Oxygen Evolution Catalyzed by Nickel–Iron Oxide Nanocrystals with a Nonequilibrium Phase
Bibliographic record
Abstract
Mixed nickel-iron oxides are of great interest as electrocatalysts for the oxygen evolution reaction (OER), the kinetically challenging half-reaction required for the generation of hydrogen gas from water via electrolysis. Previously, we had reported the synthesis of single crystal, soluble nickel-iron oxide nanoparticles over a wide range of nickel:iron compositions, with a metastable cubic rock salt phase ([Ni,Fe]O) that can be isolated despite the low solubility of iron in cubic nickel oxide at ambient temperatures. Here, activity for OER was examined, catalyzed by these [Ni,Fe]O nanoparticles integrated with indium tin oxide (ITO) electrodes. Because the as-prepared [Ni,Fe]O nanoparticles are oleate-capped, the surface ligands needed to be removed to induce adherence to the ITO substrate, and to enable charge transfer and contact with water to enable OER catalysis. Two different approaches were taken to reduce or eliminate the coverage of oleate ligands in these films: UV irradiation (254 nm) and air plasma. UV irradiation proved to lead to better results in terms of stable and OER-active films at pH 13. Kinetic analysis revealed that the Tafel slopes of these nanoparticle [Ni,Fe]O OER electrodes were limited by the electrochemical surface area and were found to be within the range of 30 to 50 mV/decade. Across the four compositions of Ni:Fe studied, from 24:76 to 88:12, the observed overpotential at 10 mA/cm2 for the OER in basic conditions decreased from 0.47 to 0.30 V as the proportion of nickel increased from 24% to 88%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".