A review on high thermally conductive polymeric composites
Bibliographic record
Abstract
Abstract Polymers are known as thermally insulated materials with reported effective thermal conductivity (Keff) in the range of 0.1 to 0.5 Wm−1 K−1. However, increasing demand for smaller and more powerful electronics has created the need for thermally conductive polymers for use in heat exchangers and electronic packaging applications. Given this background, much research has been done over the past two decades to increase the Keff of polymers. Based on the strategy involved, those works can be divided into two main categories: (i) increasing the Keff of the neat polymer by aligning its chains orientation; and (ii) increasing polymer Keff by fabrication of polymeric composites with thermally conductive filler networks. Among these two strategies, the former is limited to nanoscale laboratory research and is difficult to scale up for mass production. Therefore, this work is mainly focused on the latter category, thermally conductive polymeric composites, which has a higher potential for large‐scale production. This work aims to summarize, evaluate, and highlight the successful strategies of the recent efforts in enhancing the thermal conductivity of polymer composites. The major achievements, future challenges, and the outlooks of high thermally conductive polymeric composites are presented by analyzing the results of about 300 works.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".