Self-Diffusion of Nafion Ionomer in Confined Nanopores
Bibliographic record
Abstract
Ordered nanoporous structures offer the advantage of high surface area and well-defined pathway for transport of charged and neutral species. Such structures with electronically conductive matrix are attractive option as alternative architecture for catalyst layers of polymer electrolyte fuel cells (PEFCs). However, the internal surface of these nanoporous material must be coated with sufficiently thin films of ionically conductive polymers (ionomers) while leaving pores still open for transport of reactants. One approach for achieving such thin films coatings is via self-diffusion of ionomers from its dispersion into the nanoporous structure. However, the efficacy of this process depends on various factors including the composition of the ionomer dispersion, which affects the aggregation state or shape/size of ionomer in the dispersion and the shape/size of the pores. This work is concerned with the study of Nafion ionomer incorporation in model nanoporous structures with well-defined pore structure and size. A key challenge is quantification of the distribution of ionomer in these nanoporous structures. The thickness of ionomer films is anticipated to be a few nanometers, which limits the experimental observation to x-ray and electron microscopy based techniques both of which are known to damage polymers and also limit size of investigated volume of few tens of nanometers cubed. In this work, we have adopted laser scanning confocal fluorescence microscopy (LSCFM). The ionomer is first tagged with a fluophor. The tagged ionomer in dispersion diffuses through the pores of the nanoporous inorganic membranes of few tens of microns in thickness. The distribution of ionomer in the nanoporous membrane is mapped by LSCFM as function of pore size, ionomer concentration and exposure time. The figure below shows an example of Nafion ionomer distribution in anodized aluminum oxide nanoporous membrane.The results are cross-correlated with energy dispersive x-ray (EDX) characterization of selected samples. One of the peripheral questions, we wish to answer is whether Nafion ionomer can diffuse through sub-10 nm size pores that may exist in the secondary pores formed by aggregation of 30 nm Pt/C nanoparticle catalysts used for conventional catalyst layers of PEFCs. Furthermore, the methodology offers an attractive option for mapping long-range distribution of ionomer in tens of micron thick catalyst layers. Figure 1
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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 | 0.000 | 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 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".