Synthesis and Characterization of LDPE-Carbon Nanotube Composite Foams
Bibliographic record
Abstract
This study details the synthesis and characterization of composites and composite foams of low-density polyethylene (LDPE) and multi-walled carbon nanotubes (MWCNT). LDPE-MWCNT composites were prepared by melt blending the components in a twin screw compounder and their foams were produced by batch foaming using CO2 as the blowing agent. The composites were characterized for dispersion using SEM and image results indicate good dispersability of MWCNT in LDPE with the formation of a MWCNT network in the LDPE matrix. Thermal and rheological properties of the composites were characterized and results indicate that even a small amount (1 wt.%) of MWCNTs can significantly affect the crystallization kinetics and the rheological behavior. Batch foaming results of the composites depict MWCNTs as heterogeneous nucleation sites for gas bubbles as indicated by the increase in cell density of the composite foams when compared to LDPE foams.
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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 | 0.001 | 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".