Effect of carboxylated cellulose nanocrystal acetylation on PLA nanocomposite crystallization behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• cCNCs and acetylated cCNCs were dispersed in PLA using ethyl lactate (EtLa). • Green solvent (EtLa) improved PLA chain mobility and crystallization efficiency. • Avrami analysis revealed a shift to 3D spherulitic growth in PLA with cCNCs. • 1.5 wt.% cCNCs significantly increased PLA crystallization rate at 100°C. • Acetylated cCNCs enhanced PLA crystallization at temperatures of 110 and 120°C. Polylactic acid (PLA) has garnered increasing attention as a biodegradable polymer derived from renewable resources; however, its relatively slow crystallization rate restricts its broader use in wider applications. We address this challenge by producing PLA nanocomposites with carboxylated cellulose nanocrystals (cCNCs) and acetylated cCNCs (AcCNCs) in ethyl lactate (EtLa), a bio-based, non-toxic solvent. The crystallization behavior and thermomechanical properties of the nanocomposites were measured using X-ray diffraction (XRD), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), dynamic mechanical analysis (DMA), and polarized light microscopy (PLM). For PLA-cCNC nanocomposites, Avrami analysis confirmed a transition from two- to three-dimensional spherulitic growth. The addition of cCNCs or AcCNCs with a low degree of substitution (i.e., DS = 0.06) in PLA led to increased crystallization rates. This demonstrated that the cCNCs and AcCNCs enhanced heterogeneous nucleation and the use of EtLa enhanced PLA chain mobility. XRD measurements revealed an increase in average crystallite size when cCNCs and AcCNCs were added to the PLA, signifying improved crystal development. Although both cCNCs and AcCNCs promoted PLA crystallization, the nucleating efficiency of AcCNCs was hampered by reduced compatibility with the EtLa solvent, likely leading to some AcCNC aggregation. The results show how leveraging a greener solvent (EtLa) and utilizing cCNCs can effectively address PLA crystallization limitations, thereby expanding opportunities to enhance high-performance, sustainable materials in packaging, additive manufacturing, and biomedical engineering.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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 it