Polyamide/Polystyrene Blend Compatibilisation by Montmorillonite Nanoclay and its Effect on Macroporosity of Gas Diffusion Layers for Proton Exchange Membrane Fuel Cells
Bibliographic record
Abstract
Abstract This work deals with a new route to modify polymer blend morphology in order to improve the porosity of gas diffusion layers (GDLs) for proton exchange membrane fuel cells (PEMFCs). First, electrically conductive polymer‐based blends were carefully formulated using a twin‐screw extrusion process. Blend electrical conductivity was ensured by the addition of high specific surface area carbon black and synthetic graphite flakes. Final GDL porosity, in particular its macroporosity, was generated by melt blending polyamide 11 (PA11) matrix with polystyrene (PS) followed by PS extraction with tetrahydrofuran (THF) solvent at room temperature. In order to improve GDL porosity by the optimisation of PS dispersion in the PA11 matrix, PA11/PS blends were compatibilised by the addition of 2 wt.‐% of clay. It was observed that both macroporosity and pore size distribution were beneficially modified after blend compatibilisation. Final GDL conductivity of about 1.25 S cm–1, a porosity of 53% and a specific pore surface area of 75 m2 g–1 were achieved.
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 | 0.000 |
| 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) | 0.001 | 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".