Shican Wu

Sep 16, 2024 | nature.com | Shican Wu |Wei Liu |Peng Zhang |Zhi-jian Zhao

AbstractLow-cost, efficient catalyst high-throughput screening is crucial for future renewable energy technology. Interpretable machine learning is a powerful method for accelerating catalyst design by extracting physical meaning but faces huge challenges.

Feb 23, 2024 | pubs.rsc.org | Lulu Li |Dongfang Cheng |Shican Wu

Electronic structure modification of SnO2 to accelerate CO2 reduction towards formate† A systematic theoretical study probing the catalytic potential of metal-doped SnO2(110) was conducted. The incorporation of metals such as Zr, Ti, W, V, Hf, and Ge is shown to drive electron transfer to Sn. The increased charge of Sn is injected into anti-bonding orbitals, finely tuning the catalytic activity and reducing the overpotential to −0.34 V.