Photochemical Route for Accessing Amorphous Metal Oxide Materials for Water Oxidation Catalysis
Bibliographic record
Abstract
Large-scale electrolysis of water for hydrogen generation requires better catalysts to lower the kinetic barriers associated with the oxygen evolution reaction (OER). Although most OER catalysts are based on crystalline mixed-metal oxides, high activities can also be achieved with amorphous phases. Methods for producing amorphous materials, however, are not typically amenable to mixed-metal compositions. We demonstrate that a low-temperature process, photochemical metal-organic deposition, can produce amorphous (mixed) metal oxide films for OER catalysis. The films contain a homogeneous distribution of metals with compositions that can be accurately controlled. The catalytic properties of amorphous iron oxide prepared with this technique are superior to those of hematite, whereas the catalytic properties of a-Fe(100-y-z)Co(y)Ni(z)O(x) are comparable to those of noble metal oxide catalysts currently used in commercial electrolyzers.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".