Hydration‐Effect‐Promoting Ni–Fe Oxyhydroxide Catalysts for Neutral Water Oxidation
Bibliographic record
Abstract
Abstract Oxygen evolution reaction (OER) catalysts that function efficiently in pH‐neutral electrolyte are of interest for biohybrid fuel and chemical production. The low concentration of reactant in neutral electrolyte mandates that OER catalysts provide both the water adsorption and dissociation steps. Here it is shown, using density functional theory simulations, that the addition of hydrated metal cations into a Ni–Fe framework contributes water adsorption functionality proximate to the active sites. Hydration‐effect‐promoting (HEP) metal cations such as Mg2+ and hydration‐effect‐limiting Ba2+ into Ni–Fe frameworks using a room‐temperature sol–gel process are incorporated. The Ni–Fe–Mg catalysts exhibit an overpotential of 310 mV at 10 mA cm−2 in pH‐neutral electrolytes and thus outperform iridium oxide (IrO2) electrocatalyst by a margin of 40 mV. The catalysts are stable over 900 h of continuous operation. Experimental studies and computational simulations reveal that HEP catalysts favor the molecular adsorption of water and its dissociation in pH‐neutral electrolyte, indicating a strategy to enhance OER catalytic activity.
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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) | 0.001 | 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".