Emerging Strategies for CO 2 Photoreduction to CH 4 : From Experimental to Data‐Driven Design

Abstract The solar‐energy‐driven photoreduction of CO 2 has recently emerged as a promising approach to directly transform CO 2 into valuable energy sources under mild conditions. As a clean‐burning fuel and drop‐in replacement for natural gas, CH 4 is an ideal product of CO 2 photoreduction, but th...

Full description

Saved in:
Bibliographic Details
Published in:Advanced energy materials 2022-05, Vol.12 (20)
Main Authors: Cheng, Shuwen, Sun, Zhehao, Lim, Kang Hui, Gani, Terry Zhi Hao, Zhang, Tianxi, Wang, Yisong, Yin, Hang, Liu, Kaili, Guo, Haiwei, Du, Tao, Liu, Liying, Li, Gang Kevin, Yin, Zongyou, Kawi, Sibudjing
Format: Article
Language:English
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract The solar‐energy‐driven photoreduction of CO 2 has recently emerged as a promising approach to directly transform CO 2 into valuable energy sources under mild conditions. As a clean‐burning fuel and drop‐in replacement for natural gas, CH 4 is an ideal product of CO 2 photoreduction, but the development of highly active and selective semiconductor‐based photocatalysts for this important transformation remains challenging. Hence, significant efforts have been made in the search for active, selective, stable, and sustainable photocatalysts. In this review, recent applications of cutting‐edge experimental and computational materials design strategies toward the discovery of novel catalysts for CO 2 photocatalytic conversion to CH 4 are systematically summarized. First, insights into effective experimental catalyst engineering strategies, including heterojunctions, defect engineering, cocatalysts, surface modification, facet engineering, and single atoms, are presented. Then, data‐driven photocatalyst design spanning density functional theory (DFT) simulations, high‐throughput computational screening, and machine learning (ML) is presented through a step‐by‐step introduction. The combination of DFT, ML, and experiments is emphasized as a powerful solution for accelerating the discovery of novel catalysts for photocatalytic reduction of CO 2 . Last, challenges and perspectives concerning the interplay between experiments and data‐driven rational design strategies for the industrialization of large‐scale CO 2 photoreduction technologies are described.
ISSN:1614-6832
1614-6840
DOI:10.1002/aenm.202200389