Hydrolytic degradation of poly(lactic acid): Unraveling correlations between temperature and the three phase structures
Bibliographic record
Abstract
Hydrolysis significantly influences both the properties and degradability of poly(lactic acid), PLA. This work investigates the hydrolysis kinetics of PLA films as affected by degree of crystallinity and temperatures by considering the three-phase model structures (i.e., mobile amorphous, rigid amorphous, and crystalline). Molecular weight and three-phase fraction analyses were performed during hydrolysis to estimate the kinetic rates using phenomenological models. Results revealed that temperature significantly impacted PLA degradation, with distinct characteristics observed for each of these three phases. Above the glass transition temperature, the hydrolysis rates of PLA were comparable among samples with different crystallinity due to rapid water-induced crystallization of the amorphous phases, coupled with accelerated hydrolysis. In contrast, at below the glass transition temperature, the higher crystallinity sample exhibited a faster hydrolysis rate attributed to the presence of the rigid amorphous fraction. An increase in crystallinity introduced more defects due to limited mobility in the rigid amorphous fraction, influencing hydrolysis. The study provides valuable insights into the crucial relationship between temperature, crystallinity, and hydrolysis kinetics which are expected to be useful for predicting PLA degradation behavior during its intended applications and at its end of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".