Enhancement of thermoelectric conversion efficiency of polymer/carbon nanotube nanocomposites through foaming‐induced microstructuring
Bibliographic record
Abstract
ABSTRACT Semiconducting‐based materials such as bismuth antimony are current thermoelectric (TE) materials of choice because of their superior TE efficiency, which can be characterized by the dimensionless figure of merit (ZT). However, factoring the cost, weight, and environmental concerns, polymeric TE material systems have become attractive alternatives despite their lower ZT values. The potential to tailor the flexibility of polymeric TE materials also represent another key advantage, especially for wearable electronics. One of the key challenges to enhance their ZT values is the need to simultaneously increase the electrical conductivity and the Seebeck coefficient, while supressing the thermal conductivity. In this research, physical foaming is suggested as an innovative and effective processing strategy to circumvent this challenge. Multi‐walled carbon nanotube (MWCNT)/high density polyethylene (HDPE) nanocomposite foams were fabricated as a case example. Experimental results showed that introducing cellular structures in MWCNT/HDPE nanocomposites, loaded with 15 wt % MWCNT, would result in a 600‐fold increase in their ZT values. This great improvement was achieved through significantly reducing their effective thermal conductivity, while simultaneously increasing their electrical conductivity and Seebeck coefficients. The findings have proven that foaming can serve as a novel strategy to enhance the efficiency of various polymeric TE materials. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45073.
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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) | 0.001 | 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".