催化作用
制氢
光催化
异质结
可见光谱
氢
材料科学
带隙
铂金
光化学
纳米技术
化学
光电子学
生物化学
有机化学
作者
Guo‐Wei Guan,Yitao Li,Liping Zhang,Su‐Tao Zheng,Si‐Chao Liu,Hao-Ling Lan,Qing‐Yuan Yang
标识
DOI:10.1016/j.gce.2024.03.002
摘要
Solar-powered water splitting is an up-and-coming method for hydrogen production. Still, it faces several challenges, including improving light responsiveness, maximizing utilization of photocatalyst active sites, and effectively utilizing photo-induced carriers to prevent low hydrogen production. In this research, we propose an approach for designing a 2D/2D heterostructure catalyst, the Cd-TCPP(Pt)@CdS, which consists of 2D CdS nanosheets (NSs) and a 2D metal-organic framework (MOF) with Pt active sites (Cd-TCPP(Pt)), aiming to achieve highly efficient visible-light-driven hydrogen evolution. Firstly, CdS NSs exhibit excellent responsiveness to visible light, ensuring robust generation of photo-induced carriers. Secondly, the 2D MOF provides abundant Pt active sites, enhancing electron utilization and reducing the energy barrier for proton reduction. Compared to pure CdS NSs (which demonstrate a hydrogen production activity of 1220 μmol/g/h), the newly designed 2D/2D composite catalyst Cd-TCPP(Pt)@CdS exhibits an activity of 13,434 μmol/g/h, representing an 11-fold increase. Impressively, Cd-TCPP(Pt)@CdS maintains a high activity of 3062 μmol/g/h even under sunlight. Density functional theory (DFT) calculations were employed to investigate the principle of proton reduction. The suitable bandgap of CdS and energy gap of 2D Cd-TCPP(Pt) contribute to their strong interaction and consequently higher efficiency in hydrogen evolution. The Pt-single atom (Pt-SA) also provides sites with low free energy for proton reduction, contributing to improved activity. The photocatalytic performance of Cd-TCPP(Pt)@CdS NSs composites demonstrates a synergistic effect between the 2D inorganic semiconductor and the 2D MOF containing the Pt-site, resulting in enhanced utilization of photo-induced carriers and atoms.
科研通智能强力驱动
Strongly Powered by AbleSci AI