Suppr超能文献

具有可调带隙的二维锗硅烯用于光催化析氢和将 CO 光还原为 CO。

Two-dimensional gersiloxenes with tunable bandgap for photocatalytic H evolution and CO photoreduction to CO.

作者信息

Zhao Fulai, Feng Yiyu, Wang Yu, Zhang Xin, Liang Xuejing, Li Zhen, Zhang Fei, Wang Tuo, Gong Jinlong, Feng Wei

机构信息

School of Materials Science and Engineering, Tianjin University, Tianjin Key Laboratory of Composite and Functional Materials, Tianjin, 300072, P. R. China.

Key Laboratory of Advanced Ceramics and Machining Technology, Ministry of Education, Tianjin, 300072, P. R. China.

出版信息

Nat Commun. 2020 Mar 19;11(1):1443. doi: 10.1038/s41467-020-15262-4.

Abstract

The discovery of graphene and graphene-like two-dimensional materials has brought fresh vitality to the field of photocatalysis. Bandgap engineering has always been an effective way to make semiconductors more suitable for specific applications such as photocatalysis and optoelectronics. Achieving control over the bandgap helps to improve the light absorption capacity of the semiconductor materials, thereby improving the photocatalytic performance. This work reports two-dimensional -H/-OH terminal-substituted siligenes (gersiloxenes) with tunable bandgap. All gersiloxenes are direct-gap semiconductors and have wide range of light absorption and suitable band positions for light driven water reduction into H, and CO reduction to CO under mild conditions. The gersiloxene with the best performance can provide a maximum CO production of 6.91 mmol g h, and a high apparent quantum efficiency (AQE) of 5.95% at 420 nm. This work may open up new insights into the discovery, research and application of new two-dimensional materials in photocatalysis.

摘要

石墨烯及类石墨烯二维材料的发现为光催化领域带来了新的活力。带隙工程一直是使半导体更适用于光催化和光电子学等特定应用的有效方法。实现对带隙的控制有助于提高半导体材料的光吸收能力,从而提高光催化性能。这项工作报道了具有可调带隙的二维-H/-OH端基取代硅烯(硅锗烯)。所有硅锗烯都是直接带隙半导体,具有广泛的光吸收范围以及适合在温和条件下光驱动水还原为H₂和CO₂还原为CO的能带位置。性能最佳的硅锗烯在420 nm处可实现最大CO产量为6.91 mmol g⁻¹ h⁻¹,以及5.95%的高表观量子效率(AQE)。这项工作可能为光催化中新型二维材料的发现、研究和应用开辟新的思路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c9c/7081354/34cd01d02a59/41467_2020_15262_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验