Hydrolytic degradation of poly(lactic acid): Population balance modelling for simulating molecular weight distribution
Bibliographic record
Abstract
Poly (lactic acid) (PLA) is one of the most promising biobased and biodegradable polymers able to replace several fossil-based plastics for packaging and other applications. However, PLA is susceptible to hydrolytic degradation, impacting its overall service performance and end-of-life. The molecular weight distribution (MWD) is a critical parameter that provides insights during hydrolytic degradation. In this study, we introduced a population balance model, utilizing the high-order moment-conserving method of classes, to describe the MWD during the hydrolytic degradation of amorphous PLA film at 45 °C and 65 °C and expanded to 85 °C. The phenomenological model provided hydrolysis constants that clarified noncatalytic and autocatalytic reaction mechanisms and information on specific chain scission of a particular length. Our predictions demonstrate a promising alignment in weight location and distribution shape with the experimental MWDs observed throughout the hydrolytic process of PLA. One notable advantage is the MWD simulation, conducted over an extended time frame. Furthermore, this predictive capability extends to forecasting the lifetime of PLA films at various temperatures within the tested range, thereby fostering insights into PLA hydrolysis applicable to real-life scenarios and supporting environmentally conscious degradation practices. • A population balance model for simulating PLA molecular weight distribution is developed. • The model aligns closely with experimentally observed molecular weight distributions. • Predictions extend to forecasting the lifetime of PLA films across different temperatures. • The approach provides valuable insights for simulating and predicting PLA lifetime.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".