Wafer-Level Artificial Photosynthesis for CO<sub>2</sub> Reduction into CH<sub>4</sub> and CO Using GaN Nanowires
Bibliographic record
Abstract
We report on the first demonstration of high-conversion-rate photochemical reduction of carbon dioxide (CO 2 ) on gallium nitride (GaN) nanowire arrays into methane (CH 4 ) and carbon monoxide (CO). It was observed that the reduction of CO 2 to CO dominates on as-grown GaN nanowires under ultraviolet light irradiation. However, the production of CH 4 is significantly increased by using the Rh/Cr 2 O 3 core/shell cocatalyst, with an average rate of ∼3.5 μmol g cat –1 h –1 in 24 h. In this process, the rate of CO 2 to CO conversion is suppressed by nearly an order of magnitude. The rate of photoreduction of CO 2 to CH 4 can be further enhanced and can reach ∼14.8 μmol g cat –1 h –1 by promoting Pt nanoparticles on the lateral m -plane surfaces of GaN nanowires, which is nearly an order of magnitude higher than that measured on as-grown GaN nanowire arrays. This work establishes the potential use of metal-nitride nanowire arrays as a highly efficient photocatalyst for the direct photoreduction of CO 2 into chemical fuels. It also reveals the potential of engineered core/shell cocatalysts in improving the selectivity toward more valuable fuels.
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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) | 0.000 | 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".