Sustainable PLA Bio-Nanocomposites: Integration of TPU Nanofibrils and CNC for Enhanced Crystallization, Toughness, Stiffness, Transparency, and Oxygen Barrier Properties
Bibliographic record
Abstract
This study explores the interplay between elastomeric nanofibrillar thermoplastic polyurethane (TPU) and cellulose nanocrystals (CNCs) to expand the applications of poly(lactic acid) (PLA) composites via optimization of the nanofibril TPU/CNC ratio. Advanced analytical methods reveal the contrasting effects of CNC stiffening and TPU toughening capabilities. Fourier transform infrared spectroscopy (FTIR) and nuclear magnetic resonance (NMR) confirm isophorone diisocyanate (IPDI) as a cross-linker and chain extender, altering the polymer network, while X-ray photoelectron spectroscopy (XPS) suggests hydrogen bonding between CNC and TPU. Scanning electron microscopy (SEM) shows that CNC reduces TPU spherical domain sizes from 270–350 to 200–270 nm and alters the nanofibril TPU diameter from 90–280 to 100–320 nm. CNC accelerates PLA crystallization, reducing the crystallization half-time from 21 to 0.87 min, and optimizes crystallinity at 100 °C. Higher annealing temperatures reduce oxygen transmission rates from 66 to 16 cc/(m 2 ·day) with 1 wt % CNC at 130 °C due to denser α-crystal formation. Transparency studies show minimal impact on PLA clarity up to 0.6 wt % CNC, with fibrillar TPU maintaining superior transparency. Mechanical tests reveal significant increases in tensile toughness, from 1.9 MPa in neat PLA to 30.9 and 38.2 MPa with 3 and 6 wt % TPU, respectively. CNC further enhances these properties at lower TPU concentrations, improving tensile strain up to 3900 times that of neat PLA while maintaining tensile strength and Young’s modulus. Morphological analysis reveals detailed toughening mechanisms, where integrating fibril TPU with CNC refines void structures and enhances fibril formations, leading to ductile cup-and-cone fracture behaviors. This configuration significantly improves ductility, promoting plastic deformation and forming microvoids and crazes. These findings highlight the potential of optimized CNC and TPU ratios to broaden the functional scope of PLA composites, suggesting promising strategies for advanced material design toward an eco-friendlier industry.
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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 | 0.001 |
| 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".