Investigation of Amorphous Mixed-Metal (Oxy)Fluorides as a New Class of Water Oxidation Electrocatalysts
Bibliographic record
Abstract
The development of electrocatalysts for the oxygen evolution reaction (OER) is one of the principal challenges in the area of renewable energy research. Within this context, mixed-metal oxides have recently emerged as the highest performing OER catalysts. Their structural and compositional modification to further boost their activity is crucial to the wide-spread use of electrolysis technologies. In this work, we investigated a series of mixed-metal F-containing materials as OER catalysts to probe possible benefits of the high electronegativity of fluoride ions. We found that crystalline hydrated fluorides, CoFe 2 F 8 (H 2 O) 2 , NiFe 2 F 8 (H 2 O) 2, and amorphous oxyfluorides, NiFe 2 F 4.4 O 1.8 and CoFe 2 F 6.6 O 0.7, feature excellent activity and stability for the OER in alkaline electrolyte. Subsequent electroanalytical and spectroscopic characterization hinted that the electronic structure modulation conferred by the fluoride ions aided their reactivity. Finally, the best catalyst of the set, NiFe 2 F 4.4 O 1.8 , was applied as anode in an electrolyzer comprised solely of earth-abundant materials.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".