Deep learning-based structural characterization and mass transport analysis of CO₂ reduction catalyst layers

Abstract

Electrochemical CO₂ reduction (CO₂RR) is a promising technology for mitigating global climate change. The catalyst layer (CL), where the reduction reaction occurs, plays a pivotal role in determining mass transport and electrochemical performance. However, accurately characterizing local structures and quantifying mass transport remains a significant challenge. To address these limitations, a systematic characterization framework based on deep learning (DL) is proposed. Five semantic segmentation models, including Segformer and DeepLabV3plus, were compared with conventional image processing techniques, among which DeepLabV3plus achieved the highest segmentation accuracy (> 91.29%), significantly outperforming traditional thresholding methods (72.35%–77.42%). Experimental validation via mercury intrusion porosimetry (MIP) confirmed its capability to precisely extract key structural parameters, such as porosity and pore size distribution. Furthermore, a series of ionomer content gradient experiments revealed that a CL with an ionomer/catalyst (I/C) ratio of 0.2 had the optimal pore network structure. Numerical simulations and electrochemical tests demonstrated that this CL enabled a twofold increase in gas diffusion distance, thereby promoting long-range mass transport and significantly enhancing CO production rates. This work establishes a multi-scale analysis framework integrating “structural characterization, mass transport simulation, and performance validation,” offering both theoretical insights and practical guidance for the rational design of CO₂RR CLs.

Graphical abstract

Keywords

carbon dioxide reduction / deep learning (DL) / semantic segmentation / catalyst layer (CL) / mass transport

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