Quantifying Spatiotemporal Heterogeneity within Fuel Cells Using Simultaneous Neutron and X-Ray Tomography (NeXT)
Bibliographic record
Abstract
Heterogeneity within electrochemical devices, such as fuel cells, influences their performance and durability in ways that are not fully understood. 4-dimensional (4-D; 3 spatial dimensions and time) operando visualization techniques such as X-ray (1-2) and neutron (3) tomography are powerful tools to probe heterogeneity within operating electrochemical devices in high spatiotemporal resolution. Combining neutron and X-ray tomography (NeXT) (3-4) simultaneously offers unique advantages over single-modality methods, especially in terms of enhanced contrast between materials. There is a significant opportunity to utilize NeXT to characterize and quantify heterogeneity within electrochemical devices during cell operation. In this study, we utilize quantitative NeXT to identify spatiotemporal heterogeneity in the morphology of the membrane electrode assembly (MEA) and water distribution within the porous layers and the membrane. A custom interfacial tracking algorithm is utilized to accurately characterize 4-D morphology and boundaries of cell components. Heterogeneity is found to depend upon location with respect to cell components and operating conditions (such as current density and inlet relative humidity). Variations in the MEA morphology is dominated by variations in membrane morphology, whereby variations of up to 80 μm is observed in membrane thickness for N117 membranes (Chemours, USA). We find that the interface between the gas diffusion layer and the enclosing gasket is a high porosity region that accumulates liquid water during cell operation, and this liquid accumulation leads to high membrane hydration and membrane swelling near the interface. With this study, we demonstrate the viability of quantitative NeXT to characterize operando heterogeneity within multi-component electrochemical devices, taking us a step closer towards rational control and design of inherent heterogeneity within these devices. References 1. Y. Singh, R. T. White, M. Najm, T. Haddow, V. Pan, F. P. Orfino, M. Dutta, and E. Kjeang, J. Power Sources., 412 (2019): 224-237. 2. Xu, Hong, Minna Bührer, Federica Marone, Thomas J. Schmidt, Felix N. Büchi, and Jens Eller, J. Electrochem. Soc., 168, no. 7 (2021): 074505. 3. J. M. LaManna, Y. Yue, T. A. Trabold, J. D. Fairweather, D. S. Hussey, E. Baltic and D. L. Jacobson, Meet. Abstr. - Electrochem. Soc., 32 (2017). 4. Shrestha, Pranay, Jacob Michael LaManna, Kieran Fahy, Junseob Kim, ChungHyuk Lee, Keonhag Keonhag Lee, Eli Baltic, David Jacobson, Daniel Hussey, and Aimy Bazylak, Meet. Abstr. - Electrochem. Soc. 242, no. 39, pp. 1451-1451 (2022).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".