Deciphering the Dynamic Structure Evolution of Fe- and Ni-Codoped CoS<sub>2</sub> for Enhanced Water Oxidation
Bibliographic record
Abstract
Multimetal doping is a promising strategy to achieve high-performance electrocatalysts for the oxygen evolution reaction (OER) due to synergistic effects; however, understanding the dynamic structure evolution and clarifying the catalytic mechanism of each individual doping metal in multimetal-based electrocatalysts remain elusive. Here, we report the synthesis of homogeneous single-metal and bimetal doping sulfides with a pyrite structure for OER catalysts via a high-pressure and high-temperature (HPHT) technique; operando Raman and X-ray absorption spectroscopy (XAS) studies are performed to capture the dynamic evolution during the OER process. Our results find that an Fe- and Ni-codoped CoS 2 electrocatalyst exhibits significantly improved OER activity with an overpotential of 242 (295) mV at 10 (100) mA cm –2 and robust stability over 500 h in an alkaline medium. Operando analysis reveals that Fe and Ni incorporations not only expedite the dynamic response of self-reconstructions of the Fe,Ni-CoS 2 surface but also accelerate the oxidation of Co and Fe into high-valence oxyhydroxides while suppressing nickel oxidation to form Ni(OH) 2 for optimized activity and robust stability. This finding provides a fundamental understanding of the composition design, dynamic reaction pathways, and controlling principle for highly active multimetal-based OER catalysts.
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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".