Integrating PGM‐Free Catalysts into Catalyst Layers and Proton Exchange Membrane Fuel Cell Devices
Bibliographic record
Abstract
While proton exchange membrane fuel cells (PEMFCs) continue to expand into commercial markets, there is still pressure to decrease cost. One of the largest opportunities to reducing cost is to reduce the amount of platinum-group metal (PGM) catalysts used in the electrodes (particularly the cathode). Over the past decade, exciting advances in the Fe/N/C family of PGM-free oxygen reduction reaction (ORR) catalysts has provided great optimism that not only can PGMs at the cathode be reduced but possibly be completely eliminated. In fact, in September 2017, Ballard Power Systems announced the commercialization of the world's first PEMFC product to utilize a PGM-free catalyst at the cathode (FCgen-micro (non-precious-metal catalyst, NPMC)). However, for these catalysts to be used in more demanding applications, an improved understanding and new design approaches for PGM-free catalyst layers will be required. Herein, some of the latest research on both modeling and experimental studies in the field of PGM-free catalyst layer research are discussed. In addition, a short discussion on Ballard's new NPMC is provided.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".