High Energy Ballmilled Pt-Mo Catalysts for Polymer Electrolyte Fuel Cells and Their Tolerance to CO
Bibliographic record
Abstract
Catalysts containing Pt and Mo were synthesized by high energy ballmilling. The two metal powders were milled together in one step with a leacheable dispersing agent (Al or ) and a leacheable process‐control agent (NaF or ) to increase the specific area of the catalysts and avoid sticking of the catalyst's precursor on the vial walls and grinding balls. The catalysts were labeled and to reflect the bulk atomic or molecular nominal content of the milled powders. After the leaching step, the actual bulk Mo content of both catalysts was rather low (∼5 atom %). Despite their low Mo content, both catalysts displayed a similar performance, in fuel cell tests, to that displayed by black from Johnson Matthey. The best catalyst in was , which is a face‐centered cubic solid solution of Mo (∼5 atom %) in Pt with a specific area of . By combining X‐ray diffraction, X‐ray photoelectron spectroscopy, and fuel cell test results, it was possible to determine that Pt was metallic in the catalyst, but that Mo(V and VI) were present at the surface of the working anode in . © 2000 The Electrochemical Society. All rights reserved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".