Effect of flow induced alignment on the thermal conductivity of injection molded carbon nanotube‐filled polystyrene nanocomposites
Bibliographic record
Abstract
Carbon nanotube (CNT) nanocomposites with a polystyrene thermoplastic matrix were injection molded and the high shear stress exerted during the injection process partially enabled the alignment of the CNTs in the flow direction. Nanocomposites with different CNT loadings and degrees of alignment were produced, and their thermal conductivities were measured based on ASTM D5470. The results were compared with compression molded samples featuring random alignments of CNTs. The results showed that the injection molded samples possessed anisotropic thermal conductivities, due to the partial alignment of the CNTs in the flow direction. The effective medium approach was used to analytically estimate the thermal conductivity of the molded samples. Good agreement was observed between the experimental and analytically simulated results in lower CNT concentrations (less than 5 wt% of CNT). Using transmission electron microscopy pictures taken of the nanocomposites, the alignment of CNTs in the thermoplastic matrix were modeled; and their thermal conductivities were simulated using the finite element method. Good agreement was observed between the experiments and simulated results. POLYM. ENG. SCI., 55:753–762, 2015. © 2014 Society of Plastics Engineers
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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".