Modulating Metal–Organic Frameworks as Advanced Oxygen Electrocatalysts
Bibliographic record
Abstract
Abstract Oxygen‐related electrocatalysis, including those used for the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), play a central role in green‐energy related technologies. Rational fabrication of effective oxygen electrocatalysts is crucial for the development of oxygen related energy devices, such as fuel cells and rechargeable metal–air batteries. Recently, owing to their tunable compositions and microstructures, metal–organic frameworks (MOFs) based materials have drawn extensive attention as nonprecious oxygen electrocatalysts. Various strategies have been developed to fabricate MOF‐based electrocatalysts and regulate their active sites, such as heterometal doping, defect engineering, morphology tuning, heterostructure construction, and hybridization. In this review, by focusing on various modulation strategies aiming at active sites, the recent advances of MOF‐based electrocatalysts are summarized. The synthetic methods used to synthesize various MOF‐based oxygen electrocatalysts are discussed, followed by the underlying engineering mechanisms required to allow performance enhancement, and finally some existing challenges that hinder for their practical applications are discussed alongside a perspective on their possible future.
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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".