Tailoring anisotropic thermal conductivity by varying filler particle organization in nickel-polydimethylsiloxane composites
Bibliographic record
Abstract
Anisotropic properties can be imparted to composite materials by arranging filler particles along specific directions inside the polymer matrix. These anisotropic patterns can be produced through dynamic field-assisted assembly of the filler particles during additive manufacturing. Using finite element analysis, we explore how chainlike arrangements of nickel particles embedded in a polydimethylsiloxane matrix modify bulk thermal conductivities in the axial and transverse directions. The axial conductivity increases up to nine times of the matrix conductivity with increasing filler volume fraction. While the axial conductivity decreases with increasing interparticle spacing, the transverse conductivity is uninfluenced. When particles within a chain are arranged in a zigzag pattern, increasing the interparticle zigzag angle decreases axial conductivity but increases transverse conductivity. As that angle increases to ∼55 º, the axial conductivity approaches a minimum, while the transverse conductivity approaches its maximum. An empirical model that includes effects of interparticle spacing and zigzag angle to predict the anisotropic thermal conductivity of a composite containing particle chains is presented. These results are relevant for the material design of particulate-reinforced polymer composites for advanced field-assisted additive manufacturing strategies.
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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) | 0.000 | 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) | 0.000 | 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 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".