Statistical optimization of compatibilized blends of poly(lactic acid) and acrylonitrile butadiene styrene
Bibliographic record
Abstract
ABSTRACT A mixture design of experiment and subsequent regression analysis was used to study the effects of two additives on blends of poly(lactic acid) (PLA) and acrylonitrile butadiene styrene (ABS). Statistical analysis was used to find a blend with a balance of high toughness, strength, and stiffness. The blends were prepared by lab scale reactive extrusion and injection molding. Least‐square regression models of statistically significant effects were built by analysis of variance (ANOVA). Using these models, optimization studies were used to study the predicted maximum values of each measurement criteria. Very large increases were seen in the measured responses with relatively small changes in additive content. Compared to the neat blend without additives, the impact strength was increased by over 600%, the elongation at break was increased by over 1000%, the tensile strength increased by 11%, and the tensile modulus increased by over 7%. Surprisingly, the composite optimization, which included all measured criteria, occurred at a point that allowed all four criteria values to remain very close to their individual maximums. The result is a partially biobased blend that does not sacrifice strength or stiffness to achieve very high toughness. © 2016 The Authors Journal of Applied Polymer Science Published by Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 44516.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".