The Science and Engineering of Durable Ultralow PGM Catalysts (2012 DOE-EERE-FCT Annual Progress Report)
Bibliographic record
Abstract
Minimizing the quantity of Pt group metals used in polymer membrane fuel cells (PEMFCs) is one of the remaining grand challenges for fuel cell commercialization. Tremendous progress has been achieved over the last two decades in decreasing the Pt loading required for efficient fuel cell performance. Unfortunately, the fluctuations in the price of Pt represent a substantial barrier to the economics of widespread fuel cell use. Durability and impurity tolerance are also challenges that are tightly coupled to fuel cell Pt electrode loading. Traditional approaches to decreasing the amount of Pt required for good performance include: (1) Increasing mass activity by decreasing Pt particle size by supporting on carbon; (2) Alloy formulation Pt-Co, Pt-Cr alloys to improve mass activity; (3) Increasing Pt utilization by optimization of electronic and ionic contact of the Pt particles; (4) Improving conductivity of the electronic and ionic conducting constituents of the membrane electrode assembly; and (5) Improving reactant to and product mass transport away from the electroactive sites. Recent novel approaches include the nanoengineering of core shell catalysts and Pt particles of unusual geometries such as nanowires/whiskers. The success of the aforementioned approaches has been great; however further advances using such approaches have been hampered by a lack of underlining scientific understanding of the catalyst activity, particle growth mechanisms, and optimization strategies for designing composite electrodes The objectives of this report are: (1) Development of durable, high mass activity Platinum Group Metal (PGM) cathode catalysts-enabling lower cost fuel cells; (2) Elucidation of the fundamental relationships between PGM catalyst shape, particle size and activity to help design better catalysts; (3) Optimization of the cathode electrode layer to maximize the performance of PGM catalysts-improving fuel cell performance and lowering cost; (4) Understanding the performance degradation mechanisms of high mass activity cathode catalysts-provide insights to better catalyst design; and (5) Development and testing of fuel cells using ultra-low loading high activity PGM catalysts-validation of advanced concepts.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.004 | 0.004 |
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".