Comparative Study on Experimental and Kerner Model Predictions of Viscoelastic Properties of Polyamide 6/ Polyvinyl Alcohol Blends
Bibliographic record
Abstract
The Polyamide 6 (PA6) / Polyvinyl alcohol (PVOH) blends of different compositions (80/20, 60/40 and 50/50) were prepared by melt mixing in a Haake Rheomixer. The selected blend systems (80/20 and 60/40) were modified with dicumyl peroxide (DCP) and tertiary butyl cumyl peroxide (TBCP). The dynamic mechanical properties of blends were systematically investigated with special reference to the effect of blend ratio and effect of presence of peroxide over a temperature range -20°C to 110°C. The effect of change in the composition of the polymer blends on tan δ was studied to understand the damping characteristics. The mean field theory developed by Kerner has been used to estimate the dynamic properties and the estimated values are compared with the experimental values. The loss tangent curve of the blend exhibited single transition peak corresponding to the glass transition temperature (Tg) of Polyamide 6. Kerner model was found to satisfactorily predict the viscoelastic properties of the blends with polyamide content in the range 50 to 80 wt% assuming PA6 as matrix and for all compositions except 80/20 assuming PVOH as matrix. The Kerner model predictions for the selected blend systems with peroxides are not satisfactory and the co-continuous morphology of the peroxide treated blends were revealed by SEM observations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".