An Improved Predictive Model for Effective Thermal Conductivity of Polymer Composites With Non-Dilute Filler Concentrations
Bibliographic record
Abstract
Abstract Most of the predictive models for thermal conductivity of composites are derived based on the assumption that the filler concentration in the matrix is dilute. This assumption leads to inaccurate predictions when filler concentration is essentially non-dilute and hence there is a need to propose a model that could handle a non-dilute filler concentration. In this work an improved and realistic model for effective thermal conductivity of polymer matrix composites with non-dilute filler’s concentrations is derived and validated by experiments. The proposed model can handle fillers with variable size and shapes. The derivation is based on the Bruggeman’s differential effective medium theory where the high volume fractions can be obtained by incrementally adding ‘small volume fractions’ into the ‘existing composite’ at each stage. The proposed model is validated by experimentally produced different series of ceramic particles-polymer composites. Differently sized and shaped alumina (Al2O3) & aluminum nitride (AlN) particulate fillers, and high density polyethylene (HDPE) & polypropylene (PP) matrices were used as the variable ingredients. Using different combinations of filler, matrix and particle size six different series of composites were produced with variable filler concentrations up to 50% by volume. The microstructure of the produced samples was studied by field emission scanning electron microscope to relate the morphology with the predictions. The predictions of proposed model are found in close agreement with the measured thermal conductivities. To understand the detailed effects of different parameters, parametric studies are presented and discussed. It is found that aspect ratio of particulate fillers is the most sensitive parameter to enhance effective thermal conductivity. Overall, the proposed model is proven to be useful in composite material design for heat transfer applications. It is expected that the proposed model will open new doors for the researchers and polymer composite industry to develop new composite designs for achieving ultrahigh thermal conductivities.
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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 | 0.000 | 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 | 0.001 |
| 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 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".