3D generation and reconstruction of the fuel cell catalyst layer using 2D images based on deep learning
The catalyst layer (CL) being the site of electrochemical reactions, is the core subunit of the membrane electrode assembly (MEA) in polymer electrolyte fuel cells (PEFCs). Thus, the porous structure of the CL has a significant influence on oxygen transfer resistance and affects the charge/discharge...
Ausführliche Beschreibung
Autor*in: |
Xuanchen Liu [verfasserIn] Kayoung Park [verfasserIn] Magnus So [verfasserIn] Shota Ishikawa [verfasserIn] Takeshi Terao [verfasserIn] Kazuhiko Shinohara [verfasserIn] Chiyuri Komori [verfasserIn] Naoki Kimura [verfasserIn] Gen Inoue [verfasserIn] Yoshifumi Tsuge [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Übergeordnetes Werk: |
In: Journal of Power Sources Advances - Elsevier, 2020, 14(2022), Seite 100084- |
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Übergeordnetes Werk: |
volume:14 ; year:2022 ; pages:100084- |
Links: |
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DOI / URN: |
10.1016/j.powera.2022.100084 |
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Katalog-ID: |
DOAJ018277055 |
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520 | |a The catalyst layer (CL) being the site of electrochemical reactions, is the core subunit of the membrane electrode assembly (MEA) in polymer electrolyte fuel cells (PEFCs). Thus, the porous structure of the CL has a significant influence on oxygen transfer resistance and affects the charge/discharge performance. In this study, the three-dimensional (3D) porous structure of the catalyst layer is reconstructed based on the deep convolutional generative adversarial network (DCGAN) deep learning method, utilizing focused ion beam scanning electron microscopy (FIB-SEM) microstructure graphs as training data. Each set of spatial-continuous microstructure graphs, generated by DCGAN with interpolation in latent space, is applied to build a unique 3D microstructure of the CL without the use of real FIB-SEM data. Meanwhile, distinct interpolation conditions in the DCGAN are discussed to optimize the ultimate structure by approaching the structural information to real data, including that of porosity, particle size distribution, and tortuosity. Moreover, the comparison of real and generated structural data reveal that the data generated by DCGAN shows an adjacency relationship with real data, indicating its potential applicability in the field of electrochemical simulation with reduced situational costs. | ||
650 | 4 | |a Polymer electrolyte fuel cells | |
650 | 4 | |a Catalyst layer | |
650 | 4 | |a Simulation | |
650 | 4 | |a Deep learning | |
650 | 4 | |a 3D reconstruction | |
650 | 4 | |a Microstructure generation | |
653 | 0 | |a Industrial electrochemistry | |
653 | 0 | |a Electric apparatus and materials. Electric circuits. Electric networks | |
700 | 0 | |a Kayoung Park |e verfasserin |4 aut | |
700 | 0 | |a Magnus So |e verfasserin |4 aut | |
700 | 0 | |a Shota Ishikawa |e verfasserin |4 aut | |
700 | 0 | |a Takeshi Terao |e verfasserin |4 aut | |
700 | 0 | |a Kazuhiko Shinohara |e verfasserin |4 aut | |
700 | 0 | |a Chiyuri Komori |e verfasserin |4 aut | |
700 | 0 | |a Naoki Kimura |e verfasserin |4 aut | |
700 | 0 | |a Gen Inoue |e verfasserin |4 aut | |
700 | 0 | |a Yoshifumi Tsuge |e verfasserin |4 aut | |
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10.1016/j.powera.2022.100084 doi (DE-627)DOAJ018277055 (DE-599)DOAJc4d1f9e8fecb4407a0ba9aee194472cc DE-627 ger DE-627 rakwb eng TP250-261 TK452-454.4 Xuanchen Liu verfasserin aut 3D generation and reconstruction of the fuel cell catalyst layer using 2D images based on deep learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The catalyst layer (CL) being the site of electrochemical reactions, is the core subunit of the membrane electrode assembly (MEA) in polymer electrolyte fuel cells (PEFCs). Thus, the porous structure of the CL has a significant influence on oxygen transfer resistance and affects the charge/discharge performance. In this study, the three-dimensional (3D) porous structure of the catalyst layer is reconstructed based on the deep convolutional generative adversarial network (DCGAN) deep learning method, utilizing focused ion beam scanning electron microscopy (FIB-SEM) microstructure graphs as training data. Each set of spatial-continuous microstructure graphs, generated by DCGAN with interpolation in latent space, is applied to build a unique 3D