Modelling of effective thermal conductivity of polymer matrix composite foams with biaxially aligned filler networks
Bibliographic record
Abstract
Recent research revealed potentials to develop polymer matrix composite foams filled with thermally conductive filler network as light-weight thermal management materials. Since polymeric foams are commonly used for thermal insulation, the concept of thermally conductive polymer matrix composite foams seems to be counter-intuitive, and the underlying factors that govern polymer matrix composite foam’s effective thermal conductivity ( k eff ) were not clear. In this context, this paper develops new models to predict polymer matrix composite foams’ k eff and to elucidate the dependence of k eff on their cellular morphology. Linear low density polyethylene–hexagonal boron nitride composite foams were used as case examples to verify the model. The model demonstrated that the composite foam’s k eff would be promoted when the volume expansion was over a threshold percentage. At low hexagonal boron nitride loadings (e.g. 10 vol.%) and fixed cell size, linear low density polyethylene–hexagonal boron nitride foams’ k eff increased with volume expansion percent through an increase in cell population density. Constrained foaming with preferential expansion in the heat flow direction also enhanced their k eff .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".