Highly efficient water oxidation via a bimolecular reaction mechanism on rutile structured mixed-metal oxyfluorides
Bibliographic record
Abstract
Mixed-metal oxides are generally considered to be the highest-performance catalysts for alkaline water oxidation. Despite significant efforts dedicated to understanding and accelerating their efficiency, most works have been limited investigations of Ni, Co, and Fe oxides, thus overlooking beneficial effects of hetero-anion incorporation. To this end, we report on the development of Co0.5Fe0.5O0.5F1.5 oxyfluoride materials featuring a rutile crystal structure and porous morphology via a scalable and green synthetic route. The catalyst surface, enhanced through electron withdrawing effects imparted by the fluoride ions, give rise to highly effective catalytic sites for electrochemical water oxidation. In particular, their performance across metrics of Tafel slope (27 mV/dec), mass activity (846 A/g at 1.53 V vs. RHE), turnover frequency (21/s at 1.53 V vs. RHE), overpotential (220 mV for 10 mA/cm2), and stability (27 days of continuous operation) largely surpasses most known Co-based catalysts. Mechanistic studies suggest that this performance is driven by a bimolecular, oxygen coupling reaction mechanism through proximal active sites on the catalyst surface, thus enabling a new avenue for achieving accelerated oxygenic electrocatalysis.
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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 | 0.001 |
| 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".