Determination of Key Influencing Factors on Thermal Conductivity Enhancement of Graphene Nano-Platelets Reinforced Epoxy
Bibliographic record
Abstract
Dispersing micro and nanoparticles into polymeric materials has proven to induce multifunctional properties in polymer composites, including their magnetic, electrical, thermal and mechanical characteristics. Adding carbon-based nanoparticle inclusions such as Graphene Nano-Platelets (GNP) to polymeric materials typically leads to thermal, electrical and mechanical property enhancements. Raising thermal conductivity by adding highly thermally conductive fillers particularly harbors great potential given diverse possible applications, such as in the electronics industry. In this study, the focus is on increasing the thermal conductivity of an epoxy by dispersing GNP in the pre-polymer. The influence of various process parameters such as filler loading, influence of swelling, use of solvent and additives, sonication time and amplitude, as well as curing cycle were determined. By means of a Design of Experiments approach the parameters which have the greatest effect on thermal conductivity enhancement were identified. Through this study a better understanding of the influence of process parameters was achieved in a qualitative and quantitative manner. The study further aids in selecting ideal process parameters for maximum thermal conductivity enhancements.
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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 | 0.001 |
| 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".