A Review of Graphene‐Based Nanostructural Materials for Both Catalyst Supports and Metal‐Free Catalysts in PEM Fuel Cell Oxygen Reduction Reactions
Bibliographic record
Abstract
A comprehensive overview and description of graphene‐based nanomaterials explored in recent years for catalyst supports and metal‐free catalysts for polymer electrolyte membrane (PEM) fuel cell oxygen reduction reactions (ORR) is presented. The catalyst material structures/morphologies, material selection, and design for synthesis, catalytic performance, catalytic mechanisms, and theoretical approaches for catalyst down‐selection and catalyzed ORR mechanisms are emphasized with respect to the performance of ORR catalysts in terms of both activity and stability. When graphene‐based materials, including graphene and doped graphene, are used as the supporting materials for both Pt/Pt alloy catalysts and non‐precious metal catalyst, the resulting ORR catalysts can give superior catalyst activity and stability compared to those of conventional carbon‐supported catalysts; when they are used as metal‐free ORR catalysts, significant catalytic activity and stability are observed. The nitrogen‐doped graphene materials even show superior performance compared to supported metal catalysts. Challenges including the lack of material mass production, unoptimized catalyst structure/morphology, insufficient fundamental understanding, and testing tools/protocols for performance optimization and validation are identified, and approaches to address these challenges are suggested.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".