Screening Bifunctional Pt Based NSTF Catalysts for Durability with the Rotating Disk Electrode: The Effect of Ir and Ru
Bibliographic record
Abstract
Various amounts of Ir (<20 μg cm −2 ) and Ru (up to 12 μg cm −2 ) on a consistent base Pt loading of 85 μg cm −2 , were sputter deposited on a nanostructured thin film catalyst support to mimic a hydrogen fuel cell's cathode catalyst. The nanostructured support was grown on glassy carbon disks designed for a rotating disk electrode, which was used to simulate what happens to a fuel cell cathode during repeated start-up, operation, and shut-down. The testing protocol subjected the catalyst to a minimum potential of 0.65 V RHE and a maximum between 1.53 and 1.8 V RHE . The upper potential was achieved with a galvanostatic hold which is an alternative way to simulate potential transients on the cathode caused by start-up and shut-down. Increasing Ir loading improved the durability of both oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) activity. When combined with Ir, Ru provided no benefit to OER durability except at an Ir loading of 10 μg cm −2 . Ru addition (no Ir present) improved the ORR durability compared to pure Pt. ORR durability was not influenced by Ru addition to Ir-containing samples. In general, ORR durability showed no dependence on Ru loading for all the Ru containing samples and ORR activity was decreased by OER catalyst addition, though more so for Ir than Ru.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".