High thermally conductive PLA based composites with tailored hybrid network of hexagonal boron nitride and graphene nanoplatelets
Why this work is in the frame
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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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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 it