Recent Progress on MOF-Derived Nanomaterials as Advanced Electrocatalysts in Fuel Cells
Bibliographic record
Abstract
Developing a low cost, highly active and durable cathode material is a high-priority research direction toward the commercialization of low-temperature fuel cells. However, the high cost and low stability of useable materials remain a considerable challenge for the widespread adoption of fuel cell energy conversion devices. The electrochemical performance of fuel cells is still largely hindered by the high loading of noble metal catalyst (Pt/Pt alloy) at the cathode, which is necessary to facilitate the inherently sluggish oxygen reduction reaction (ORR). Under these circumstances, the exploration of alternatives to replace expensive Pt-alloy for constructing highly efficient non-noble metal catalysts has been studied intensively and received great interest. Metal–organic frameworks (MOFs) a novel type of porous crystalline materials, have revealed potential application in the field of clean energy and demonstrated a number of advantages owing to their accessible high surface area, permanent porosity, and abundant metal/organic species. Recently, newly emerging MOFs materials have been used as templates and/or precursors to fabricate porous carbon and related functional nanomaterials, which exhibit excellent catalytic activities toward ORR or oxygen evolution reaction (OER). In this review, recent advances in the use of MOF-derived functional nanomaterials as efficient electrocatalysts in fuel cells are summarized. Particularly, we focus on the rational design and synthesis of highly active and stable porous carbon-based electrocatalysts with various nanostructures by using the advantages of MOFs precursors. Finally, further understanding and development, future trends, and prospects of advanced MOF-derived nanomaterials for more promising applications of clean energy are presented.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".