The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Semiconductor Nanowires for Artificial Photosynthesis
Open accessAbstract
In this Perspective, we discuss current challenges in artificial photosynthesis research, with a focus on the benefits of a nanowire morphology. Matching the flux between electrocatalysts and light-absorbers, and between individual semiconducting light-absorbers, are two major issues to design economically viable devices for artificial photosynthesis. With the knowledge that natural photosynthesis is an integrated nanosystem, individual building blocks of biomimetic artificial photosynthesis are discussed. Possible research directions are presented under an integrated device design scheme, with examples of our current progress in these areas. Coupling all of the components together, including electrocatalysts, light-absorbers, and charge transport units, is crucial due to both fundamental and practical considerations. Given the advantages of one-dimensional nanostructures, it is evident that semiconductor nanowires can function as essential building blocks and help to solve many of the issues in artificial photosynthesis.
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Cites
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Self-citationTheoretical Insights into Heterogeneous (Photo)electrochemical CO2Reduction
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New BiVO4 Dual Photoanodes with Enriched Oxygen Vacancies for Efficient Solar‐Driven Water Splitting
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Bandgap engineering in semiconductor alloy nanomaterials with widely tunable compositions
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Self-citationSub-particle reaction and photocurrent mapping to optimize catalyst-modified photoanodes
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Metadata
- Authors
- Liu, C., Dasgupta, N. P., & Yang, P.
- Venue
- Chemistry of Materials
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-10-02
- Volume
- 26
- Issue
- 1
- Pages
- 415-422
- ISSN
- 0897-4756 (ISSN-L)
- 1520-5002
- OpenAlex
- W2153000479
- Cited by
- 366
- References
- 9
- FWCI
- 13.95
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
