The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Self-photosensitization of nonphotosynthetic bacteria for solar-to-chemical production
Abstract
Using light in the darkness: Solid-state devices can efficiently capture solar energy to produce chemicals and fuels from carbon dioxide. Yet biology has already developed a high-specificity, low-cost system to do just that through photosynthesis. Sakimoto et al. developed a biological-inorganic hybrid that combines the best of both worlds (see the Perspective by Müller). They precipitated semiconductor nanoparticles on the surface of a nonphotosynthetic bacterium to serve as a light harvester. The captured energy sustained cellular metabolism, producing acetic acid: a natural waste product of respiration. Science , this issue p. 74 ; see also p. 34
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Cites
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Cited by
Interfacing nature’s catalytic machinery with synthetic materials for semi-artificial photosynthesis
Kornienko, N., Zhang, J. Z., Sakimoto, K. K., Yang, P., & Reisner, E. (2018) · Nature Nanotechnology · doi:10.1038/s41565-018-0251-7 · Cited by 533
Self-citationSemiconductor Quantum Dots: An Emerging Candidate for CO2 Photoreduction
Wu, H., Li, X., Tung, C., & Wu, L. (2019) · Advanced Materials · doi:10.1002/adma.201900709 · Cited by 486
Liew, F., Martin, M. E., Tappel, R. C., Heijstra, B. D., Mihalcea, C., & Köpke, M. (2016) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.00694 · Cited by 485
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Metadata
- Authors
- Sakimoto, K. K., Wong, A. B., & Yang, P.
- Venue
- Science
- Published
- 2016-01
- Volume
- 351
- Issue
- 6268
- Pages
- 74-77
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2204347736
- Cited by
- 1,162
- References
- 32
- FWCI
- 18.96
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
