An Active and Robust Catalytic Architecture of NiCo/GaN Nanowires for Light‐Driven Hydrogen Production from Methanol
Bibliographic record
Abstract
Abstract On‐site hydrogen production from liquid organic hydrogen carriers e.g., methanol provides an emerging strategy for the safe storage and transportation of hydrogen. Herein, a catalytic architecture consisting of nickel‐cobalt nanoclusters dispersed on gallium nitride nanowires supported by silicon for light‐driven hydrogen production from methanol is reported. By correlative microscopic, spectroscopic characterizations, and density functional theory calculations, it is revealed that NiCo nanoclusters work in synergy with GaN nanowires to enable the achievement of a significantly reduced activation energy of methanol dehydrogenation by switching the potential‐limiting step from *CHO → *CO to *CH 3 O → *CH 2 O. In combination with the marked photothermal effect, a high hydrogen rate of 5.62 mol·gcat‐1·h‐1 with a prominent turnover frequency of 43,460 h‐1 is achieved at 5 Wcm‐2 without additional energy input. Remarkably, the synergy between Co and Ni, in combination with the unique surface of GaN, renders the architecture with outstanding resistance to sintering and coking. The architecture thereby exhibits a high turnover number of >16,310,000 over 600 h. Outdoor testing validates the viability of the architecture for active and robust hydrogen evolution under natural concentrated sunlight. Overall, this work presents a promising architecture for on‐site hydrogen production from CH 3 OH by virtually unlimited solar energy.
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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".