Machine Learning Exploration of Experimental Conditions for Optimized Electrochemical CO2 Reduction

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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Bibliographic Details
Main Authors: Vuri Ayu Setyowati, Shiho Mukaida, Kaito Nagita, Takashi Harada, Shuji Nakanishi, Kazuyuki Iwase
Format: Article
Language:English
Published: Wiley-VCH 2024-12-01
Series:ChemElectroChem
Subjects:
CO2 electrolysis
Product selectivity
Machine learning
Inverse analysis
Gas-diffusion electrodes
Online Access:https://doi.org/10.1002/celc.202400518
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