Natural Degradation Behavior of Poly(lactic acid) Nanocomposite Films and the Adsorption Behavior of Degraded Products on Cu(II)
Bibliographic record
Abstract
In this study, the degradation behavior of poly(lactic acid) nanocomposite films (PLA/Hec-g@PS) under extreme natural environments was investigated, and the degraded PLA based films were applied to adsorb Cu(II). During the early and midstages of degradation, the surface roughness and crack propagation rate of PLA/Hec-g@PS films were significantly lower than those of PLA films. This could be due to the fact that Hec-g@PS enhanced the interaction forces between C–O–C + CH 3 and C═O in the PLA chains, thereby mitigating the degradation of PLA. Neural network predictions indicated that the molecular weight of PLA films decreased to 30% after 1344 days, whereas PLA/Hec-g@PS films reached the same reduction in 1451 days, extending the lifespan of PLA by 1.08 times. The environmental impact of PLA/Hec-g@PS was further assessed by their adsorption behavior toward Cu(II). It was found that PLA films adsorbed 244.51 μg/g of Cu(II), while PLA/Hec-g@PS films adsorbed 372.63 μg/g of Cu(II). The isotherm adsorption model showed that the maximum adsorption capacities ( q m ) of PLA and PLA/Hec-g@PS were 326.60 μg/g and 441.51 μg/g, respectively. This improvement offers PLA based films new possibilities for applications in wastewater treatment and soil remediation.
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".