Dual‐ion engineering of Fe‐doped Ni <sub>3</sub> ( <scp>OH</scp> ) <sub>4</sub> ( <scp> NO <sub>3</sub> </scp> ) <sub>2</sub> for Cl <sup>−</sup> ‐resistant oxygen evolution reaction in alkaline medium
Bibliographic record
Abstract
Abstract Seawater electrolysis offers a sustainable pathway for green hydrogen production, yet the development of oxygen evolution reaction (OER) catalysts with robust Cl¯ resistance remains a critical challenge. Here, we propose a dual‐ion modification strategy, incorporating NO 3 ¯ and Fe 3+ cations into Ni(OH) 2 (denoted as Fe‐NiNH/NF), to synergistically enhance Cl¯ resistance and OER activity in alkaline simulated seawater. The Fe‐NiNH/NF catalyst demonstrates exceptional performance, requiring only 320 mV overpotential at 100 mA cm −2 in KOH and NaCl solution, with a negligible 10 mV increase compared to pure KOH. When implemented in an alkaline anion exchange membrane water electrolyzer, the catalyst achieves 1.76 V cell voltage at 1 A cm −2 with 350‐h stability. Post‐reaction characterizations confirm the transformation of Fe‐NiNH/NF into Fe‐NiOOH with adsorbed NO 3 ¯. Theoretical calculations reveal that NO 3 ¯ forms an exclusionary layer via strong polarity and steric hindrance, electrostatically repelling Cl¯, and NO 3 ¯ and Fe 3+ collectively downshift the Ni d‐band center, weakening Cl¯ adsorption while optimizing intermediate binding.
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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".