate increases as the sliding speed increases due to work hardening of the surface and crushing of the fly ash particles. The WR rises with the raise of the normal load. It is attributed to excessive damage to the composite. Wear rate increases dependably with regular interval due to deduction of the majority of the materials when exposed to sliding force.
COF growths through an increase in sliding speed, whereas increase in coefficient of friction normal load decreases then before rises. The assessment of friction coefficient is raised nearly linear up to 10 minutes of impression and subsequently rests to constant.
Response surface methodology has the potential for more stringent sensitivity analysis and may be used for optimal parameter estimation for other mathematical models.
Modern computers and electronics, such as smartphones and supercomputers, have been developed in accordance with Moore’s law [1], which implies improvement in cost, speed, and power consumption by scaling down devices. However, the fundamental physical limits and increased fabrication costs pose a hindrance to sustainable development of computing technology [2, 3]. Moreover, with the advent of the big data era, unstructured data and data complexity explosively increases, imposing constraints on the conventional computing technology owing to the von Neumann bottleneck [4, 5]. Neuromorphic systems [6, 7], which mimic the nervous system in the brain, have recently become known as strong candidates to overcome these technical and economic limitations owing to their proficiency in cognitive and data-intensive tasks, together with their low power consumption. To successfully implement these neuromorphic systems, it is of utmost importance to research and develop artificial synapses capable of synapse functions, high reliability, low energy consumption, etc. [8, 9]. In the plethora of possible devices, memristors have gained the spotlight because of their desirable characteristics as artificial synapses [10, 11, 12], including device speed [13], footprint [14], low energy consumption [15], and analog switching [16, 17].
In this chapter, we introduce the basic concepts of neuromorphic systems and memristor synapses. We also describe diverse examples for state-of-the-art artificial synapses in terms of novel functional materials and device architecture. We then briefly review the implemented neuromorphic systems based on memristor synapses.
Conventional computing architecture, that is, von Neumann architecture, forms the groundwork for modern computing technologies [3, 18]. Despite tremendous growth in computing performance, classical architecture currently suffers from the von Neumann bottleneck, which results from data movements between the processor and the memory unit [4, 5]. The memory wall issue, causing high power consumption and low speed, hinders the continuous development of computing technologies [4, 5, 9]. Moreover, artificial neural network (ANN) algorithms, such as deep learning [19], deal with image classification [20, 21], sound recognition [22, 23], specific complex tasks (e.g., the AlphaGo [24]) and so on. Although the ANN algorithms have exhibited superior performance over the conventional computing technologies, they are, at present, constructed on the von Neumann architecture; hence, considerable time and energy resources are required for their operation [8, 9]. Neuromorphic architecture [6, 7], a bio-inspired computing architecture, is one of the most promising candidates to resolve these problems. The neuromorphic systems take advantage of the cerebral nervous system, which consists of a massive parallel connectivity between the neurons (i.e., processor) and the synapses (i.e., memory), indicating the absence of the von Neumann bottleneck [8, 9]. Figure 1 shows the shift of the computing architecture from von Neumann architecture (Figure 1a) to neuromorphic architecture (Figure 1b). The von Neumann architecture shows that the processor and memory are separate, leading to the von Neumann bottleneck. In contrast, in the case of neuromorphic architecture, the neurons and synapses are combined, alleviating the bottleneck issue. The neurons are uncomplicated computing units, the synapses are local memory units, and the communication channels (red line) connect numerous neurons and synapses. It should be noted that the practical purpose of neuromorphic systems is not to replace the von Neumann architecture completely, but to supplement the conventional architecture to make up its leeway, especially for intelligent tasks such as image recognition and natural language processing.
(a) Conventional computing architecture (von Neumann architecture). Data transfer is performed through the bus (memory wall). (b) Neuromorphic architecture. In contrast to von Neumann architecture, von Neumann bottleneck does not exist.
Memristors that consist of a storage layer inserted between the top and bottom electrodes can undergo dynamic reconfiguration within the storage layer with the application of electrical stimuli, resulting in resistance modulation referred to as memory effect [16, 17]. The changed resistance state can be retained even after electrical inputs are removed, and memristors are based on the history of applied electrical stimuli. These capabilities lead to analog switching, which resembles biological synapses where the strength (or synaptic weight) can increase or decrease depending on the applied action potential [25, 26]. When neuromorphic architecture is implemented on the conventional computing architecture, the synaptic weights are stored in the memory unit and are continuously read into the processor unit to transfer information to post-neurons. In other words, practically, the von Neumann bottleneck still remains challenged. However, in case of memristor synapse-based neuromorphic systems, the synapses can not only store a specific weight but also naturally transmit information into post-neurons, overcoming the von Neumann bottleneck and improving system efficiency [8, 9]. In addition to analog switching, memristors have exhibited desirable device properties, including nanoscale footprint [14], long endurance and retention [17, 27], nanosecond switching speed [13, 15], and low power consumption [15]. Owing to these characteristics, memristors have emerged as promising candidates for artificial synapses. However, it should be noted that no specific material/device system has shown all-encompassed characteristics so far.
