Modeling and characterization of micro-porous layers in fuel cells
Bibliographic record
Abstract
Modern hydrogen powered polymer electrolyte fuel cells (PEFCs) utilize a micro-porous layer (MPL) consisting of carbon nanoparticles and polytetrafluoroethylene (PTFE) to enhance the transport phenomena of reactants and products adjacent to the active catalyst layers.The use of MPLs in advanced PEFCs has aided manufacturing of higher performing fuel cells with substantially reduced cost.However, the underlying mechanisms are not yet completely understood due to a lack of information about the detailed MPL structure and properties.In the present work, the 3D phase segregated nanostructure of an MPL is revealed for the first time through the development of a customized, non-destructive procedure for monochromatic nano-scale X-ray computed tomography (NXCT) visualization.Utilizing this technique, it is discovered that PTFE is situated in conglomerated regions distributed randomly within connected domains of carbon particles; hence, it is concluded that PTFE acts as a binder for the carbon particles and provides structural support for the MPL.Exposed PTFE surfaces are also observed that will aid the desired hydrophobicity of the material.Additionally, the present approach uniquely enables phase segregated calculation of effective transport properties, as reported herein, which is particularly important for accurate estimation of electrical and thermal conductivity.Additionally, two analytical models are developed for estimation of thermal conductivity and diffusivity of MPL, as a function of structural properties, i.e., porosity and pore size.Based on these models, the pore size distribution and porosity of an MPL with a high diffusivity and thermal conductivity is proposed.Finally, a performance model is developed that is used to study the effects of MPL properties on fuel cell performance.Overall, the new imaging technique and associated findings may contribute to further performance improvements and cost reduction in support of fuel cell commercialization for clean energy applications.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".