A Kinetic Modeling Approach to Estimate the Lifetime of Polymer Electrolyte Fuel Cell Membranes Under Accelerated Stress Test Conditions
Bibliographic record
Abstract
Failure of membranes used in polymer electrolyte fuel cells is attributed to the in-situ chemical degradation of the membrane that is further exacerbated by the mechanical stresses generated due to hygro-thermal cycling during fuel cell operation. The chemical radical attack on the polymer chains within the membrane leads to gradual loss of the material and randomly distributed stress concentration sites are created. These sites are then subjected to repeated swelling and shrinkage of the membrane, eventually leading to the appearance of micro-cracks and/or pin-holes in the membrane. To simulate the lifetime of a fuel cell membrane in such an environment, it is therefore important to consider both these effects that occur simultaneously in the membrane. A stochastic modeling approach is presented in this work that takes into consideration the rates of chemical and mechanical degradation of the membrane incorporated into a two-dimensional membrane lattice network. While the rate of chemical degradation is based on the solution of reaction kinetics occurring in a fuel cell membrane, the mechanical degradation rate is evaluated using a stress-biased, thermally activated process. Depending upon any given chemical and mechanical load, and using appropriate physical properties of the membrane, the model can predict, within reasonable error, the time to crack initiation in the membrane based on a probabilistic non-homogeneous Poisson-type process. The membrane lifetime predictions are validated against test data acquired for membranes subjected to accelerated stress tests.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".