A Multiparadigm Modeling Investigation of Membrane Chemical Degradation in PEM Fuel Cells
Bibliographic record
Abstract
We report a multi-paradigm model of the membrane chemical degradation in Polymer Electrolyte Membrane Fuel Cells (PEMFCs), by combining Coarse-Grained Molecular Dynamics (CGMD) and a multiscale cell performance model. CGMD is used to generate structural databases that relate the amount of detached (degraded) ionomer sidechains with the water content and the resulting PEM meso-microporous structure. The multiscale cell performance model describes the electrochemical reactions and transport mechanisms occuring in the electrodes from an on-the-fly coupling between Kinetic Monte Carlo (KMC) sub-models parametrized with Density Functional Theory (DFT) data and (partial differential equations-based) continuum sub-models. Furthermore, the performance model includes a kinetic PEM degradation sub-model which integrates the CGMD database. The cell model also predicts the instantaneous PEM sidechain content and conductivity evolution at each time step. The coupling of these diverse modeling paradigms allows one to describe the feedback between the instantaneous cell performance and the intrinsic membrane degradation processes. This provides detailed insights on the membrane degradation (sidechain detachment as well as water reorganization within the PEM) during cell operation. This novel modeling approach opens interesting perspectives in engineering practice to predict materials degradation and durability as a function of the initial chemical composition and structural properties in electrochemical energy conversion and storage devices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".