A comparison of photodeposited RuOx foralkaline water electrolysis
Bibliographic record
Abstract
The storage of renewable energy is a pressing challenge to overcome in the transition towards a power grid based on plentiful, yet intermittent energy supplies. The renewables-driven electrolysis ofwater to formhydrogen fuel is an attractive avenue, but requires better oxygen-evolution reaction (OER) catalysts to be feasible at scale. RuO2 is touted as one of the superior OER catalysts, but only under acidic conditions – RuO2 electrocatalysts suffer from poor stability under alkaline conditions. In this work, we evaluate three photodeposited RuO2 OER electrocatalysts, all prepared via a scalable photodeposition method. Based on electrochemical and spectroscopic studies (x-ray photoelectron spectroscopy and X-ray absorption spectroscopy) our main findings are that nanocrystalline RuO2 catalysts outperform their amorphous counterpart, and are stable under alkaline (0.1 M KOH) conditions. This works thus lifts a major hurdle towards the use of RuO2 for alkaline water electrolysis.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".