Design of thermal hybrid composites based on liquid crystal polymer and hexagonal boron nitride fiber network in polylactide matrix
Bibliographic record
Abstract
Development of high thermally conductive and electrically insulative composites is of interest for electronic packaging industry. Advancements in smaller and more compact electronic devices required improvements in packing materials, including their weight, thermal conductivity, and electrical resistivity. In addition, with the increasing environmental awareness, the usage of green (bio-based) alternatives was equally important. In the present study a hybrid based on fibers of highly concentrated hexagonal boron nitride (hBN) in liquid crystal polymer (LCP) matrix were fabricated. These hybrids were formed by arranging hBN platelets into LCP fiber form to reach high filler concentration and then randomly mix it in polylactide (PLA) matrix. With appropriate filler interaction within the hybrid, thermal conductivity similar to that of pure fiber could be achieved. Filler interaction may be tailored by optimizing the fibers aspect ratio. This study demonstrated the effect of random fillers in fibers shape in increasing the overall thermal conductivity of PLA polymeric hybrid using hBN and LCP fibers. © 2015 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2016, 54, 457–464
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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".