Polymer Electrolyte Membrane Fuel Cells: Characterization and Diagnostics
Bibliographic record
Abstract
The normal operation of proton exchange membrane fuel cells (PEMFCs) can induce significant temperature, humidity, pressure or concentration gradients across the cell’s active area. The overall performance and stability can be affected negatively by these gradients. For example, localized transient changes in environmental conditions may contribute to material degradation, and ultimately, to cell failure. We report on new diagnostic techniques and methodologies that have been developed to characterise PEMFC material properties, and the inhomogeneities across the active area of a working cell. Specifically, we will describe ex situ techniques for membrane electrode assembly (MEA) materials characterisation, and in situ techniques for the spatially resolved characterization of PEMFC performance. These techniques incorporate independent control over local potentials with concurrent membrane water content characterization (via high-frequency resistance measurements). Our testing hardware features sixteen fully isolated segments over a 42 cm2 active area, reference electrode capabilities, and individual segment control. This configuration enables the measurement of localised anode and cathode overpotentials separately. The technique’s versatility will be illustrated by effective platinum surface area (EPSA) measurements across the active under accelerated stress tests (ASTs) conducive to platinum dissolution. Each segment in a membrane electrode assembly was exposed to 10,000 cycles of Pt dissolution AST separately. The beginning-of-life EPSA was calculated via cyclic voltammetry, and the catalyst layer morphology and properties were found to change during the AST. Impedance measurements were obtained in H2/N2 conditions in order to map the ionic conductivity and the polarization resistance of the catalyst layer across the segmented cell. Higher EPSA and performance loss were observed in segments near the fuel cell outlet compared to those near the reactant inlets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
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".