Multifunctional polymer nanocomposites with uniaxially aligned liquid crystal polymer fibrils and graphene nanoplatelets
Bibliographic record
Abstract
Polymer nanocomposites have actively been studied to replace metals in different emerging applications because of their light weight, superior manufacturability, and low processing cost. For example, extensive research efforts have been made to develop advanced thermally conductive polymer nanocomposites, with good processability, for heat management applications. In this study, liquid crystal polymer (LCP)-based nanocomposites have shown to possess much higher effective thermal conductivity (keff) (i.e., as high as 2.58 W/m K) than neat polymers (i.e., ∼0.2–0.4 W/m K). The fibrillation of LCP in LCP-graphene nanoplatelet (GNP) nanocomposites also demonstrated more pronounced increase in keff than that of polyphenylene sulfide (PPS)-GNP nanocomposites. Furthermore, ultra-drawing of LCP-GNP nanocomposite led to additional increase in the nanocomposite's keff because of the alignments of LCP fibrils and the embedded GNP. Experimental results also revealed that, unlike keff, the electrical conductivity (σ) of nanocomposites was unaffected by the types of polymer matrix. This exhibited that the keff and σ were promoted by different mechanisms, suggesting a potential route to tailor polymer nanocomposite's keff and σ independently.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".