microstructure of the CL without the use of real FIB-SEM data. Meanwhile, distinct interpolation conditions in the DCGAN are discussed to optimize the ultimate structure by approaching the structural information to real data, including that of porosity, particle size distribution, and tortuosity. Moreover, the comparison of real and generated structural data reveal that the data generated by DCGAN shows an adjacency relationship with real data, indicating its potential applicability in the field of electrochemical simulation with reduced situational costs. Polymer electrolyte fuel cells Catalyst layer Simulation Deep learning 3D reconstruction Microstructure generation Industrial electrochemistry Electric apparatus and materials. Electric circuits. Electric networks Kayoung Park verfasserin aut Magnus So verfasserin aut Shota Ishikawa verfasserin aut Takeshi Terao verfasserin aut Kazuhiko Shinohara verfasserin aut Chiyuri Komori verfasserin aut Naoki Kimura verfasserin aut Gen Inoue verfasserin aut Yoshifumi Tsuge verfasserin aut In Journal of Power Sources Advances Elsevier, 2020 14(2022), Seite 100084- (DE-627)1698593228 26662485 nnns volume:14 year:2022 pages:100084- https://doi.org/10.1016/j.powera.2022.100084 kostenfrei https://doaj.org/article/c4d1f9e8fecb4407a0ba9aee194472cc kostenfrei http://www.sciencedirect.com/science/article/pii/S2666248522000026 kostenfrei https://doaj.org/toc/2666-2485 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 14 2022 100084- |
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10.1016/j.powera.2022.100084 doi (DE-627)DOAJ018277055 (DE-599)DOAJc4d1f9e8fecb4407a0ba9aee194472cc DE-627 ger DE-627 rakwb eng TP250-261 TK452-454.4 Xuanchen Liu verfasserin aut 3D generation and reconstruction of the fuel cell catalyst layer using 2D images based on deep learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The catalyst layer (CL) being the site of electrochemical reactions, is the core subunit of the membrane electrode assembly (MEA) in polymer electrolyte fuel cells (PEFCs). Thus, the porous structure of the CL has a significant influence on oxygen transfer resistance and affects the charge/discharge performance. In this study, the three-dimensional (3D) porous structure of the catalyst layer is reconstructed based on the deep convolutional generative adversarial network (DCGAN) deep learning method, utilizing focused ion beam scanning electron microscopy (FIB-SEM) microstructure graphs as training data. Each set of spatial-continuous microstructure graphs, generated by DCGAN with interpolation in latent space, is applied to build a unique 3D microstructure of the CL without the use of real FIB-SEM data. Meanwhile, distinct interpolation conditions in the DCGAN are discussed to optimize the ultimate structure by approaching the structural information to real data, including that of porosity, particle size distribution, and tortuosity. Moreover, the comparison of real and generated structural data reveal that the data generated by DCGAN shows an adjacency relationship with real data, indicating its potential applicability in the field of electrochemical simulation with reduced situational costs. Polymer electrolyte fuel cells Catalyst layer Simulation Deep learning 3D reconstruction Microstructure generation Industrial electrochemistry Electric apparatus and materials. Electric circuits. Electric networks Kayoung Park verfasserin aut Magnus So verfasserin aut Shota Ishikawa verfasserin aut Takeshi Terao verfasserin aut Kazuhiko Shinohara verfasserin aut Chiyuri Komori verfasserin aut Naoki Kimura verfasserin aut Gen Inoue verfasserin aut Yoshifumi Tsuge verfasserin aut In Journal of Power Sources Advances Elsevier, 2020 14(2022), Seite 100084- (DE-627)1698593228 26662485 nnns volume:14 year:2022 pages:100084- https://doi.org/10.1016/j.powera.2022.100084 kostenfrei https://doaj.org/article/c4d1f9e8fecb4407a0ba9aee194472cc kostenfrei http://www.sciencedirect.com/science/article/pii/S2666248522000026 kostenfrei https://doaj.org/toc/2666-2485 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 14 2022 100084- |
