Work

Data-driven and interpretable machine learning for performance-determining interactions governing C–C coupling and C 2 + selectivity in Cu-catalyzed CO 2 electroreduction

Year: 2026

Type: article

Source: Physical Chemistry Chemical Physics

DOI: 10.1039/d6cp00893c

Authors: Muhammad Asif, Luqman Hakeem, Chengxi Yao

Institutions: University of Tsukuba, Centre National de la Recherche Scientifique, Sorbonne Université, Laboratoire de Chimie Physique - Matière et Rayonnement, Sungkyunkwan University

Language: English

Topic: CO2 Reduction Techniques and Catalysts

Subfield: Renewable Energy, Sustainability and the Environment

Field: Energy

Domain: Physical Sciences

Open Access status: closed

OpenAlex ID: W7160933352

FWCI: 4.373

Cites: 38

Cited by: 5

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