Influence of mold cavity thickness on electrical, morphological and thermal properties of polypropylene/carbon micromoldings
Bibliographic record
Abstract
Abstract In this work, a comparative study on the electrical conductivity (σ) and thermal properties of polypropylene (PP)/carbon microparts with different part thickness (namely, 0.85 and 0.50 mm) is reported. Two different types of carbon filler (i.e., CNT and CB) were adopted to study the efficacy of different carbon fillers in improving the σ of PP/carbon microparts. In general, the σ of 0.85 mm thickness microparts were higher than the 0.50 mm thickness microparts, regardless of the carbon filler type and testing directions. This suggested that higher shearing conditions that prevailed in the microinjection molding (μIM) process were unfavorable for the formation of intact conductive pathways in corresponding moldings, albeit the distribution of carbon fillers turned better with increasing shear rates, as confirmed by morphology observations. Differential scanning calorimetry results showed that prior thermomechanical histories (including melt blending and μIM) experienced by the polymer melts had an influence on the thermal behavior of subsequent moldings. Also, there existed a strong shear flow-induced crystallization of polymer chains during μIM because the crystallinity of microparts was higher than that of feed materials.
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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 | 0.000 |
| 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".