Depending on their storage layer and electrode, memristors can be broadly classified into two categories: cation-based devices and anion-based devices. It is widely believed that cation-based devices are based on migration of metallic cations (see Figure 2a) [17, 28]. They employ electrochemically active materials such as Ag or Cu as an electrode [29, 30, 31, 32]. The counter electrode is usually an electrochemically inert material, such as Pt, Au, or W, and the storage layer consists of a solid-electrolyte like Ta2O5, SiO2, or Cu2S. For example, when a positive voltage is applied to an Ag top electrode, the atoms from this electrode are electrochemically oxidized to Ag+ cations because of anodic reaction, which are then dissolved into a solid-electrolyte layer. The Ag+ cations migrate across the solid-electrolyte layer toward the counter electrode (e.g., Pt) depending on electric field. At the Pt electrode, the Ag+ cations are electrochemically reduced to Ag atoms because of cathodic reaction and are deposited on its surface. Thus, conductive filaments grow toward the Ag top electrode, and eventually the filaments bridge the anode and the cathode, indicating that the device switches into ON state (low resistance state) as shown in Figure 2a. In contrast, when a negative voltage is applied to the Ag top electrode, the Ag filament begins to dissolve anodically, starting from the interface of the Ag top electrode/Ag filament, which results in OFF state (high resistance state). Owing to this process, cation-based devices are referred to as electrochemical metallization memories and conductive bridging random access memories. It should be noted that the initial formation of conductive filaments is called the electroforming process, which needs a voltage higher than a switching voltage.
(a) Cation-based devices: Through electrochemical reaction, metal cations M+ migrate toward the counter electrode and form conductive filaments between the top and bottom electrodes. (b) Anion-based devices: During electroforming, the soft-breakdown leads to O2− ions (oxygen vacancies V), and the oxygen vacancies form conductive filaments between the top and bottom electrodes.
Anion-based devices usually require the initial electroforming process and are switched depending on the O2− anions (or positively charged oxygen vacancy V) induced into the storage layer by soft-breakdown (see Figure 2b). These devices consist of a sub-stoichiometric storage layer made of HfOx [33, 34], TaOx [35, 36], WOx [37, 38], etc. When a positive forming voltage is applied to the top electrode, the induced O2− ions migrate toward it. This anion motion causes a change in the valence state of the cation to keep the charge neutral; hence, these devices are also referred to as valance change memories. Throughout the process, the oxygen vacancies continue to form conductive filaments in the storage layer. When the filaments bridge the top and bottom electrodes, current flows through the filaments, with the result that the device switches to ON state. Contrastingly, when a negative voltage is applied to the top electrode, the O2− ions either recombine with oxygen vacancies present in the filaments or oxidize the cation precipitates, with the result that the device switches to OFF state. Thus, memristors could be understood to some extent based on cation- and anion-based mechanisms. However, identifying the precise mechanism of a specific device is a challenge because of the presence of mingled mechanisms and different driving forces or locations. Therefore, further studies are necessary for a deeper understanding of the switching mechanism.
Various properties of memristor synapses that affect the performance of neuromorphic computing need to be discussed in detail. Among them, representative characteristics such as the linearity in weight update, multilevel states, dynamic range (ON/OFF ratio), variation, retention, endurance, and footprint will be addressed in this section as they can substantially affect computing achievements [8, 35]. The linearity of the weight update indicates the linear relationship between synaptic weight change (∆w) and programming pulse. In other words, the conductance of the memristor synapse changes linearly in accordance with the number of programming pulses, which is associated with the mapping of weight in the algorithms for conductance in memristor synapses. Hence, the linearity of weight update affects the performance (e.g., accuracy). Notably, most memristor synapses show a nonlinear weight update, where the conductance change gradually saturates, as shown in Figure 3. Hence, the nonlinearity of weight update should be improved to achieve highly efficient computing.
