Study of the effect of processing conditions on the co‐injection of PBS/PBAT and PTT/PBT blends for parts with increased bio‐content
Bibliographic record
Abstract
ABSTRACT This work studies the effect of processing parameters on mechanical properties and material distribution of co‐injected polymer blends within a complex mold shape. A partially bio‐sourced blend of poly(butylene terephthalate) and poly(trimethylene terephthalate) PTT/PBT was used for the core, with a tough biodegradable blend of poly (butylene succinate) and poly (butylene adipate‐co‐terephthalate) PBS/PBAT for the skin. A ½ factorial design of experiments is used to identify significant processing parameters from skin and core melt temperatures, injection speed and pressure, and mold temperature. Interactions between the processing effects are considered, and the resulting statistical data produced accurate linear models indicating that the co‐injection of the two blends can be controlled. Impact strength of the normally brittle PTT/PBT blend is shown to increase significantly with co‐injection and variations in core to skin volume ratios to have a determining role in the overall impact strength. Scanning electron microscope images were taken of co‐injected tensile samples with the PBS/PBAT skin dissolved displaying variations of mechanical interlocking occurring between the two blends. © 2014 The Authors Journal of Applied Polymer Science Published by Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 41278.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".