Development and Characterization of Conductive Materials for PEMFC Bipolar Plates
Bibliographic record
Abstract
The aim of this work was to develop and characterize electrically conductive bipolar plates (BPPs) used in proton exchange membrane fuel cells (PEMFCs). These BPPs were made from highly conductive blends of polyethylene terephthalate (PET) and polyvinylidene fluoride (PVDF), as matrix phase. The conductive materials were developed from carefully formulated blends composed of conductive carbon black powder (CB) mixed with pure PET, PVDF, or with PVDF/PET systems. They were first developed by twin screw extrusion (TSE) process then compression molded to give BPP final shape. Focus was made on the effect of crystallization of PVDF and PET polymers on the electrical through-plane resistivity of BPPs. It was observed that lower resistivity was obtained with PVDF/CB blends due to the higher interfacial energy between the PVDF matrix and CB and also the higher density and crystallinity of PVDF, compared to those of PET. For the different systems studied, slow cooling rates helped to attain the lowest values of through-plane resistivity since higher PET crystallinity led to smaller amorphous region in which CB was more concentrated. In addition, BPPs made from (PVDF/PET)/CB blends led to lower through-plane resistivity when the PVDF/PET phase had a co-continuous morphology. This is mainly due to the selective localization of the CB in the PET phase leading to a denser conductive carbon network. The lowest through-plane resistivity was around 0.3 Ohm.cm obtained with a (50/50 PVDF/PET)/CB filled with 30 wt% CB.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".