Post mortem Study of Catalyst Degradations Occurring in High-Temperature Proton Exchange Membrane Fuel Cells Upon Start-Stop Operation
Bibliographic record
Abstract
High-temperature proton exchange membrane fuel cells (HT-PEMFCs) could replace fossil fuel-based technologies for applications which cannot involve bulky/heavy cooling systems, such as aeronautics. However, severe materials degradations upon operation prevent performance retention for acceptable lifetimes. While others have already reported degradations in HT-PEMFC, post mortem characterizations of used HT-PEMFC membrane electrode assemblies (MEAs) remain scarce. Herein, HT-PEMFC performance degradation is studied by applying a startup/shutdown protocol to a short-stack operated at 160 °C; one MEA is characterized using complementary physicochemical/electrochemical techniques to identify/understand the degradation mechanisms and their origin. This start/stop operation mode (co-flow gas reactants) leads to substantial degradation inhomogeneity. For the anode, migration, coalescence, and detachment of Pt nanoparticles are witnessed induced by high-surface-area carbon support functionalization and corrosion. The anode electrochemical surface area (ECSA) remains constant at the inlet and increases significantly at the outlet, following inhomogeneous degradation of the cathode catalyst: the Pt z+ ions formed at high potential/oxidizing conditions concentrate towards the outlet, where they redeposit locally or at the anode, after diffusion/migration across the PBI membrane. Hence, the cathode ECSA decreases significantly at the inlet. Furthermore, intense Ni-leaching from the initial PtNi alloy catalyst is reported as a result of O 2 mass-transport and phosphoric acid dilution inhomogeneity.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".