Development of Porous Electrode Gas Diffusion Layers for Proton Exchange Membrane Fuel Cells
Bibliographic record
Abstract
The aim of this work was to develop a porous film structure for an electrode gas diffusion layer (GDL) used for proton exchange membrane fuel cells (PEMFCs). This film was made from a matrix composed of two immiscible polymers filled with a mixture of electrically conductive materials fabricated via a twin-screw extrusion process followed by selective extraction of one of the two polymers. The matrix consisted of low-viscosity polypropylene and polystyrene (PS) and the conductive additives were composed of high specific surface area carbon black and synthetic flake graphite. The conductive blends were first compounded in a corotating twin-screw extruder and subsequently extruded through a flexible film die to obtain a GDL film of around 500μm having high electronic conductivity. The PS phase was then extracted with tetrahydrofuran (THF) solvent and a film of controlled porosity was generated. The morphology of the GDL porous structure was then analyzed by scanning electron microscopy. GDL porosity characterization was done by both Brunauer–Emmett–Teller (BET) and mercury-intrusion porosimeter. The effects of PS concentration and extraction time with THF on GDL porosity were also studied. Pore-size distribution obtained by BET and mercury-intrusion porosimetry revealed that the GDL structure is composed by both mesopores and macropores. Mesopores represent more than 60% of the total pore volume inside the GDL film.
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 | 0.001 |
| Open science | 0.001 | 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".