Development and characterization of solid and porous polylactide‐multiwall carbon nanotube composites
Bibliographic record
Abstract
Abstract This article describes the fabrication of solid and porous polylactide (PLA)‐multiwall carbon nanotube (MWNT) composites prepared using melt blending and subsequent batch processing of porous structures. The morphology and thermal, rheological and electrical properties of the PLA‐MWNT composites prepared with MWNT concentrations of 0, 0.5, 1, 2, and 5 wt% were characterized. The composite structure consisted of identifiable regions of MWNT aggregation and MWNT dispersion. Increasing MWNT content was found to increase the thermal stability and crystallization kinetics of PLA. The addition of MWNT to PLA significantly increased the melt viscosity and electrical conductivity of the composites. Based on rheological and electrical measurements, a continuous MWNT network structure in PLA was found to form when the concentration of MWNT is increased from 0.5 wt% (0.33 vol%) to 1 wt% (0.66 vol%). As many current day applications of polymers and polymer composites require lightweight and low‐density materials, porous PLA‐MWNT composites were fabricated from a batch porous structure processing technique. Porous PLA‐MWNT composites containing 2 and 5 wt% MWNT had lower relative densities, which is attributed to the higher viscosity of the composites suppressing collapse of the porous structure during processing. POLYM. ENG. SCI., 2011. © 2010 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 | 0.000 |
| 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".