High thermally conductive PLA based composites with tailored hybrid network of hexagonal boron nitride and graphene nanoplatelets
Bibliographic record
Abstract
Bio‐based polymers and multifunctional polymeric composites are promising for the development of new environmentally sustainable materials and are becoming increasingly popular compared to their oil based counterparts. This research aims to develop new multifunctional bio‐based polymer composites with improved thermal conductivity and tailored electrical properties to be used as heat management materials in the electronics industry. A series of parametric studies were conducted to clarify the science behind the hybrid composites' behavior and their structure‐to‐property relationships. Using bio‐based polymers [e.g., polylactic acid (PLA)] as the matrix, heat transfer networks were developed and structured by embedding hexagonal boron nitride (hBN) and graphene nanoplatelets (GNP) in a PLA matrix. The effects of random uniform thermal hybrid networks of hBN‐GNP on improving the effective thermal conductivity ( k eff ) of produced composites were studied and compared. Composites were characterized with respect to physical, thermal, electrical, and mechanical properties for practical application in the electronics industry. The use of high thermally conductive hybrid filler systems, with optimized filler content, was found to promote the composites' effective thermal conductivity to more than 12 times over neat PLA. The thermally conductive composite is expected to provide unique opportunities to injection mold three‐dimensional, net‐shape, lightweight, and eco‐friendly microelectronic enclosures with superior heat dissipation performance. POLYM. COMPOS., 37:2196–2205, 2016. © 2015 Society of Plastics Engineers
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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".