Ultrasonic Irradiation Enhanced Cell Nucleation in Microcellular Poly(lactic Acid): A Novel Approach to Reduce Cell Size Distribution and Increase Foam Expansion
Bibliographic record
Abstract
The preparation of poly(lactic acid) (PLA) foam with a well-defined cell structure, high crystallinity, a high expansion ratio, and good mechanical properties is critical to its broader applications. However, achieving these properties in PLA foam simultaneously is challenging, because high crystallinity generally results in nonuniform cell nucleation and suppresses cell growth in the case of solid-state foaming. This study presents a novel approach using ultrasonic irradiation (UI) to achieve the desired properties in PLA simultaneously. As expected, CO 2 -saturated PLA samples at 5 MPa have a high crystallinity (23.4%), and foamed PLA samples at various foaming temperatures exhibit low foam expansion and nonuniform cell structure. By introducing UI at the very start of the foaming, however, the resultant PLA foams presented a significant and concurrent increase in cell structure uniformity and cell density: cell density increased about 2 orders of magnitude, the expansion ratio increased 1–2 times, the elongation at break increased 2 times, and the specific tensile strength increased 1.1 times, compared to samples without UI. Further investigation indicated that the enhanced cell nucleation induced by UI was the main reason for this unique phenomenon. Our study provides a simple but efficient and cost-effective method to fabricate PLA foams that possess excellent mechanical properties.
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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) | 0.001 | 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".