Metallic W/WO2 solid-acid catalyst boosts hydrogen evolution reaction in alkaline electrolyte
Bibliographic record
Abstract
Abstract The lack of available protons severely lowers the activity of alkaline hydrogen evolution reaction process than that in acids, which can be efficiently accelerated by tuning the coverage and chemical environment of protons on catalyst surface. However, the cycling of active sites by proton transfer is largely dependent on the utilization of noble metal catalysts because of the appealing electronic interaction between noble metal atoms and protons. Herein, an all-non-noble W/WO 2 metallic heterostructure serving as an efficient solid-acid catalyst exhibits remarkable hydrogen evolution reaction performance with an ultra-low overpotential of −35 mV at −10 mA/cm 2 and a small Tafel slope (−34 mV/dec), as well as long-term durability of hydrogen production (>50 h) at current densities of −10 and −50 mA/cm 2 in alkaline electrolyte. Multiple in situ and ex situ spectroscopy characterizations combining with first-principle density functional theory calculations discover that a dynamic proton-concentrated surface can be constructed on W/WO 2 solid-acid catalyst under ultra-low overpotentials, which enables W/WO 2 catalyzing alkaline hydrogen production to follow a kinetically fast Volmer-Tafel pathway with two neighboring protons recombining into a hydrogen molecule. Our strategy of solid-acid catalyst and utilization of multiple spectroscopy characterizations may provide an interesting route for designing advanced all-non-noble catalytic system towards boosting hydrogen evolution reaction performance in alkaline electrolyte.
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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".