Open-source FCPEM-Performance & Durability Model Consideration of Membrane Properties on Cathode Degradation
Bibliographic record
Abstract
The durability of PEM fuel cells is a primary requirement for large scale commercialization of these power systems in transportation and stationary market applications which target operational lifetimes of 5,000 hours and 60,000 hours by 2020, respectively. Key degradation modes contributing to fuel cell lifetime limitations have been largely associated with the platinum-based cathode catalyst layer. Furthermore, as fuel cells are driven to low cost materials and lower catalyst loadings in order to meet the cost targets for commercialization, the catalyst durability has become even more important. While over the past few years significant progress has been made in identifying the underlying causes of fuel cell degradation and key parameters that greatly influence the degradation rates, many gaps with respect to knowledge of the driving mechanisms still exist; in particular, the acceleration of the mechanisms due to different membrane compositions remains an area not well understood. The focus of this project extension was to enhance the predictive capability of the PEM Fuel Cell Performance & Durability Model called FC-APOLLO (Application Package for Open-source Long Life Operation) by including interaction effects of membrane transport properties such as water transport, changes in proton conductivity, and overall water uptake/adsorption and the state of the catalyst layer local conditions to further understand the driving forces for platinum dissolution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.004 | 0.001 |
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".