Dye sensitization of TiO2 relevant to photocatalytic hydrogen generation: Current research trends and prospects

光催化 制氢 纳米技术 分解水 半导体 色素敏化染料 材料科学 氢燃料 光化学 化学 催化作用 光电子学 有机化学 电极 物理化学 电解质
作者
Spandana Gonuguntla,Reddi Kamesh,Ujjwal Pal,Debabrata Chatterjee
出处
期刊:Journal of Photochemistry and Photobiology C-photochemistry Reviews [Elsevier]
卷期号:57: 100621-100621 被引量:5
标识
DOI:10.1016/j.jphotochemrev.2023.100621
摘要

Research over dye-sensitized hydrogen generation using TiO2 semiconductor photocatalysts has gained abiding importance over the past three decades due to its manifold advantages over other photocatalytic systems for the production of clean energy fuels. The single-step excitation of the electrons over the sensitizer molecules anchored at the TiO2 semiconductor serves as a driving source to facilitate the electron effect transfers, thus prompting the visible-driven photocatalytic hydrogen generation activities. Though many review articles that evaluate the performance of such dye-sensitized semiconductor particulate systems are available in the literature, research progress made in the last few years since 2016 is not yet systematically reviewed. In this article, we therefore, systematically review the development of new dye-sensitizers that include metal-free organic dyes, metal-based sensitizers, and donor-bridged-acceptor (D-π-A) type dye-sensitizers, and their performances in sensitization of the TiO2 semiconductor photocatalyst towards visible light driven hydrogen generation through water splitting. It has been chronicled that the aforesaid sensitizers are capable of harvesting a broader part of the solar spectrum, and could achieve photocatalytic H2 production with varying degrees of success. The results discussed in this review afford a significant scope of rationalizating the factors that govern the H2 production activity over the dye-modified TiO2 photocatalyst, and provide a basis for further research towards the realization of high-performing dye-sensitized H2 production photocatalytic system. The prospect of artificial intelligence (AI)-machine learning (ML) based modeling for quicker design and development of dye-sensitized TiO2 based photocatalytic solar to fuel conversion system has been briefly discussed in the article.
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