Doping Shortens the Metal/Metal Distance and Promotes OH Coverage in Non-Noble Acidic Oxygen Evolution Reaction Catalysts
Bibliographic record
Abstract
Acidic water electrolysis enables the production of hydrogen for use as a chemical and as a fuel. The acidic environment hinders water electrolysis on non-noble catalysts, a result of the sluggish kinetics associated with the adsorbate evolution mechanism, reliant as it is on four concerted proton-electron transfer steps. Enabling a faster mechanism with non-noble catalysts will help to further advance acidic water electrolysis. Here, we report evidence that doping Ba cations into a Co 3 O 4 framework to form Co 3– x Ba x O 4 promotes the oxide path mechanism and simultaneously improves activity in acidic electrolytes. Co 3– x Ba x O 4 catalysts reported herein exhibit an overpotential of 278 mV at 10 mA/cm 2 in 0.5 M H 2 SO 4 electrolyte and are stable over 110 h of continuous water oxidation operation. We find that the incorporation of Ba cations shortens the Co–Co distance and promotes OH adsorption, findings we link to improved water oxidation in acidic electrolyte.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".