Rheological and Multifunctional Properties of <scp>PBAT</scp> / <scp>CNT</scp> Nanocomposites With Diverse <scp>CNT</scp> Dispersion Quality Tuned by the Processing Parameters
Bibliographic record
Abstract
ABSTRACT This study investigates the effect of processing parameters on the dispersion quality of carbon nanotube (CNT) within poly(butylene adipate‐co‐terephthalate) (PBAT) matrix and the corresponding rheological behavior, electrical conductivity, dielectric properties, and electromagnetic interference shielding effectiveness (EMI‐SE). Neat PBAT and nanocomposites containing 1, 3, and 5 wt% CNT were prepared using an internal melt mixer with varying processing temperatures and screw speeds. Small amplitude oscillatory shear rheological analysis revealed that an increase in processing temperature resulted in a more significant increase in complex viscosity and storage modulus at low frequencies reflecting a better CNT dispersion and the formation of a stronger network. Higher mixing speeds also facilitated CNT dispersion more effectively; although further increases could cause the mechanical degradation of PBAT molecules and CNTs breakage. Scanning electron microscopy analysis confirmed the better and more uniform CNT dispersion when the nanocomposites were processed at higher temperatures and mixing speeds. The changes in electrical conductivity, dielectric permittivity, and EMI‐SE of the nanocomposites were consistent with the melt rheological results confirming a symbiotic correlation between these characteristics. Nanocomposites with 5 wt% CNT revealed DC conductivity and EMI‐SE values of about 10 −7 S/cm and 35–38 dB, respectively, when processed at 150°C and 100 rpm. These values, however, reached about 10 −3 S/cm and 44–50 dB, respectively, when nanocomposites were prepared at 190°C and 200 rpm. Under this preparation condition, the onset of rheological and electrical conductivity percolation thresholds was estimated at CNT contents of about 0.18 and 0.45 wt%, respectively. A higher processing temperature (190°C) and increased mixing speed (200 rpm) were found to be critical in achieving uniform CNT dispersion and enhancing the multifunctional properties of the nanocomposites.
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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".