Electrochemical Reconstruction Engineering: Metal–Organic Gels as Pre‐Catalysts for NiOOH/FeOOH Heterostructure to Boost Oxygen Evolution Reaction
Bibliographic record
Abstract
Metal–organic gels (MOG) as new types of soft materials have shown promising applications in various fields such as chemosensors, environmental remediation, and gas adsorption/separation, owing to their high porosity, low density, and high surface area. However, the application of MOG materials in energy electrocatalysis and the active components made from them are rarely perceived. Herein, a new electrochemistry‐driven reconstruction strategy to synthesize the NiOOH/FeOOH heterostructure from MOG materials is reported. The reconstructed NiOOH/FeOOH exhibits superior oxygen evolution reaction activity and excellent stability, owing to the synergistic effect of bimetallic centers, the abundant interface between NiOOH and FeOOH, and the plentiful defects. Impressively, the activated Re–FeNi–MOG‐4 electrocatalyst displays remarkable catalytic activity with a low overpotential of 220 mV at a current density of 10 mA cm−2 and a small Tafel slope of 48 mV dec−1 in alkaline electrolyte, outperforming most recently reported electrocatalysts. Herein, a facile and effective electrochemical reconstruction engineering of pre‐catalysts is provided and the evolution of self‐reconstruction of MOG materials for accelerating the kinetics of the electrocatalytic process is highlighted.
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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".