(a, b) Nonlinearity of weight update. Current abruptly changes in initial pulses and gradually saturates. Most memristors exhibit a nonlinear relationship. All figures are reproduced with permission from Ref [39, 10], respectively. Copyright (2017, 2010) American Chemical Society.
The resolution capability of storage is influenced by multilevel states and dynamic ranges because numerous conductance states can distinguishably store individual pixels of input patterns. Moreover, variations, including cycle-to-cycle and device-to-device variations, could degrade neuromorphic computing, particularly in large-scale systems. However, considering that neuromorphic computing exhibits the fault-tolerant property, neuromorphic architectures could be immune to the variation to some extent, and this is supported by several papers [8, 35, 52]. In addition, memristor synapses are repeatedly updated during the training process and should retain the trained weights (i.e., final conductance). Subsequently, the larger the endurance cycles and retention time, the better are the achievements of the neuromorphic network. Last but not least, it is desirable that device’s footprint is below sub-10 nm because high density leads to more synaptic devices that store learned information under a specific area [8].
Furthermore, it is efficient to improve the characteristics of memristor synapses depending on individual neuromorphic networks, because a desirable memristor synapse capable of being employed into neuromorphic systems is yet to be reported. Supervised learning-based networks [35, 40, 41, 42, 43, 44], for example, are less vulnerable to cycle-to-cycle and device-to-device variations. This is because memristor synapses are updated according to calculated errors under known target values. By contrast, the networks based on unsupervised learning [39, 45, 46, 47] are directly affected by the variation owing to unknown target values. Therefore, memristor synapses need to be designed or selected depending on individual neuromorphic networks.
Memristors for synaptic devices with two-terminal (e.g., vertical/planar-type and gap-type) and three-terminal (e.g., field-effect transistor and lateral coupling type) structures are manufactured by well-established processing technologies [7, 8, 9, 10, 11, 12, 35, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55].
In the case of a two-terminal structure, when different voltages are applied to each of the two electrodes, resulting in current flow through the insulator, varying the conductance of the device enables emulation of biological synapse functions such as synapse plasticity [10, 11, 12, 16, 35, 48]. In particular, the crossbar array of two-terminal devices has received attention because of its characteristics relevant to synaptic devices, such as scalability for high density, simple fabrication process, low cost of fabrication, parallel connection structure, low power, fault-tolerance, and compactness. Thus, they are expected to provide an appropriate structure to support synaptic electronics. The type of two-terminal memristors that are being reffered to as the artificial synapses includes resistive random-access memory, phase change memory, conductive bridge memory, and spin-based memory. Although two-terminal devices are attracting much attention because of their ease of implementation of crossbar arrays, a two-terminal device, as a matter of fact, requires a select device to eliminate the sneak path that occurs in a crossbar array configuration. Additionally, it is difficult to imitate complex synaptic functions such as hetero-synaptic plasticity (e.g., modulatory input-dependent plasticity).
Three-terminal structures (e.g., field effect transistor memory and floating/gate transistor memory) with tunable conductance of channels between the source and the drain are also considered as synaptic devices [49, 50, 51]. The gate electrode acts as the pre-synapse, transferring the stimulus to the insulating layer, indicating the cleft of the synapse, and modulates the conductance of the channel representing the synaptic strength. Although the three-terminal structure is more complicated than the two-terminal structure and is disadvantageous in terms of density, the terminal for the signal transmission process and the learning terminal are separated such that simultaneous signal processing is possible, and complex synapse functions such as hetero-synaptic plasticity can be mimicked. Moreover, they do not require an additional selector device to reduce sneak current in an integrated array architecture.
Recently, going beyond simply implementing a synapse function, researchers have demonstrated advanced concepts of synapse device functions, including self-rectification, photo-assisted synaptic plasticity and neuromodulation to achieve more delicate imitation of the human brain and learning-and energy-efficiency in neurocomputing.
In [35], Choi et al. fabricated a self-rectifying memristor synapse through a two-terminal structure (Pt/TaOy/nanoporous TaOx/Ta), which is capable of suppressing unwanted leakage pathways and then a 16 x 16 crossbar array using only the devices without an additional selector (see Figure 4a and b). The mechanism of memristive switching and synaptic functions, including long-term potentiation (LTP), STDP (spike-timing dependent plasticity), and long-term depression (LTD) were caused by the migration of O2− ions with oxygen vacancies V by applied electric field in the TaOx. In addition, the asymmetric interface contacts of Pt/TaOy and TaOx/Ta prevent the undesired signal by performing the self-rectification function without the selector.
