Self-Optimized Metal–Organic Framework Electrocatalysts with Structural Stability and High Current Tolerance for Water Oxidation
Bibliographic record
Abstract
Metal–organic frameworks (MOFs) as electrocatalysts for oxygen evolution reaction (OER) typically suffer from fast degradation under harsh electrolyte conditions, impeding their practical use in industrial electrolyzers. Besides, the evolution of catalytic centers in MOFs and the related influence on their performance along the progress of reaction have rarely been studied. Here, we report a type of structurally stable bimetallic FeNi-MOF nanoarrays with self-optimized electrocatalytic activities in the oxygen production. Such a unique dynamic phenomenon is related with the gradual valence increments of Fe ions in MOFs, which trigger the continuous performance improvement before reaching an optimal steady state. Apart from the intact crystalline structures upon cycling, these FeNi-MOFs achieve low overpotentials of 239 and 308 mV at the current densities of 50 and 200 mA cm –2, respectively, and show durable operation for over 1033 h (>43 days) at 100 mA cm –2 and for another 200 h at 500 mA cm –2 . A direct comparison of isostructural and single crystalline Fe-MOFs and Ni-MOFs resolves higher activities of Fe sites in the bimetallic MOFs, which are corroborated by theoretical calculations. The Fe–O bond covalency increment during Fe oxidation enhances the proton–electron transfers with the oxygen 2p-band closer to the Fermi level, thereby expediting the OER process. This work provides deep insights into the understanding of catalytic processes in heterometallic MOFs.
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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".