Microinjection molding of polyoxymethylene/multiwalled carbon nanotubes composites with different matrix viscosities
Bibliographic record
Abstract
Abstract This study features the effect of matrix viscosity on the properties of carbon nanotubes reinforced polyoxymethylene (POM/CNT) microparts, which were obtained via melt blending and subsequent microinjection molding (μIM) processes, under a defined set of processing conditions. Results of compression molding and μIM were compared to assess the influence of processing methods (i.e., thermomechanical history) on the electrical and thermal conductivities, melting and crystallization behavior as well as the thermal degradation resistance of POM/CNT composites. Filler orientation in POM/CNT microparts was evaluated using Raman spectral analysis. The electrical conductivity measurements revealed that matrix viscosity plays a significant role in determining the distribution of CNT. Also, the extreme shearing conditions that prevail in μIM are unfavorable for the construction of random conductive pathways within the micromoldings, as corroborated by transmission electron microscopy. Although the thermal degradation resistance of both POM/CNT composites and corresponding microparts deteriorated with increasing filler concentration, samples prepared with higher matrix viscosity showed higher thermal stability when compared with lower matrix viscosity counterparts. This study provides valuable insights into fabricating multifunctional microparts for potential industrial applications in replacement of metallic components for precision electronic instruments.
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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) | 0.001 | 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".