Nickel–Iron Bimetal as a Cost-Effective Cocatalyst for Light-Driven Hydrogen Release from Methanol and Water
Bibliographic record
Abstract
Light-driven hydrogen evolution from liquid hydrogen carriers offers an innovative solution for the realization of safe storage and transportation of hydrogen. The exploration of efficient and cost-effective cocatalysts is highly desirable for constructing an affordable light-driven catalytic architecture. In this work, nickel–iron bimetal (NiFe) is rationally designed and then supported by gallium nitride nanowires (GaN NWs)/Si for light-driven hydrogen generation from methanol aqueous solution. Under optimized conditions, the H 2 evolution rate of NiFe is even comparable to noble metals, e.g., Pt, Ru. By correlative operando spectroscopy characterizations, with density functional theory calculations, it is discovered that Fe is cooperative with Ni for dramatically lowering the energy barrier of the potential-limiting step of *CHO → *CO. What is more, by coordination of photoexcited charge carriers with photothermal effect, the production of hydrogen from CH 3 OH/H 2 O is evidently improved via the evolving track of *CH 3 O > *CH 2 O/*CHO > *CO > *CO 2, in concurrent H 2 O dissociation toward · OH. Combined with the superior optical and electronic attributes of the GaN NWs/Si semiconductor platform, NiFe bimetal enables the achievement of a marked hydrogen activity of 61.2 mmol g –1 h –1 by the only input of light under ambient conditions. This study presents a promising strategy for hydrogen release from liquid hydrogen carriers by using earth-abundant materials under mild conditions.
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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".