Microinjection molding of multiwalled carbon nanotubes (<scp>CNT</scp>)–filled polycarbonate nanocomposites and comparison with electrical and morphological properties of various other <scp>CNT</scp>‐filled thermoplastic micromoldings
Bibliographic record
Abstract
A series of polycarbonate (PC)/multiwalled carbon nanotubes (CNT) nanocomposites were prepared by diluting a commercially available masterbatch using a neat PC resin in a lab‐scale batch mixer. The obtained nanocomposites were subjected to microinjection molding to fabricate microparts, which have a 3‐step decrease in thickness along the flow direction, under a defined set of processing conditions. The obtained microparts were mechanically divided into 3 different sections, namely, thick, middle, and thin sections, based on thickness. Morphology observations and electrical conductivity measurements were conducted to explore the evolution of microstructure within subsequent microparts. Additionally, a comparison of the electrical and morphological properties of stepped microparts of various thermoplastic polymers filled with CNT was studied. Results suggested that the selection of host polymers influences the dispersion of nanotubes within subsequent moldings, thereby affecting the electrical properties. The thermal stability of subsequent moldings deteriorated upon the addition of CNT, suggesting that the addition of CNT and the thermomechanical history experienced by the polymer melts in microinjection molding might cause a chain scission effect on PC. Raman spectroscopy analysis was used to study the orientation and properties of CNT in microparts.
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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".