Exploring Challenging C₂₊ Products During CO₂ Reduction via Machine Learning Acceleration

Advanced Energy Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

Abstract Although CO 2 reduction reaction (CO RR) has achieved significant progress in past years, the C 2+ products are mainly limited to a few products, while many other have rarely been reported experiments with understanding of underlying mechanisms. Accordingly, this work, machine learning (ML)‐based theoretical investigations is conducted uncover mechanisms for conversion challenging C₂ + (C H 6 , CH 3 OCH CO, and ) during RR on graphdiyne‐supported atomic catalysts (GDY‐ACs) well‐defined active sites. Using first‐principles (FPML) predictions, key factors limiting diversity identified. The conversions hindered by large rate‐determining step (RDS) barriers (>4 eV). formation meets competitive reactions due similar pathways which also undergoes further hydrogenation easily products. dehydration caused steric hindrance induced neighboring adsorption 1 intermediates. FPML predictions reveal significance binding configuration parameters realizing efficient accurate predictions. This work offers not only important references low selectivity specific but critical insights into

Language: Английский

Atomic-level insights into the synergistic effect between ligands on electrochemical CO2 reduction: based on Au7Ag8 series nanoclusters

Rare Metals, Journal Year: 2025, Volume and Issue: unknown

Published: March 21, 2025

Language: Английский