Reversible Structural Oscillation Mediates Stable Oxygen Evolution Reaction
Bibliographic record
Abstract
Abstract The dynamic dissolution of active species of electrocatalysts suffers severe durability issues, thus limiting practical sustainable electrochemical application despite the enormous strides in the activity. An atomistic understanding of the dynamic pattern is a fundamental prerequisite for realizing prolonged stability. Herein, modeling on NiFe LDHs, multiple operando spectroscopies revealed the structural oscillation of the local [Ni–O 2 –Fe] unit identified a strong dependence on the alternant Fe dissolution and redeposition during the oxygen evolution reaction (OER) process, thus mediating the dynamic stability. At this point, a proof‐of‐concept strategy with S, Co co‐doping was demonstrated to tune structural oscillations. In situ S leaching that alleviates the lattice mismatch suppresses Fe dissolution, while the electron‐withdrawing Co as a deposition site promotes Fe redeposition, thus achieving the reversible oscillation of local [Ni/Co–O 2 –Fe] units and dynamic stability. The implementation of the modified NiFe LDH in industrial water electrolysis equipment operated steadily over 800 h (5000‐h lifetime obtained by epitaxial method with 10% attenuation) with an energy consumption of 4.05 kWh Nm −3 H 2 @ 4000 A m −2 . The levelized cost of hydrogen of US$ 2.315 per kg H2 overmatches the European Commission's target for the coming decade ( H2 ).
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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".