Emerging Strategies for CO2 Photoreduction to CH4: From Experimental to Data‐Driven Design (Adv. Energy Mater. 20/2022). Issue 20 (26th May 2022)

• Record Type: Journal Article
• 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
• Is Part Of: Advanced energy materials. Volume 12:Issue 20(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-26
• Subjects: activity -- catalytic engineering -- CH 4 selectivity -- CO 2 photoreduction -- data‐driven strategies -- high‐throughput screening -- photocatalysts; Energy harvesting -- Materials -- Periodicals; Energy conversion -- Materials -- Periodicals; Energy storage -- Materials -- Periodicals; Photovoltaics -- Periodicals; Fuel cells -- Periodicals; Thermoelectric materials -- Periodicals; 621.31
• DOI: 10.1002/aenm.202270079
• Languages: English
• ISSNs: 1614-6832

Abstract

Abstract : Data Driven Strategies Through experimental strategies (human brain) and data‐driven strategies (machine brain) that have been reported, high‐performance photocatalysts are gradually being explored and developed under the huge data flow (transmitted by neurons). In article number 2200389, Gang Kevin Li, Zongyou Yin, Sibudjing Kawi and co‐workers show that the combination of experimentation and data‐driven design (brain ensemble) is a powerful solution for the future design of novel catalysts for selective photocatalytic reduction of carbon dioxide to methane.