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Cited by 395

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Chemistry of Personalized Solar Energy

Open access
Nocera, Daniel G.·2009·Inorganic Chemistry

Abstract

Personalized energy (PE) is a transformative idea that provides a new modality for the planet's energy future. By providing solar energy to the individual, an energy supply becomes secure and available to people of both legacy and nonlegacy worlds and minimally contributes to an increase in the anthropogenic level of carbon dioxide. Because PE will be possible only if solar energy is available 24 h a day, 7 days a week, the key enabler for solar PE is an inexpensive storage mechanism. HY (Y = halide or OH(-)) splitting is a fuel-forming reaction of sufficient energy density for large-scale solar storage, but the reaction relies on chemical transformations that are not understood at the most basic science level. Critical among these are multielectron transfers that are proton-coupled and involve the activation of bonds in energy-poor substrates. The chemistry of these three italicized areas is developed, and from this platform, discovery paths leading to new hydrohalic acid- and water-splitting catalysts are delineated. The latter water-splitting catalyst captures many of the functional elements of photosynthesis. In doing so, a highly manufacturable and inexpensive method for solar PE storage has been discovered.

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Metadata

Authors
Nocera, D. G.
Venue
Inorganic Chemistry
Published
2009-09-23
Volume
48
Issue
21
Pages
10001-10017
ISSN
  • 0020-1669 (ISSN-L)
  • 1520-510X
OpenAlex
W1560627658
Cited by
395
References
4
FWCI
14.65
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Chemistry of Personalized Solar Energy. https://app.cochat.ai/papers/10.1021/ic901328v
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Nocera, D. G. (2009). Chemistry of Personalized Solar Energy. Inorganic Chemistry. https://doi.org/10.1021/ic901328v

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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.