Deep learning-assisted microstructural analysis of Ni/YSZ anode composites for solid oxide fuel cells
DC Field | Value | Language |
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dc.contributor.author | Hwang, Heesu | - |
dc.contributor.author | Ahn, J. | - |
dc.contributor.author | Lee, Hyunbae | - |
dc.contributor.author | Oh, Jiwon | - |
dc.contributor.author | Kim, Jaehwan | - |
dc.contributor.author | Ahn, J.-P. | - |
dc.contributor.author | Kim, H.-K. | - |
dc.contributor.author | Lee, J.-H. | - |
dc.contributor.author | Yoon, Y. | - |
dc.contributor.author | Hwang, Jinha | - |
dc.date.accessioned | 2021-09-02T04:40:42Z | - |
dc.date.available | 2021-09-02T04:40:42Z | - |
dc.date.created | 2021-03-12 | - |
dc.date.issued | 2021-02 | - |
dc.identifier.issn | 1044-5803 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/16114 | - |
dc.description.abstract | Quantitative microstructural interpretations were carried out without human involvement through an integrated combination of deep learning and focused ion beam-scanning electron microscopy (FIB-SEM) analytics on Ni/Y2O3-stabilized ZrO2 (Ni/YSZ) cermets. The Ni/YSZ/pore composites were analyzed for the automated extraction of microstructural parameters to prevent the subjective analysis problems and unavoidable artifacts frequently encountered in lengthy image processing tasks and eliminate biased evaluations. Considering the high volume of image data and future expectations for electron microscopy usage, FIB-SEM was efficiently combined with semantic segmentation. Traditional image processing analysis tools are combined with phase separation predictions by semantic segmentation algorithms, leading to a quantitative evaluation of microstructural parameters. The combined strategy enables one to significantly enhance poor image quality originating from artifacts in electron microscopy, including charging effects, curtain effects, out-of-focus problems, and unclear phase boundaries encountered in searching for high-efficiency solid oxide fuel cells (SOFCs). ? 2021 Elsevier Inc. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Elsevier Inc. | - |
dc.subject | Deep learning | - |
dc.subject | Image analysis | - |
dc.subject | Image enhancement | - |
dc.subject | Image segmentation | - |
dc.subject | Ion beams | - |
dc.subject | Nickel compounds | - |
dc.subject | Nickel metallography | - |
dc.subject | Parameter estimation | - |
dc.subject | Phase separation | - |
dc.subject | Scanning electron microscopy | - |
dc.subject | Semantics | - |
dc.subject | Zirconia | - |
dc.subject | Automated extraction | - |
dc.subject | Focused ion beam-scanning electron microscopies | - |
dc.subject | Microstructural analysis | - |
dc.subject | Microstructural parameters | - |
dc.subject | Quantitative evaluation | - |
dc.subject | Semantic segmentation | - |
dc.subject | Solid oxide fuel cells (SOFCs) | - |
dc.subject | Subjective analysis | - |
dc.subject | Solid oxide fuel cells (SOFC) | - |
dc.title | Deep learning-assisted microstructural analysis of Ni/YSZ anode composites for solid oxide fuel cells | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hwang, Jinha | - |
dc.identifier.doi | 10.1016/j.matchar.2021.110906 | - |
dc.identifier.scopusid | 2-s2.0-85099663630 | - |
dc.identifier.wosid | 000620431000005 | - |
dc.identifier.bibliographicCitation | Materials Characterization, v.172 | - |
dc.relation.isPartOf | Materials Characterization | - |
dc.citation.title | Materials Characterization | - |
dc.citation.volume | 172 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Metallurgy & Metallurgical Engineering | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Metallurgy & Metallurgical Engineering | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Characterization & Testing | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Image analysis | - |
dc.subject.keywordPlus | Image enhancement | - |
dc.subject.keywordPlus | Image segmentation | - |
dc.subject.keywordPlus | Ion beams | - |
dc.subject.keywordPlus | Nickel compounds | - |
dc.subject.keywordPlus | Nickel metallography | - |
dc.subject.keywordPlus | Parameter estimation | - |
dc.subject.keywordPlus | Phase separation | - |
dc.subject.keywordPlus | Scanning electron microscopy | - |
dc.subject.keywordPlus | Semantics | - |
dc.subject.keywordPlus | Zirconia | - |
dc.subject.keywordPlus | Automated extraction | - |
dc.subject.keywordPlus | Focused ion beam-scanning electron microscopies | - |
dc.subject.keywordPlus | Microstructural analysis | - |
dc.subject.keywordPlus | Microstructural parameters | - |
dc.subject.keywordPlus | Quantitative evaluation | - |
dc.subject.keywordPlus | Semantic segmentation | - |
dc.subject.keywordPlus | Solid oxide fuel cells (SOFCs) | - |
dc.subject.keywordPlus | Subjective analysis | - |
dc.subject.keywordPlus | Solid oxide fuel cells (SOFC) | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Microstructure features | - |
dc.subject.keywordAuthor | SOFC anode composites | - |
dc.subject.keywordAuthor | Stereology | - |
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