Superior Performance of Ag over Pt for Hydrogen Evolution Reaction in Water Electrolysis under High Overpotentials
Bibliographic record
Abstract
Abstract There has been a substantial research effort worldwide to develop non-noble metal catalysts in electrolyzers for H2 production from renewable energy sources. Pt catalysts are found to display the highest hydrogen evolution reaction (HER) activity under typical experimental conditions with relatively low acidity and overpotentials. However, it is noted that catalytic activity is highly dependent on acidity and applied potential used. In real practice of a high workload electrolyzer, high acidity and large negative potentials are required to optimize the HER activity. We hereby report that inexpensive silver catalysts, particularly the cubic form of silver nanoparticles, can clearly exhibit superior HER activity over Pt with a different rate-determining step in an electrolyzer when such conditions are reached. This is attributed to the weaker Ag–H bond at the surface than Pt–H which is more favorable for H recombination to form H2. It is thus believed that this study provides new insights into designing economical and highly efficient catalysts that can replace the expensive noble metal analogues in a working electrolyzer.
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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 | 0.001 |
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".