Various-Sized Nanocelluloses Induced Stereocomplexes Crystallization Formation and Its Mechanism of Stereoisomers Poly(lactide acid) Blend
Bibliographic record
Abstract
This work provides evidence to explore the relationship between hydrogen bonding interactions and stereocomplex crystallization (SC) formation of nanocomposite-based nanocelluloses (NCs). Here, we evaluated the synergistic effect of various aspect ratios of NCs as nucleating agents on the properties of a poly( l -lactic acid)/poly( d -lactic acid) ( l / d ) stereocomplexable matrix using the casting method. Furthermore, NCs enhanced the interfacial adhesion interaction of the l / d matrix, improving crystallization ability, heat resistance, and barrier properties against the water vapor of nanocomposites. As expected, the nanocomposite with cellulose nanofibers (CNF 3 ) had the highest SC crystallinity of 33.9% and a maximum SC melting temperature of 220.6 °C. The tensile strength of the l / d /CNF 3 nanocomposite increased to 53.0 MPa due to the highest aspect ratio and rigidity of CNF, which improved interfacial hydrogen bonding interactions between CNF 3 and the l / d matrix. Compared with different samples, nanocomposite with cellulose nanospheres (CNS 3 ) had the smallest spherulite size. While nanocomposite with cellulose nanocrystals (CNC 3 ) exhibited more efficient heterogeneous nucleation than that of CNS 3 or CNF 3 . We demonstrated the relationship between the hydrogen-bonding interactions of l / d nanocomposites and crystallization kinetic/mechanism. This work offers a suitable strategy for selecting appropriate types of NCs as nucleating agents for designing high-performance materials for food packaging industries to solve the plastic melt processing problem.
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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 | 0.001 |
| 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".