Accelerated screening of gas diffusion electrodes for carbon dioxide reduction†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-04-30 DOI:10.1039/D4DD00061G
Ryan J. R. Jones, Yungchieh Lai, Dan Guevarra, Kevin Kan, Joel A. Haber and John M. Gregoire
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Abstract

The electrochemical conversion of carbon dioxide to chemicals and fuels is expected to be a key sustainability technology. Electrochemical carbon dioxide reduction technologies are challenged by several factors, including the limited solubility of carbon dioxide in aqueous electrolyte as well as the difficulty in utilizing polymer electrolytes. These considerations have driven system designs to incorporate gas diffusion electrodes (GDEs) to bring the electrocatalyst in contact with both a gaseous reactant/product stream as well as a liquid electrolyte. GDE optimization typically results from manual tuning by select experts. Automated preparation and operation of GDE cells could be a watershed for the systematic study of, and ultimately the development of a materials acceleration platform (MAP) for, catalyst discovery and system optimization. Toward this end, we present the automated GDE (AutoGDE) testing system. Given a catalyst-coated GDE, AutoGDE automates the insertion of the GDE into an electrochemical cell, the liquid and gas handling, the quantification of gaseous reaction products via online mass spectroscopy, and the archiving of the liquid electrolyte for subsequent analysis.

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加速筛选用于二氧化碳还原的气体扩散电极
电化学将二氧化碳转化为化学品和燃料有望成为一项关键的可持续发展技术。电化学二氧化碳还原技术面临着多种因素的挑战,包括二氧化碳在水性电解质中的溶解度有限以及难以使用聚合物电解质。这些因素促使系统设计采用气体扩散电极 (GDE),使电催化剂同时与气态反应物/产物流和液态电解质接触。气体扩散电极的优化通常是由选定的专家进行手动调整。GDE 单元的自动制备和操作可以成为系统研究的分水岭,并最终开发出用于催化剂发现和系统优化的材料加速平台 (MAP)。为此,我们推出了自动 GDE(AutoGDE)测试系统。给定一个催化剂涂层 GDE,AutoGDE 可自动将 GDE 插入电化学电池、处理液体和气体、通过在线质谱对气态反应产物进行定量,以及将液体电解质存档以备后续分析。
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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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