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10.1016/j.powera.2022.100084 doi (DE-627)DOAJ018277055 (DE-599)DOAJc4d1f9e8fecb4407a0ba9aee194472cc DE-627 ger DE-627 rakwb eng TP250-261 TK452-454.4 Xuanchen Liu verfasserin aut 3D generation and reconstruction of the fuel cell catalyst layer using 2D images based on deep learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The catalyst layer (CL) being the site of electrochemical reactions, is the core subunit of the membrane electrode assembly (MEA) in polymer electrolyte fuel cells (PEFCs). Thus, the porous structure of the CL has a significant influence on oxygen transfer resistance and affects the charge/discharge performance. In this study, the three-dimensional (3D) porous structure of the catalyst layer is reconstructed based on the deep convolutional generative adversarial network (DCGAN) deep learning method, utilizing focused ion beam scanning electron microscopy (FIB-SEM) microstructure graphs as training data. Each set of spatial-continuous microstructure graphs, generated by DCGAN with interpolation in latent space, is applied to build a unique 3D microstructure of the CL without the use of real FIB-SEM data. Meanwhile, distinct interpolation conditions in the DCGAN are discussed to optimize the ultimate structure by approaching the structural information to real data, including that of porosity, particle size distribution, and tortuosity. Moreover, the comparison of real and generated structural data reveal that the data generated by DCGAN shows an adjacency relationship with real data, indicating its potential applicability in the field of electrochemical simulation with reduced situational costs. Polymer electrolyte fuel cells Catalyst layer Simulation Deep learning 3D reconstruction Microstructure generation Industrial electrochemistry Electric apparatus and materials. Electric circuits. Electric networks Kayoung Park verfasserin aut Magnus So verfasserin aut Shota Ishikawa verfasserin aut Takeshi Terao verfasserin aut Kazuhiko Shinohara verfasserin aut Chiyuri Komori verfasserin aut Naoki Kimura verfasserin aut Gen Inoue verfasserin aut Yoshifumi Tsuge verfasserin aut In Journal of Power Sources Advances Elsevier, 2020 14(2022), Seite 100084- (DE-627)1698593228 26662485 nnns volume:14 year:2022 pages:100084- https://doi.org/10.1016/j.powera.2022.100084 kostenfrei https://doaj.org/article/c4d1f9e8fecb4407a0ba9aee194472cc kostenfrei http://www.sciencedirect.com/science/article/pii/S2666248522000026 kostenfrei https://doaj.org/toc/2666-2485 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 14 2022 100084- |
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10.1016/j.powera.2022.100084 doi (DE-627)DOAJ018277055 (DE-599)DOAJc4d1f9e8fecb4407a0ba9aee194472cc DE-627 ger DE-627 rakwb eng TP250-261 TK452-454.4 Xuanchen Liu verfasserin aut 3D generation and reconstruction of the fuel cell catalyst layer using 2D images based on deep learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The catalyst layer (CL) being the site of electrochemical reactions, is the core subunit of the membrane electrode assembly (MEA) in polymer electrolyte fuel cells (PEFCs). Thus, the porous structure of the CL has a significant influence on oxygen transfer resistance and affects the charge/discharge performance. In this study, the three-dimensional (3D) porous structure of the catalyst layer is reconstructed based on the deep convolutional generative adversarial network (DCGAN) deep learning method, utilizing focused ion beam scanning electron microscopy (FIB-SEM) microstructure graphs as training data. Each set of spatial-continuous microstructure graphs, generated by DCGAN with interpolation in latent space, is applied to build a unique 3D microstructure of the CL without the use of real FIB-SEM data. Meanwhile, distinct interpolation conditions in the DCGAN are discussed to optimize the ultimate structure by approaching the structural information to real data, including that of porosity, particle size distribution, and tortuosity. Moreover, the comparison of real and generated structural data reveal that the data generated by DCGAN shows an adjacency relationship with real data, indicating its potential applicability in the field of electrochemical simulation with reduced situational costs. Polymer electrolyte fuel cells Catalyst layer Simulation Deep learning 3D reconstruction Microstructure generation Industrial electrochemistry Electric apparatus and materials. Electric circuits. Electric networks Kayoung Park verfasserin aut Magnus So verfasserin aut Shota Ishikawa verfasserin aut Takeshi Terao verfasserin aut Kazuhiko Shinohara verfasserin aut Chiyuri Komori verfasserin aut Naoki Kimura verfasserin aut Gen Inoue verfasserin aut Yoshifumi Tsuge verfasserin aut In Journal of Power Sources Advances Elsevier, 2020 14(2022), Seite 100084- (DE-627)1698593228 26662485 nnns volume:14 year:2022 pages:100084- https://doi.org/10.1016/j.powera.2022.100084 kostenfrei https://doaj.org/article/c4d1f9e8fecb4407a0ba9aee194472cc kostenfrei http://www.sciencedirect.com/science/article/pii/S2666248522000026 kostenfrei https://doaj.org/toc/2666-2485 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 14 2022 100084- |
