Green, Sustainable, and Melt-Compoundable PLA Composites Reinforced with Spray-Dried Lignocellulosic Nanofibrils for Enhanced Barrier and Mechanical Properties
Bibliographic record
Abstract
This study presents the first integration of spray dried (SD), non-modified, lignocellulosic nanofibrils (LCNF) into polylactic acid (PLA) by melt blending. Originating from industrial forestry waste, SD LCNFs are an inexpensive, non-toxic, and abundantly accessible drop-in filler whose production is facile, continuous, and highly scalable. Addition of SD LCNF into PLA yields enhanced barrier and mechanical performance due to SD LCNF’s alteration of the crystalline microstructure and fracture dynamics. Incorporating 1–1.5 wt% of SD LCNFs into PLA results in significant enhancements: tensile strength by 32.8%, toughness by 44.6%, water vapor barrier performance by 38.8%, and oxygen barrier properties by 26.4%, compared to neat PLA. Their nucleating capability hastens isothermal crystallization of PLA composites by over 90%, enabling faster processing times. For the first time, in-situ polarized optical microscopy is used to visualize fracture toughening mechanisms in PLA under strain, revealing a direct link between mechanical property improvements and the role of SD LCNFs as craze nucleators in PLA. Additionally, the in-situ observation of crystallization kinetics highlights how SD LCNFs influences PLA microstructure, correlating these structural changes with enhanced barrier and mechanical properties. The composite’s optical clarity and UV shielding capabilities are assessed, confirming its potential for specialty packaging applications.
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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 | 0.000 | 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 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".