Theoretical analysis of electrochemical surface-area loss in supported nanoparticle catalysts
Bibliographic record
Abstract
In polymer electrolyte fuel cells a decrease in catalytic surface-area within the cathode catalyst layer is a critical barrier to commercialization. This loss in catalytic surface-area manifests as a loss in cell voltage and thus power density of the cell. It has been established that potential cycling accelerates the loss in catalytic surface-area yet isolating the contributing mechanisms as well as relating mechanisms to operating conditions is not as straightforward. We approach the issue of surface-area loss deconvolution with a combined experimental, modelling and theoretical framework. The methodology is based on the Lifshitz-Slyozov-Wagner and Smoluchowski theories of particle size distribution evolution. Electrochemical surface-area loss experiments probing upper potential limits of 0.9 and 1.2 V as well as temperatures from 298 to 343 K were analyzed with the model. A dissolution and redeposition mechanism was correlated with the measurements for both upper potential limits; however, at the upper potential limit of 1.2 V, ambiguity between the coagulation and the dissolution and redeposition mechanisms was found. Notwithstanding, the extracted dissolution and redeposition parameters aligned with independent studies on Pt dissolution whereas similar positive comparisons with independent results were unable to be made for the coagulation mechanism.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".