Overcoming the Fundamental Challenges in Improving the Impact Strength and Crystallinity of PLA Biocomposites: Influence of Nucleating Agent and Mold Temperature
Bibliographic record
Abstract
Poly(lactic acid) (PLA), one of the widely studied renewable resource based biopolymers, has yet to gain a strong commercial standpoint because of certain property limitations. This work is a successful attempt in achieving PLA biocomposites that showed concurrent improvements in impact strength and heat deflection temperature (HDT). Biocomposites were fabricated from a super toughened ternary blend of PLA, poly(ether-b-amide) elastomeric copolymer and ethylene-methyl acrylate-glycidyl methacrylate and miscanthus fibers. The effects of varying the processing parameters and addition of various nucleating agents were investigated. Crystallinity was controlled by optimizing the mold temperature and cycle time of the injection process. With the addition of 1 wt % aromatic sulfonate derivative (Lak-301) as a nucleating agent at a mold temperature of 110 °C, PLA biocomposites exhibited dramatic reduction in crystallization half time to 1.3 min with crystallinity content of 42%. Mechanical and thermal properties assessment for these biocomposites revealed a 4-fold increase in impact strength compared to neat PLA. The HDT of PLA biocomposites increased to 85 °C from 55 °C compared to neat PLA. Crystallization behavior was studied in detail using differential scanning calorimetry and was supported with observations from wide-angle X-ray diffraction profiles and polarized optical microscopy. The presence of a nucleating agent did not alter the crystal structure of PLA; however, a significant difference in spherulite size, crystallization rate and content was observed. Fracture surface morphology and distribution of nucleating agent in the PLA biocomposites were investigated through scanning electron microscopy.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".