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Robust and synthesizable photocatalysts for CO2 reduction: a data-driven materials discovery
The photocatalytic conversion of the greenhouse gas CO 2 to chemical fuels such as hydrocarbons and alcohols continues to be a promising technology for renewable generation of energy. Major advancements have been made in improving the efficiencies and product selectiveness of currently known CO 2 re...
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Published in: | Nature communications 2019-01, Vol.10 (1), p.443-443, Article 443 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | The photocatalytic conversion of the greenhouse gas CO
2
to chemical fuels such as hydrocarbons and alcohols continues to be a promising technology for renewable generation of energy. Major advancements have been made in improving the efficiencies and product selectiveness of currently known CO
2
reduction electrocatalysts, nonetheless, materials discovery is needed to enable economically viable, industrial-scale CO
2
reduction. We report here the largest CO
2
photocathode search to date, starting with 68860 candidate materials, using a rational first-principles computation-based screening strategy to evaluate synthesizability, corrosion resistance, visible-light absorption, and compatibility of the electronic structure with fuel synthesis. The results confirm the observation of the literature that few materials meet the stringent CO
2
photocathode requirements, with only 52 materials meeting all requirements. The results are well validated with respect to the literature, with 9 of these materials having been studied for CO
2
reduction, and the remaining 43 materials are discoveries from our pipeline that merit further investigation.
While the conversion of greenhouse CO
2
to chemical fuels offers a promising renewable energy technology, there is a dire need for new materials. Here, authors report the largest CO
2
photocathode search using a first-principles approach to identify both known and unreported candidate photocatalysts. |
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ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/s41467-019-08356-1 |