Model-Based Deconvolution of Potential Losses in a PEM Fuel Cell
Bibliographic record
Abstract
Deconvolution of potential losses or voltage loss breakdown (VLB) is a method to extract individual component losses, e.g., due to sluggish kinetics, reactant diffusion or charge transport, from measurements of the total potential loss incurred under operation of a polymer electrolyte fuel cell. For efficient fuel cell design and diagnostics, it is desirable to be able to identify the origin of potential losses. The challenge involved in this task is that individual losses can hardly be separated since the underlying physical phenomena are highly interdependent. We propose a two-step deconvolution, which is based on a physical performance model, to calculate the 3 conventional potential losses, including kinetics, ohmic, and mass-transport losses. For medium to highly degraded membrane electrode assemblies (MEA) caused by carbon corrosion, the model-based deconvolution method shows a considerable increase in the potential loss due to reduced oxygen transport, even at low current densities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".