Development of Micro- to Macropores in Conductive Polymer-Based Gas Diffusion Layers for Proton Exchange Membrane Fuel Cells
Bibliographic record
Abstract
The aim of this work is to improve the porosity of gas diffusion layers (GDLs) for proton exchange membrane fuel cell electrodes. These GDLs are made by twin-screw extrusion process from conductive formulations composed of polyamide11 (PA11)/polystyrene (PS) as the polymer matrix phase and an appropriate mixture of carbon black (CB) and graphite (GR) as the conductive additives. Final GDL porosity, especially macroporosity, was generated by selective extraction of the PS phase using adequate solvents. Since the generation of pores was found to be directly related to blend morphology, several blend compositions were studied and small amounts (2–6 wt %) of montmorillonite (MMT) clay were used as compatibilizer to improve the dispersion of the PS phase inside the PA11. It was observed that, although GDL volume porosity was not or slightly affected by the addition of MMT compatibilizer, its pore specific surface area was clearly increased. For GDLs made from a blend composed of 65 wt % of PA11/PS (30/70) and 35 wt % of GB/GR (57/43), an increase from 53 m2/g (with no MMT) to around 75 m2/g (with 2 wt % MMT) was obtained. This improvement within the addition of MMT was attributed to the modification of the dispersion state of PS phase. Such modification led to a higher connectivity of pores and consequently more accessibility to the micro/mesopores of CB and GR. The major changes observed with the incorporation of MMT compatibilizer were obtained for the small pore sizes (in the range of 10–400 nm). Depending on MMT content, a considerable shift of pore size distribution in this range to smaller or higher values was obtained. Then the MMT compatibilization could be considered as an interesting route to tailor GDL porous properties.
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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".