Hydrolytic degradation of poly(lactic acid): Population balance modelling for simulating molecular weight distribution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it