(a) Schematic of a self-rectifying memristor with a Pt/TaOy/nanoporous TaOx/Ta and cross-sectional image of a memristor synapse. (b) I-V curves of the self-rectifying memristor synapse. (a, b) are reproduced with permission from Ref [35] under a Creative Commons Attribution 4.0 International License. (c) Schematics of the suggested mechanism of how a conductive switching filament is formed by the iodine vacancy migration in the presence of light. (d) Synaptic potentiation and depression behavior of the OHP-based synaptic device. (c and d) are reproduced with permission from Ref [52]. Copyright (2018) John Wiley and Sons.
In [51], Huh et al. reported a synapse device that performs the neuromodulator function of a barristor structure using 2D material as shown in Figure 4c. The three-terminal device consisted of a vertically integrated monolithic tungsten oxide memristor, and a variable-barrier tungsten selenide/graphene Schottky diode, termed as a “synaptic barrister.” This synaptic barristor could implement fundamental synaptic functions, including short-term plasticity (STP), paired pulse facilitation (PPF), LTP, and LTD, with external gate controllability, termed as a neuromodulator in bio-synapse. This architecture potentially offers considerable power-saving benefits while significantly tuning the synaptic weights and intrinsically modifying the synaptic plasticity, in comparison with conventional two-neuronal-based synaptic architectures.
In [52], Ham et al. fabricated an organo-lead halide perovskite (OHP)-based photonic synapse in which the synaptic plasticity is modified by both electrical pulses and light illumination. The switching mechanism originates from the presence of a conductive filament by iodine-vacancy mediator, with its switching states controlled by electric-field domination (see Figure 4d). Using diverse electrical stimuli and relative timing between the input pulses, essential synaptic functionalities such as STP, LTP, and LTD were successfully demonstrated. In addition, owing to the accelerated migration of the iodine vacancy inherently existing in the coated OHP film under light illumination, the OHP synaptic device exhibits light-tunable synaptic functionalities with very low programming inputs (≈0.1 V) as shown in Figure 4d. The ability of high-order tuning of the photo-assisted synaptic plasticity in an artificial synapse can offer significant improvements in the processing time, low-power recognition, and learning capability in a neuro-inspired computing system (Figure 4e).
In [12], Wang et al. designed a diffusive memristor for STP synapses and threshold neurons. The devices contain a switching layer doped with Ag nanoclusters (MgOx:Ag, SiOxNy:Ag, and HfOx:Ag) using the co-sputtering method. The switching mechanism is based on the growth and relaxation of Ag nanoclusters depending on whether the voltage pulse is applied, which was experimentally verified by in-situ high-resolution transmission electron microscopy (HRTEM). The designed device mimicked STP under PPF and PPD. Moreover, the device was used as a threshold neuron along with drift memristor synapse based on TaOx to emulate STDP learning rule. Because the conductance of the device gradually increases according to applied voltage and then abruptly decreases under no applied voltage, the device can be used as a threshold neuron. The results give a potential application for simple artificial neurons as compared with CMOS artificial neurons [53, 54].
Prezioso et al. experimentally demonstrated neuromorphic networks based on memristor synapses (see [55]). In their paper, Al2O3/TiO2−x memristor was used to fabricate a 12 × 12 crossbar array to implement a single-layer network [56]. The single-layer network architecture was schematically described as shown in Figure 5a, where 10 input neurons and 3 output neurons are fully linked by 10 × 3 = 30 synaptic weights (Wi,j). Notably, this ANN architecture naturally corresponds to a crossbar array [9, 35]. Input voltages (Vi = 1…9) assigned from pixels of the 3 × 3 input images (see Figure 5b) were applied to each input neuron. After being applied into the network, the input voltages were individually weighted depending on each synaptic weight. Note that V10 is a bias voltage to control the degree of activation of the output neurons. The output neurons received each weighted voltage through linked weights and then integrated the weighted voltages (∑Wi,jVj), where j and i represent the input (j = 1–9) and output (i = 1–3) neurons respectively. The output neurons converted each integrated voltage into output (fi) ranging from −1 to 1 according to the nonlinear activation function: fi = tanh(βIi), where β adjusts the nonlinearity of the activation function and Ii = ∑Wi,jVj. The activation function can be considered as the threshold firing function in a biological neuron. The synaptic weights were represented by a pair of adjacent memristors (Wi,j = Gi,j+ − Gi,j−) for the effectiveness of weight update. The number of selected memristor synapses in 12 × 12 array were 30 × 2 = 60, due to a pair of memristors (Figure 5c). When the network was under the training process, as shown in Figure 5d and e, memristor synapses between input and output neurons were updated based on the Manhattan update rule, which is classified as supervised learning: ∆Wi,j = ηsgn∑[(ti(n) − fi(n)) × df/dI × Vj(n)], where η is the learning rate, ti(n) is the target value, fi(n) is the output value, and n is the nth input image. After the training process was complete, the memristor synapses retained their final conductance, and the test process was performed without weight update (see Figure 5d). From the test process, the neuromorphic network exhibited perfect classification for the first time in 21 epochs (note that one epoch indicates one training process). Although simple and few input images were used to train/test the neuromorphic network, this work greatly contributed to neuromorphic systems based on memristor synapses in terms of experimental demonstration using crossbar arrays.
