Development of thermally conductive polymer matrix composites by foaming‐assisted networking of micron‐ and submicron‐scale hexagonal boron nitride
Bibliographic record
Abstract
ABSTRACT Thermally conductive polymer matrix composite (PMC) foams with effective thermal conductivities ( k eff ) higher than their solid counterparts have been developed for the first time. Using a material system consists of low density polyethylene and micron‐scale or submicron‐scale hexagon boron nitride platelets as a case example, this article demonstrates that foaming‐assisted filler networking is a feasible processing strategy to enhance PMC's k eff , especially at a low hBN loading. Parametric studies were conducted to identify the structure‐to‐property relationships between foam morphology (e.g., cell population density, cell size, and foam expansion) and the PMC foam's k eff . In particular, there exists an optimal cell size to maximize the PMC foam's k eff for foams with up to 50% volume expansion. However, an optimal cell size is absent for PMC foams with higher volume expansion. X‐ray diffraction (XRD) analyses reveal that both the presence of hBN platelets and foam expansion promoted the crystallization of LDPE phase. Moreover, the XRD spectra also provide evidence for the effect of foam expansion on the orientation of hBN platelets. Overall, the findings provide new directions to design and fabricate thermally conductive PMC foams with low filler contents for heat management applications. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133 , 42910.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".