Synthesis and Performance of MOF-Based Non-Noble Metal Catalysts for the Oxygen Reduction Reaction in Proton-Exchange Membrane Fuel Cells: A Review
Bibliographic record
Abstract
Hydrogen is widely regarded as a prime energy carrier for bridging the gap between renewable energy supply and demand. As the energy-generating component of the hydrogen cycle, affordable and reliable fuel cells are of key importance to the growth of the hydrogen economy. However, the use of scarce and costly Pt as an electrocatalyst for the oxygen reduction reaction (ORR) remains an issue to be addressed, and in this regard, metal-organic frameworks (MOFs) are viewed as promising non-noble alternatives because of their self-assembly capability and tunable properties. Herein, recent (2018-2020) works on MOF-based electrocatalysts containing N-doped C, Mn, Fe, Co, multiple metals, and multiple sites are reviewed and summarized with a focus on ORR activity, and the principal physicochemical properties and electrochemical performance of these catalysts realized using rotating electrodes are compared.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".