Visible light-driven efficient overall water splitting using p-type metal-nitride nanowire arrays
Bibliographic record
Abstract
Solar water splitting for hydrogen generation can be a potential source of renewable energy for the future. Here we show that efficient and stable stoichiometric dissociation of water into hydrogen and oxygen can be achieved under visible light by eradicating the potential barrier on nonpolar surfaces of indium gallium nitride nanowires through controlled p-type dopant incorporation. An apparent quantum efficiency of ∼12.3% is achieved for overall neutral (pH∼7.0) water splitting under visible light illumination (400–475 nm). Moreover, using a double-band p-type gallium nitride/indium gallium nitride nanowire heterostructure, we show a solar-to-hydrogen conversion efficiency of ∼1.8% under concentrated sunlight. The dominant effect of near-surface band structure in transforming the photocatalytic performance is elucidated. The stability and efficiency of this recyclable, wafer-level nanoscale metal-nitride photocatalyst in neutral water demonstrates their potential use for large-scale solar-fuel conversion. Solar water splitting for hydrogen generation may be a future source of renewable energy. Here, the authors demonstrate that controlled p-type doping of metal-nitride nanowires can eradicate surface potential barriers and promotes stable stoichiometric dissociation of water under visible light.
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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".