Quantifying Spatio‐Operational Heterogeneity in Electrochemical Devices via Operando Correlative Neutron and X‐Ray Tomography
Bibliographic record
Abstract
Abstract Heterogeneity in component morphology and distribution, inherent in modern electrochemical devices, frequently limits device performance and durability. However, accurately characterizing heterogeneity is challenging as it requires high‐contrast detection of evolving multi‐material components and associated interfaces, and this often bottlenecks rational design. In this study, new insights into spatio‐operational heterogeneity are quantitatively revealed within multi‐component electrochemical systems using simultaneous neutron and X‐ray tomography (NeXT). In operando fuel cells, this technique uniquely offers independent yet simultaneous and correlated characterization of material distribution and morphology. This enables accurate contextualization of liquid water within all key component interfaces in sufficient detail to resolve previously unidentified 4D heterogeneity. First, 4D heterogeneity in membrane thickness and water content is found to depend strongly upon location and operating conditions, with membrane thickness variations up to 80 µm and membrane water content variation from dry to hydrated at 21 . Second, a direct experimental link is established between anisotropic humidification and local anisotropic swelling of the membrane. The observations lend unique insights into degradation mechanisms of the membrane and have notable implications on the practical durability of fuel cells. The proposed methodology is highly relevant to advancing multi‐material electrochemical devices (with evidence of applicability to batteries provided).
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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".