Poly(vinylidene fluoride)/graphene nano-platelets electrically conductive composite foam for thermoelectric applications
Bibliographic record
Abstract
In this paper, we present the next generation of polymer based composite foam material fabricated from poly(vinylidene fluoride) (PVDF) and graphene nano-platelets (GNPs) as secondary fillers. We discovered that such composite material has thermoelectric properties and has the potential to be used in energy harvesting applications. The samples were fabricated though melt blending methods, which is a cheaper, simpler process and can be easily scaled up to industrial level for mass production. Our results indicate that melt blending processes can produce either similar or superior results compared to traditional solvent casting methods. In addition, we utilized a novel batch foaming method and successfully created closed-cell structure for the composite material. Our results also show that the thermal conductivity of PVDF/GNP foam samples have approximately an order of magnitude drop compared to solid samples, which is desired for thermoelectric materials. Furthermore, we observed a change in the electrical conductivity threshold of the GNP fillers after foaming. We report a Seebeck coefficient of 217 μV/K for 15 wt% GNP/PVDF foam samples, which is approximately 10 times higher than values reported previously.
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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) | 0.002 | 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".