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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".