Influence of processing conditions on the evolution of morphology in <scp>PVDF</scp>/<scp>PS</scp> and <scp>PP</scp>/<scp>PS</scp> polymer blends: Examining the processing‐phase inversion mechanism
Bibliographic record
Abstract
Abstract Understanding morphological changes in polymer blends in the initial melting region of an extruder is crucial for optimizing the polymer mixing process. In blends with a large difference in their softening temperatures, a processing‐phase inversion transition occurs when a larger volume‐fraction of the higher melting component is present. The morphological changes in 80/20 PVDF/PS blends were examined (where PVDF forms the major phase) in an internal mixer under different processing conditions, such as preset temperature program, fill volume and rotor speed. We studied the torque behavior, energy consumption during mixing and phase inversion point for both 80/20 PVDF/PS and 80/20 PP/PS blends. PP needed 2.4 times higher mechanical energy to be deformed and to achieve a similar phase inverted state, compared to PVDF. To examine the effect of the minor phase properties, two 80/20 PVDF/minor ph.ase systems were examined: (i) a low‐melting viscous EVA (ethylene vinyl acetate copolymer), and (ii) a low viscosity PCL (polycaprolactone). Using a range of techniques, such as torque monitoring, optical microscopy, SEM etc., it is established that the nature of the minor phase (amorphous vs. semicrystalline), minor phase melt viscosity and the exact processing parameters influence the point at which the phase inversion occurs.
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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) | 0.002 | 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".