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10.1016/j.powera.2022.100084 doi (DE-627)DOAJ018277055 (DE-599)DOAJc4d1f9e8fecb4407a0ba9aee194472cc DE-627 ger DE-627 rakwb eng TP250-261 TK452-454.4 Xuanchen Liu verfasserin aut 3D generation and reconstruction of the fuel cell catalyst layer using 2D images based on deep learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The catalyst layer (CL) being the site of electrochemical reactions, is the core subunit of the membrane electrode assembly (MEA) in polymer electrolyte fuel cells (PEFCs). Thus, the porous structure of the CL has a significant influence on oxygen transfer resistance and affects the charge/discharge performance. In this study, the three-dimensional (3D) porous structure of the catalyst layer is reconstructed based on the deep convolutional generative adversarial network (DCGAN) deep learning method, utilizing focused ion beam scanning electron microscopy (FIB-SEM) microstructure graphs as training data. Each set of spatial-continuous microstructure graphs, generated by DCGAN with interpolation in latent space, is applied to build a unique 3D microstructure of the CL without the use of real FIB-SEM data. Meanwhile, distinct interpolation conditions in the DCGAN are discussed to optimize the ultimate structure by approaching the structural information to real data, including that of porosity, particle size distribution, and tortuosity. Moreover, the comparison of real and generated structural data reveal that the data generated by DCGAN shows an adjacency relationship with real data, indicating its potential applicability in the field of electrochemical simulation with reduced situational costs. Polymer electrolyte fuel cells Catalyst layer Simulation Deep learning 3D reconstruction Microstructure generation Industrial electrochemistry Electric apparatus and materials. Electric circuits. Electric networks Kayoung Park verfasserin aut Magnus So verfasserin aut Shota Ishikawa verfasserin aut Takeshi Terao verfasserin aut Kazuhiko Shinohara verfasserin aut Chiyuri Komori verfasserin aut Naoki Kimura verfasserin aut Gen Inoue verfasserin aut Yoshifumi Tsuge verfasserin aut In Journal of Power Sources Advances Elsevier, 2020 14(2022), Seite 100084- (DE-627)1698593228 26662485 nnns volume:14 year:2022 pages:100084- https://doi.org/10.1016/j.powera.2022.100084 kostenfrei https://doaj.org/article/c4d1f9e8fecb4407a0ba9aee194472cc kostenfrei http://www.sciencedirect.com/science/article/pii/S2666248522000026 kostenfrei https://doaj.org/toc/2666-2485 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 14 2022 100084- |
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Xuanchen Liu |
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Xuanchen Liu misc TP250-261 misc TK452-454.4 misc Polymer electrolyte fuel cells misc Catalyst layer misc Simulation misc Deep learning misc 3D reconstruction misc Microstructure generation misc Industrial electrochemistry misc Electric apparatus and materials. Electric circuits. Electric networks 3D generation and reconstruction of the fuel cell catalyst layer using 2D images based on deep learning |
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TP250-261 TK452-454.4 3D generation and reconstruction of the fuel cell catalyst layer using 2D images based on deep learning Polymer electrolyte fuel cells Catalyst layer Simulation Deep learning 3D reconstruction Microstructure generation |
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3D generation and reconstruction of the fuel cell catalyst layer using 2D images based on deep learning |
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3D generation and reconstruction of the fuel cell catalyst layer using 2D images based on deep learning |
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Xuanchen Liu Kayoung Park Magnus So Shota Ishikawa Takeshi Terao Kazuhiko Shinohara Chiyuri Komori Naoki Kimura Gen Inoue Yoshifumi Tsuge |
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3d generation and reconstruction of the fuel cell catalyst layer using 2d images based on deep learning |
