Processing and MWNT Composition Effects on the Thermal, Electrical and Mechanical Properties of PLA-MWNT Composites
Bibliographic record
Abstract
This paper investigates the processing and its effects and the effect of multiwall carbon nanotube (MWNT) composition on the thermal, electrical and mechanical properties of polylactide (PLA)-MWNT composites. The composite films were prepared by a solvent casting process using two solvents, chloroform and 1,4-dioxane. The dispersion of the MWNTs in PLA was examined using a scanning electron microscope and was found to be more improved when 1,4-dioxane was used as the solvent as compared to chloroform. The thermal characteristics of the composites were examined on Differential Scanning Calorimetry and Thermo-gravimetric Analysis. Composites prepared using 1,4-dioxane had greater improvements in composite decomposition temperature, glass transition temperature and displayed faster crystallization kinetics. The mechanical properties of the composites were tested in uniaxial tension. Composites prepared using chloroform had a lower modulus than composites prepared using 1,4-dioxane. The electrical AC conductivity of the composites was measured over a broad frequency spectrum. Composites prepared using 1,4-dioxane displayed electrical percolation at 0.5 wt.% MWNT in PLA while percolation was absent in 0.5 wt.% MWNT composites prepared using chloroform.
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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 | 0.001 |
| 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 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".