Machine Learning Exploration of Experimental Conditions for Optimized Electrochemical CO2 Reduction
Abstract Electrochemical CO2 reduction has attracted significant attention as a potential method to close the carbon cycle. In this study, we investigated the impact of the electrode fabrication and electrolysis conditions on the product selectivity of Ag electrocatalysts using a machine learning (M...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2024-12-01
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| Series: | ChemElectroChem |
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| Online Access: | https://doi.org/10.1002/celc.202400518 |
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| author | Vuri Ayu Setyowati Shiho Mukaida Kaito Nagita Takashi Harada Shuji Nakanishi Kazuyuki Iwase |
| author_facet | Vuri Ayu Setyowati Shiho Mukaida Kaito Nagita Takashi Harada Shuji Nakanishi Kazuyuki Iwase |
| author_sort | Vuri Ayu Setyowati |
| collection | DOAJ |
| description | Abstract Electrochemical CO2 reduction has attracted significant attention as a potential method to close the carbon cycle. In this study, we investigated the impact of the electrode fabrication and electrolysis conditions on the product selectivity of Ag electrocatalysts using a machine learning (ML) approach. Specifically, we explored the experimental conditions for obtaining the desired H2/CO mixture ratio with high CO efficiency. Notably, unlike previous ML‐based studies, we used experimental results as training data. This ML‐based approach allowed us to quantitatively assess the effect of experimental parameters on these targets with a reduced number of experimental trials (only 56 experiments). An inverse analysis based on the ML model suggested the optimal experimental conditions for achieving the desired characteristics of the electrolysis system, with the proposed conditions experimentally validated. This study constitutes the first demonstration of optimal experimental conditions for electrochemical CO2 reduction with desired characteristics using the experimental results as training data. |
| format | Article |
| id | doaj-art-1e68b2899d4a4c7688fb0c41ca4e1e4d |
| institution | OA Journals |
| issn | 2196-0216 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | ChemElectroChem |
| spelling | doaj-art-1e68b2899d4a4c7688fb0c41ca4e1e4d2025-08-20T01:56:25ZengWiley-VCHChemElectroChem2196-02162024-12-011124n/an/a10.1002/celc.202400518Machine Learning Exploration of Experimental Conditions for Optimized Electrochemical CO2 ReductionVuri Ayu Setyowati0Shiho Mukaida1Kaito Nagita2Takashi Harada3Shuji Nakanishi4Kazuyuki Iwase5Research Center for Solar Energy Chemistry Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 JapanResearch Center for Solar Energy Chemistry Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 JapanResearch Center for Solar Energy Chemistry Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 JapanResearch Center for Solar Energy Chemistry Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 JapanResearch Center for Solar Energy Chemistry Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 JapanResearch Center for Solar Energy Chemistry Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 JapanAbstract Electrochemical CO2 reduction has attracted significant attention as a potential method to close the carbon cycle. In this study, we investigated the impact of the electrode fabrication and electrolysis conditions on the product selectivity of Ag electrocatalysts using a machine learning (ML) approach. Specifically, we explored the experimental conditions for obtaining the desired H2/CO mixture ratio with high CO efficiency. Notably, unlike previous ML‐based studies, we used experimental results as training data. This ML‐based approach allowed us to quantitatively assess the effect of experimental parameters on these targets with a reduced number of experimental trials (only 56 experiments). An inverse analysis based on the ML model suggested the optimal experimental conditions for achieving the desired characteristics of the electrolysis system, with the proposed conditions experimentally validated. This study constitutes the first demonstration of optimal experimental conditions for electrochemical CO2 reduction with desired characteristics using the experimental results as training data.https://doi.org/10.1002/celc.202400518CO2 electrolysisProduct selectivityMachine learningInverse analysisGas-diffusion electrodes |
| spellingShingle | Vuri Ayu Setyowati Shiho Mukaida Kaito Nagita Takashi Harada Shuji Nakanishi Kazuyuki Iwase Machine Learning Exploration of Experimental Conditions for Optimized Electrochemical CO2 Reduction ChemElectroChem CO2 electrolysis Product selectivity Machine learning Inverse analysis Gas-diffusion electrodes |
| title | Machine Learning Exploration of Experimental Conditions for Optimized Electrochemical CO2 Reduction |
| title_full | Machine Learning Exploration of Experimental Conditions for Optimized Electrochemical CO2 Reduction |
| title_fullStr | Machine Learning Exploration of Experimental Conditions for Optimized Electrochemical CO2 Reduction |
| title_full_unstemmed | Machine Learning Exploration of Experimental Conditions for Optimized Electrochemical CO2 Reduction |
| title_short | Machine Learning Exploration of Experimental Conditions for Optimized Electrochemical CO2 Reduction |
| title_sort | machine learning exploration of experimental conditions for optimized electrochemical co2 reduction |
| topic | CO2 electrolysis Product selectivity Machine learning Inverse analysis Gas-diffusion electrodes |
| url | https://doi.org/10.1002/celc.202400518 |
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