Hydrogen Peroxide-Induced Controlled Degradation of Poly(lactic acid) for Melt-Blown Nonwovens
Bibliographic record
Abstract
This study aimed to increase the melt flow index (MFI) of poly(lactic acid) (PLA) through aqueous hydrogen peroxide (H 2 O 2 )-induced degradation. From this work, the impacts of hydrogen peroxide concentration and processing time were examined with samples that were prepared through reactive melt batch mixing. The hydrogen peroxide treatment and change in processing times were shown to decrease molar mass ( M W ) to various degrees, up to 70%. Overall crystallinity was only decreased with lower peroxide concentrations, while the glass transition temperature was reduced by 4 to 11% among all treated samples. Results confirm degradation via random chain scission with additional contributions from heat, hydrolysis, and potential alcoholysis. All results concur with the finding that lower peroxide concentrations paired with longer processing times result in the most effective degradation, while higher peroxide concentrations begin to favor cross-linking as processing time continues, resulting in increased M W . Therefore, lower peroxide concentrations are recommended for greater control over the extent of degradation. The resulting high MFI PLA can have applications that require low viscosity, such as melt-blown nonwoven materials where the treated sample was shown to produce finer, more uniform fibers at lower operating temperatures compared to raw PLA.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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 teacher head, 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".