Competing Activation and Deactivation Mechanisms in Photodoped Bismuth Oxybromide Nanoplates Probed by Single-Molecule Fluorescence Imaging.
Meikun ShenTianben DingJiang LuoChe TanKhalid MahmoodZheyu WangDongyan ZhangRohan MishraMatthew D LewBryce SadtlerPublished in: The journal of physical chemistry letters (2020)
Oxygen vacancies in semiconductor photocatalysts play several competing roles, serving to both enhance light absorption and charge separation of photoexcited carriers as well as act as recombination centers for their deactivation. In this Letter, we show that single-molecule fluorescence imaging of a chemically activated fluorogenic probe can be used to monitor changes in the photocatalytic activity of bismuth oxybromide (BiOBr) nanoplates in situ during the light-induced formation of oxygen vacancies. We observe that the specific activities of individual nanoplates for the photocatalytic reduction of resazurin first increase and then progressively decrease under continuous laser irradiation. Ensemble structural characterization, supported by electronic-structure calculations, shows that irradiation increases the concentration of surface oxygen vacancies in the nanoplates, reduces Bi ions, and creates donor defect levels within the band gap of the semiconductor particles. These combined changes first enhance photocatalytic activity by increasing light absorption at visible wavelengths. However, high concentrations of oxygen vacancies lower the photocatalytic activity both by introducing new relaxation pathways that promote charge recombination before photoexcited electrons can be extracted and by weakening binding of resazurin to the surface of the nanoplates.
Keyphrases
- visible light
- single molecule
- fluorescence imaging
- living cells
- photodynamic therapy
- atomic force microscopy
- dna damage
- quantum dots
- dna repair
- molecular dynamics simulations
- reduced graphene oxide
- room temperature
- highly efficient
- gold nanoparticles
- radiation induced
- liquid chromatography
- binding protein
- convolutional neural network
- solar cells
- deep learning