A Roadmap Review of Thermally Conductive Polymer Composites: Critical Factors, Progress, and Prospects
Bibliographic record
Abstract
Abstract Recently, the need for miniaturization and high integration have steered a strong technical wave in developing (micro‐)electronic devices. However, excessive amounts of heat may be generated during operation/charging, severely affecting device performance and leading to life/property loss. Benefiting from their low density, easy processing and low manufacturing cost, thermally conductive polymer composites have become a research hotspot to mitigate the disadvantage of excessive heat, with potential applications in 5G communication, electronic packaging and energy transmission. By far, the reported thermal conductivity coefficient (λ) of thermally conductive polymer composite is far from expectation. Deeper understanding of heat transfer mechanism is desired for developing next generation thermally conductive composites. This review holistically scopes current advances in this field, while giving special attention to critical factors that affect thermal conductivity in polymer composites as well as the thermal conduction mechanisms on how to enhance the λ value. This review covers critical factors such as interfacial thermal resistance, chain structure of polymer, intrinsic λ value of different thermally conductive fillers, orientation/configuration of nanoparticles, 3D interconnected networks, processing technology, etc. The applications of thermally conductive polymer composites in electronic devices are summarized. The existing problems are also discussed, new challenges and opportunities are prospected.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".