Water Oxidation Catalysis: Electrocatalytic Response to Metal Stoichiometry in Amorphous Metal Oxide Films Containing Iron, Cobalt, and Nickel
Bibliographic record
Abstract
Photochemical metal-organic deposition (PMOD) was used to prepare amorphous metal oxide films containing specific concentrations of iron, cobalt, and nickel to study how metal composition affects heterogeneous electrocatalytic water oxidation. Characterization of the films by energy-dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy confirmed excellent stoichiometric control of each of the 21 complex metal oxide films investigated. In studying the electrochemical oxidation of water catalyzed by the respective films, it was found that small concentrations of iron produced a significant improvement in Tafel slopes and that cobalt or nickel were critical in lowering the voltage at which catalysis commences. The best catalytic parameters of the series were obtained for the film of composition a-Fe20Ni80. An extrapolation of the electrochemical and XPS data indicates the optimal behavior of this binary film to be a manifestation of iron stabilizing nickel in a higher oxidation level. This work represents the first mechanistic study of amorphous phases of binary and ternary metal oxides for use as water oxidation catalysts, and provides the foundation for the broad exploration of other mixed-metal oxide combinations.
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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 | 0.000 |
| 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 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".