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3D generation and reconstruction of the fuel cell catalyst layer using 2D images based on deep learning |
abstract |
The catalyst layer (CL) being the site of electrochemical reactions, is the core subunit of the membrane electrode assembly (MEA) in polymer electrolyte fuel cells (PEFCs). Thus, the porous structure of the CL has a significant influence on oxygen transfer resistance and affects the charge/discharge performance. In this study, the three-dimensional (3D) porous structure of the catalyst layer is reconstructed based on the deep convolutional generative adversarial network (DCGAN) deep learning method, utilizing focused ion beam scanning electron microscopy (FIB-SEM) microstructure graphs as training data. Each set of spatial-continuous microstructure graphs, generated by DCGAN with interpolation in latent space, is applied to build a unique 3D microstructure of the CL without the use of real FIB-SEM data. Meanwhile, distinct interpolation conditions in the DCGAN are discussed to optimize the ultimate structure by approaching the structural information to real data, including that of porosity, particle size distribution, and tortuosity. Moreover, the comparison of real and generated structural data reveal that the data generated by DCGAN shows an adjacency relationship with real data, indicating its potential applicability in the field of electrochemical simulation with reduced situational costs. |
abstractGer |
The catalyst layer (CL) being the site of electrochemical reactions, is the core subunit of the membrane electrode assembly (MEA) in polymer electrolyte fuel cells (PEFCs). Thus, the porous structure of the CL has a significant influence on oxygen transfer resistance and affects the charge/discharge performance. In this study, the three-dimensional (3D) porous structure of the catalyst layer is reconstructed based on the deep convolutional generative adversarial network (DCGAN) deep learning method, utilizing focused ion beam scanning electron microscopy (FIB-SEM) microstructure graphs as training data. Each set of spatial-continuous microstructure graphs, generated by DCGAN with interpolation in latent space, is applied to build a unique 3D microstructure of the CL without the use of real FIB-SEM data. Meanwhile, distinct interpolation conditions in the DCGAN are discussed to optimize the ultimate structure by approaching the structural information to real data, including that of porosity, particle size distribution, and tortuosity. Moreover, the comparison of real and generated structural data reveal that the data generated by DCGAN shows an adjacency relationship with real data, indicating its potential applicability in the field of electrochemical simulation with reduced situational costs. |
abstract_unstemmed |
The catalyst layer (CL) being the site of electrochemical reactions, is the core subunit of the membrane electrode assembly (MEA) in polymer electrolyte fuel cells (PEFCs). Thus, the porous structure of the CL has a significant influence on oxygen transfer resistance and affects the charge/discharge performance. In this study, the three-dimensional (3D) porous structure of the catalyst layer is reconstructed based on the deep convolutional generative adversarial network (DCGAN) deep learning method, utilizing focused ion beam scanning electron microscopy (FIB-SEM) microstructure graphs as training data. Each set of spatial-continuous microstructure graphs, generated by DCGAN with interpolation in latent space, is applied to build a unique 3D microstructure of the CL without the use of real FIB-SEM data. Meanwhile, distinct interpolation conditions in the DCGAN are discussed to optimize the ultimate structure by approaching the structural information to real data, including that of porosity, particle size distribution, and tortuosity. Moreover, the comparison of real and generated structural data reveal that the data generated by DCGAN shows an adjacency relationship with real data, indicating its potential applicability in the field of electrochemical simulation with reduced situational costs. |
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title_short |
3D generation and reconstruction of the fuel cell catalyst layer using 2D images based on deep learning |
url |
https://doi.org/10.1016/j.powera.2022.100084 https://doaj.org/article/c4d1f9e8fecb4407a0ba9aee194472cc http://www.sciencedirect.com/science/article/pii/S2666248522000026 https://doaj.org/toc/2666-2485 |
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