(a) Input voltages corresponding to an input image (Vi = 1…9) and a bias voltage (V10). These voltages are fed into the single-layer network where 10 input neurons and 3 output neurons are linked by synaptic weights. (b) The “z,” “v,” and “n” input images. Aside from ideal images, other images contain one noise pixel. (c) The schematic of implemented 10 × 6 crossbar array, a pair of adjacent memristors provide one synaptic weight. (d) When an image (e.g., “z”) is fed into network, pixels for black give VR (read voltage) to the network, otherwise, −VR is applied into the network. (e) An instance of weight update according to Manhattan update rule. The synaptic weights corresponding to sign + should be increased, so that the memristors representing G1,1+, G1,2+, G1,5+, G1,6+, and G1,9+ are applied by set voltage. All figures are reproduced with permission from Ref [55]. Copyright (2015) Springer Nature.
It should be noted that the circuit that acquires sgn[ fi(n)] = sgn[∑Wi,jVj] = sgn[∑(Gi,j+ − Gi,j)Vj] could be implemented by a virtual ground circuit and a differential amplifier [43, 57]. Then, the output value is compared with the target value by circuits using a comparator. According to calculated ∆Wi,j, programming memristors of the array, for example, could be performed as shown in Figure 6 [39]. The test board contains four digital-to-analog converters (DACs) providing voltage pulses through the DACs. The DACs 1–4 represent the chosen bottom line, the unchosen bottom line, the chosen top line, and the unchosen top line, respectively. Using matrix switches (Switch 1 and 2), individual memristor is assigned to the corresponding DAC. The multiplexer (MUX) is operated to obtain currents that flow through memristors in the array by delivering the currents into the analog-to-digital converter (ADC). The ADC obtains the applied voltage of the resistor (1 kΩ), and the voltage is changed into the current. The arrows of Figure 6 represent the current flowing through a chosen memristor in case of write, erase, and read processes. Notably, there are non-idealities such as sneak currents and wire resistance in array-level, which could degrade the performance of neuromorphic computing [35, 44, 58, 59, 60]. The sneak currents affect learning accuracy and epochs because of undesired information, especially large-scale array. In Figure 6, in order to avoid sneak currents during read process, unchosen rows and columns are grounded [39]. Moreover, wire resistance consumes input voltages, so that memristors far from points of input voltage could be applied by smaller voltage than input voltage. This influences output currents, leading to degradation of learning performance. The non-idealities in array-level could be overcome by device functions [35, 44], operational scheme [39, 58, 59, 60], or learning algorithms [35, 40, 41, 42, 43, 44] to some degree.
Circuit scheme for write, erase, and read processes. The figure is reproduced with permission from Ref [39]. Copyright (2017) American Chemical Society.
Neuromorphic systems are one of the most promising candidates to deal with the von Neumann bottleneck caused by the memory wall between memory and process units. Using memristor synapses simply classified into cation- and anion-based devices can resolve this bottleneck owing to their storage and transmittance capabilities. To obtain higher performance of neuromorphic systems, representative characteristics, including the linearity of weight update, large multilevel states and dynamic range (ON/OFF ratio), variation and endurance, and retention need to be improved. In this context, different memristor synapses based on novel materials and device structures were introduced. Finally, we have briefly explained neuromorphic networks based on crossbar arrays of memristor synapses, and the network demonstrated perfect classification after 21 epochs. We believe that this chapter offers a deep understanding of the field of memristor synapses.
This work was supported by the National Research Foundation of Korea (NRF-2016R1C1B2007330 and NRF-2019R1A2C2003704), KU-KIST Research Fund, Samsung Electronics, and a Korea University Future Research Grant.
The authors declare no competing interests.
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