Enhanced thermal conductivity of novel multifunctional polyphenylene sulfide composites embedded with heat transfer networks of hybrid fillers
Bibliographic record
Abstract
Today's smaller, more powerful electronic devices, communications equipment, and lighting apparatus required optimum heat dissipation solutions. Traditionally, metals are widely known for their superior thermal conductivity; however, their good electrical conductivity has limited their applications in heat management components for microelectronic applications. This prompts the requirement to develop novel plastic composites that satisfy multifunctional requirements thermally, electrically, and mechanically. Furthermore, the moldability of polymer composites would make them ideal for manufacturing three-dimensional, net-shape enclosures and/or heat management assembly. Using polyphenylene sulfide (PPS) as the matrix, heat transfer networks were developed and structured by embedding hexagonal boron nitride (BN) alone, blending BN fillers of different shapes and sizes, as well as hybridizing BN fillers with carbonaceous nano- and micro-fillers. Parametric studies were conducted to elucidate the effects of types, shapes, sizes, and hybridization of fillers on the composite's thermal and electrical properties. The use of hybrid fillers, with optimized material formulations, was found to effectively promote a composite's thermal conductivity. This was achieved by optimizing the development of an interconnected thermal conductive network through structuring hybrid fillers with appropriate shapes and sizes. The thermal conductive composite affords unique opportunities to injection mold three-dimensional, net-shape microelectronic enclosures with superior heat dissipation performance.
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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".