
Journal of CO2 Utilization, Год журнала: 2025, Номер 97, С. 103106 - 103106
Опубликована: Май 12, 2025
Язык: Английский
Journal of CO2 Utilization, Год журнала: 2025, Номер 97, С. 103106 - 103106
Опубликована: Май 12, 2025
Язык: Английский
Rare Metals, Год журнала: 2025, Номер unknown
Опубликована: Март 21, 2025
Язык: Английский
Процитировано
2Advanced Energy Materials, Год журнала: 2025, Номер unknown
Опубликована: Фев. 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
Язык: Английский
Процитировано
0Journal of CO2 Utilization, Год журнала: 2025, Номер 97, С. 103106 - 103106
Опубликована: Май 12, 2025
Язык: Английский
Процитировано
0