Influence of Electrolyte Composition and pH on Platinum Electrochemical and/or Chemical Dissolution in Aqueous Acidic Media
Bibliographic record
Abstract
Comprehension of the impact of electrolyte nature and concentration on Pt degradation is essential for the improvement of durability of catalyst layers (CLs), which are the heart of polymer electrolyte membrane fuel cells (PEMFCs). Electrochemical and chemical dissolution of polycrystalline Pt in aqueous CF 3 SO 3 H, H 2 SO 4, and HClO 4 solutions of different concentrations ( c = 0.1 and 0.5 M) upon potential switching and holding in the 0.60–1.20 V versus RHE range is analyzed using inductively coupled plasma mass spectroscopy. This potential range mimics the conditions encountered in operating PEMFCs. Trifluoromethanesulfonic acid (CF 3 SO 3 H) is employed because it is the smallest fluorinated sulfonic acid and can serve as a model molecule. Degradation of Pt in H 2 SO 4 and HClO 4 solutions is examined for comparative analysis. The results reveal that the electrolyte concentration has a significant impact on Pt electrochemical and chemical dissolution. The amount of dissolved Pt in 0.1 M solutions of CF 3 SO 3 H, H 2 SO 4, and HClO 4 is practically the same and lower than that in analogous 0.5 M solutions. However, the amount of dissolved Pt in 0.5 M H 2 SO 4 solution is greater than that in 0.5 M solutions of CF 3 SO 3 H or HClO 4 . The influence of anion nature and pH on Pt dissolution is examined in 0.1 and 0.5 M HClO 4 solutions without and with 1.0 × 10 –2 M H 2 SO 4 addition. The results show that under these conditions the anion nature has no or negligible impact on Pt dissolution, but pH significantly affects the process. An analysis of potential versus pH diagrams (Pourbaix diagrams) for acid solutions of different pH values suggests that Pt degradation (with the formation of Pt 2+ (aq) and Pt 4+ (aq)) might proceed through both electrochemical and chemical pathways.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 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".