Controlled Degradation of PBAT for PBAT/PLA Blend Melt‐Blown Nonwovens
Bibliographic record
Abstract
ABSTRACT This study utilized the chain‐scission capability of peroxides, such as aqueous hydrogen peroxide (H2O2), to induce controlled degradation of poly(butylene adipate‐co‐terephthalate)(PBAT) through reactive batch mixing with the objective of increasing the melt flow index (MFI). The effects of the peroxide concentration and processing time were examined, and the results showed that concentration had the greatest impact, with an approximate 450% increase in MFI at the optimal peroxide concentration. On the other hand, the peroxide treatment had a minimal impact on crystallinity and thermal properties. Degradation was deemed to occur chiefly via random chain scission with contributions from heat and hydrolysis, as supported by proton nuclear magnetic resonance spectroscopy (+HNMR). The treated PBAT sample showed promise in melt‐blown micro‐fiber production, producing fibers with a 68% smaller average diameter than that of the untreated PBAT. The treated PBAT was then blended with various levels of high MFI poly(lactic acid) (PLA) to optimize properties and cost of the resulting micro‐fiber material. As expected, the blends demonstrated increased tensile strength and decreased elongation at break with higher PLA contents, up to 30% and 13%, respectively, successfully balancing the material properties of the PBAT starting material. Despite these favorable tensile properties, the material blend remained suboptimal due to evidence of phase separation. To bridge this incompatibility, maleation was implemented, resulting in a polymer characterized by improved homogeneity, thereby enabling the production of uniform fibers without compromising desired tensile properties. The melt‐blowing generated PBAT‐PLA micro‐fibers can have applications as a sustainable alternative for polypropylene‐dominated HVAC air filters, medical masks, etc.
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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 | 0.000 | 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) | 0.001 | 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".