Synergistic effects of hybrid fillers on the development of thermally conductive polyphenylene sulfide composites
Bibliographic record
Abstract
Abstract The future of integrated circuits with three‐dimensional chip architecture hinges on the development of practical solutions for the management of excessive amounts of heat generation. This requires new polymer–matrix composites (PMCs), with good processibility, high effective thermal conductivity (keff), and low but tailored electrical conductivity (σ). This article explores the synergy of hybrid fillers: (i) hexagonal boron nitride (hBN) platelets with different sizes and shapes; (ii) hBN platelets with carbon‐based fillers promoting the keff of the polyphenylene sulfide (PPS) composites. It explores the promotion of interconnectivity among the fillers in the PPS matrix, leading to higher keff, by the uses of hybrid fillers. It discusses using carbon‐based fillers as secondary fillers to tailor the PMCs' σ. Finally, it presents the effects of hybrid fillers on the PMCs' coefficient of thermal expansion. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013
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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".