(Invited) Facet-Dependent Photocatalysis for CO<sub>2</sub> Reduction
Bibliographic record
Abstract
Bioinspired artificial photosynthesis has resulted in efficient solutions for many areas of science and technology spanning from solar cells to medicine. Owing to rapid development of synthesis and nanofabrication methods we are able to engineer advanced materials at atomic and molecular levels and assemble them into functional devices. Copper compounds are very promising as photocatalysts with good multielectron transfer properties due to their loosely bonded d electrons. Cu2O is an inexpensive material with near-ideal electronic properties for solar energy conversion into fuels. Importantly, Cu2O shows intrinsic p-type conductivity due to the presence of negative-charged Cu vacancies with one of the lowest electron affinities, identifying Cu2O as an optimal candidate for reduction of CO2. However, crystalline Cu2O is photocathodically unstable and therefore unsuitable for multielectron reductive photocatalysis, unless strong adsorption of the reactants that modifies active sites and kinetically enhances reduction reactions occurs. Herein, we report atomic level understanding of the facet-selective active sites in Cu2O that lead to the discovery of the facet specific adsorption and subsequent light induced reduction of CO2 exclusively into liquid fuel – methanol. The activity of these sites was unraveled using operando multimodal correlated characterization of a single particle, and in situ activity measurements. By employing correlated scanning fluorescence x-ray microscopy and environmental transmission electron microscopy at atmospheric pressure, in operando, on a single particle level, we designed nanoparticles with highly active facet selective active sites and particles activity. We also show the interplay between strain and photocatalytic reaction for CO2 reduction on Cu2O single particles that leads to high yield of facet-selective photocatalytic reduction of CO2 to methanol.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".