Evaluation of the Corrosion Resistance of Carbons for Use as PEM Fuel Cell Cathode Supports
Bibliographic record
Abstract
The electrochemical corrosion of the carbon-based catalyst support materials used in proton exchange membrane fuel cells (PEMFCs), especially at the cathode, has a significant impact on PEMFC lifetime. Thus, an effective and reliable method for the evaluation of the corrosion resistance of new carbon supports, as they are developed, is required. In the present work, a novel approach that allows for the ranking of carbon supports for their corrosion susceptibility has been developed, based on the testing of a conventional microporous carbon material (Vulcan carbon, VC) and two ordered mesoporous carbons (OMCs), synthesized using sucrose and anthracene as the carbon precursors. While cyclic voltammetry does reveal important information about surface area and surface oxidation changes, the charge passed during potential stepping between 0.8 and 1.4 V vs. RHE was found to be still more informative, with the cathodic charge used to continuously correct for the changing surface area of the carbon during its oxidation. Based on this approach, it was found that VC, which is the more crystalline material, is more stable to oxidation than the OMCs, as expected, while the anthracene precursor leads to a more stable OMC than when sucrose is used.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".