Efficient mapping of CO adsorption on Cu1−xMx bimetallic alloys via machine learning-Reference-Cited by-同舟云学术

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Efficient mapping of CO adsorption on Cu_1−xM_x bimetallic alloys via machine learning

Published:2024 Issue:23 Volume:12 Page:14148-14158
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Salomone Mattia¹^ORCID,Re Fiorentin Michele¹^ORCID,Risplendi Francesca¹^ORCID,Raffone Federico¹^ORCID,Sommer Timo²^ORCID,García-Melchor Max²^ORCID,Cicero Giancarlo¹

Affiliation:

1. Dipartimento di Scienza Applicata e Tecnologia, Politecnico di Torino, 10129 Torino, Italy

2. CRANN and AMBER Research Centres, School of Chemistry, Trinity College Dublin, College Green, Dublin 2, Ireland

Abstract

A two-step machine learning model to predict CO binding energies on CuM(111)/(100) bimetallic surfaces and enhance the CO2RR selectivity towards C2 products.

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/TA/D3TA06915J

Reference88 articles.

1. Net-zero emissions energy systems

2. The role of CO2 capture and utilization in mitigating climate change

3. High Rate, Selective, and Stable Electroreduction of CO2 to CO in Basic and Neutral Media

4. Insights into the Low Overpotential Electroreduction of CO2 to CO on a Supported Gold Catalyst in an Alkaline Flow Electrolyzer

5. Isolated Ni single atoms in graphene nanosheets for high-performance CO2 reduction

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