Recent advances in non-precious metal catalysis for oxygen-reduction reaction in polymer electrolyte fuelcells
Bibliographic record
Abstract
Hydrogen produced from water and renewable energy could fuel a large fleet of proton-exchange-fuel-cell vehicles in the future. However, the dependence on expensive Pt-based electrocatalysts in such fuel cells remains a major obstacle for a widespread deployment of this technology. One solution to overcome this predicament is to reduce the Pt content by a factor of ten by replacing the Pt-based catalysts with non-precious metal catalysts at the oxygen-reducing cathode. Fe- and Co-based electrocatalysts for this reaction have been studied for over 50 years, but they were insufficiently active for the high efficiency and power density needed for transportation fuel cells. Recently, several breakthroughs occurred that have increased the activity and durability of non-precious metal catalysts (NPMCs), which can now be regarded as potential competitors to Pt-based catalysts. This review focuses on the new synthesis methods that have led to these breakthroughs. A modeling analysis is also conducted to analyze the improvements required from NPMC-based cathodes to match the performance of Pt-based cathodes, even at high current density. While no further breakthrough in volume-specific activity of NPMCs is required, incremental improvements of the volume-specific activity and effective protonic conductivity within the fuel-cell cathode are necessary. Regarding durability, NPMCs with the best combination of durability and activity result in ca. 3 times lower fuel cell performance than the most active NPMCs at 0.80 V. Thus, major tasks will be to combine durability with higher activity, and also improve durability at cell voltages greater than 0.60 V.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".