Optimizing photothermal CO <sub>2</sub> reduction through integrated band-division utilization and thermal management structure
Solar-driven photothermal catalytic CO<sub>2</sub> conversion into fuels offers a promising approach to reducing fossil fuel dependence. To enhance the efficiency of photothermal CO<sub>2</sub> reduction, photothermal catalyst design must not only sustain the high temperatures required for the reaction but also effectively utilize the entire solar spectrum. In this study, we present a novel photothermal catalyst architecture BiVO<sub>4</sub>/Bi/BiOCl that surpasses traditional designs by integrating plasmonic metal Bi as the “hot spot” and BiOCl as the thermal insulation layer on the outermost part. This structure realizes thermal management, contributing to maintaining the high temperatures required for the reaction. The BiVO<sub>4</sub>/Bi/BiOCl multi-component system synergistically absorbs the full solar light spectrum and achieves band-division utilization: short- and mid-wavelengths drive reduction and oxidation reactions, respectively, while long-wavelengths induce the photothermal effect. The BiVO<sub>4</sub>/Bi/BiOCl catalyst demonstrates high-efficiency CO<sub>2</sub> conversion performance in an outdoor concentrating system, achieving a CO production rate of 9.5 μmol/h. This work presents a design strategy for functional photothermal catalysts, making them viable candidates for industrial-scale CO<sub>2</sub> conversion processes.
Authors
- Wenhao Jing (ORCID: https://orcid.org/0009-0006-1023-3053)
- Guiwei He (ORCID: https://orcid.org/0009-0005-2023-559X)
- Feng Wang (ORCID: https://orcid.org/0000-0002-0482-3720)
- Liejin Guo (ORCID: https://orcid.org/0009-0003-2257-3109)
- Xue Ding (ORCID: https://orcid.org/0000-0003-1648-1817)
- Z. M. Jiang (ORCID: https://orcid.org/0000-0001-6643-3835)
- Ya Liu (ORCID: https://orcid.org/0009-0005-5866-3178)
Institutions
- Southwest Jiaotong University (CN)
Publication Details
- Journal
- Nano Research
- Published
- 2024-12-16
- DOI
- https://doi.org/10.26599/nr.2025.94907157
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.12