Mechanical accelerated stress testing and 4D degradation visualization to evaluate hydrocarbon-based membranes for fuel cell operation at intermediate temperatures
Bibliographic record
Abstract
Although fuel cell operation at intermediate temperatures ( e.g. , 100–120 °C) could potentially reduce cost in heavy-duty transportation, the associated durability challenges need systematic investigation. This work employs ex-situ dynamic mechanical analysis and pressure differential accelerated mechanical stress test together with in-situ fuel cell stability and durability tests to evaluate a sulfo-phenylated poly(phenylene)-based composite membrane for intermediate temperature operation. Compared to wet/dry cycling at 80 °C, the membrane shows ∼50 % lower stress of dehydration and 10x slower crack propagation at 110 °C, albeit the risks of creep failure are higher. Fuel cell testing at fixed current density (1.5 A cm −2 ) demonstrates stable performance at 110 °C and 34 % RH for 100 h, with no indication of irreversible degradation over longer durations. However, single-frequency electrochemical impedance spectroscopy shows that RH cycling at such temperatures increases the rate and depth of membrane drying. In-situ RH cycling durability tests combined with electrochemical diagnostics and 4D X-ray computed tomography visualization reveals membrane-electrode incompatibility and localized thinning near the edges as failure modes. Hence, fully hydrocarbon-based designs with proper stress concentration management are believed to enhance the durability of next generation fuel cell membranes at intermediate temperatures. • Operation at 110 °C, low RH reduces membrane dehydration stress and crack growth. • MEAs ran ∼100 h at 110 °C, 34 % RH, 1.5 A cm −2 with no sign of membrane degradation. • RH cycling AST at 110 °C is proposed, guided by stress–strain and hydration data. • Interface incompatibility in CCMs causes membrane failure during 110 °C RH cycling. • RH cycling at 110 °C induces localized creep and membrane thinning near the edges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".