Microinjection molding of polypropylene/multi‐walled carbon nanotube nanocomposites: The influence of process parameters
Bibliographic record
Abstract
Polypropylene (PP) filled with 10 wt% multi‐walled carbon nanotube (CNT) nanocomposites were prepared via masterbatch dilution and subjected to microinjection molding (μIM) under various processing conditions. The molding conditions were altered by systematically changing the machine variables, such as: melt temperature, mold temperature, backpressure and injection velocity. A mold insert with a three‐step decrease in thickness along the flow direction was adopted. The effect of molding parameters on the electrical conductivity and dimensional stability of as‐molded microparts was evaluated using the design of experiments (DOE) method. The distribution of maximum shear rates along the flow direction was simulated via Moldflow, and the state of dispersion of CNT within the microparts was examined by scanning electron microscopy (SEM). In addition, the thermal behavior of the microparts molded from unfilled PP and PP/CNT 10 wt% nanocomposites at different sampling positions along the flow direction was studied by differential scanning calorimetry. Results showed that the crystallization process of unfilled PP taken from different regions of the microparts is temperature dependent, which was ascribed to the variations of shearing effects undergone by the polymer melt during μIM, while this effect is not significant for CNT loaded systems. POLYM. ENG. SCI., 58:E226–E234, 2018. © 2017 Society of Plastics Engineers
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".