Morphological, thermal, and mechanical properties of cellulose nanocrystal reinforced poly(lactic acid) and poly(butylene adipate‐co‐terephthalate): A comparative study on common and novel solvent casting methods
Bibliographic record
Abstract
Abstract The mechanical and thermal properties of semicrystalline (sc) and amorphous (a) poly(lactic acid), PLA, and poly(butylene adipate‐co‐terephthalate), PBAT, and their nanocomposites containing 1 and 3 wt% CNCs, prepared through solvent casting methods using one (N,N‐dimethylformamide [DMF]) or two (dimethyl sulfoxide (DMSO), and tetrahydrofuran (THF)) solvents were analyzed. Differential scanning calorimetry (DSC) showed that the total amount of crystals of the scPLA/CNC nanocomposites increased, whereas it decreased in the PBAT/CNC systems. In both cases, the crystallization temperature increased with CNC content. In tensile experiments, the Young modulus and yield strength of all nanocomposites were found to increase by incorporating CNCs, more significantly for the samples prepared using one solvent. The elongation at break of both PLA nanocomposites increased when prepared via one solvent, while it decreased for the two solvent methods as well as for PBAT nanocomposites prepared by both methods. The impact properties of the samples prepared by the two solvent methods decreased. In contrast, for the one solvent method, incorporating 3 wt% CNCs improved the impact properties by 32% and 9% in scPLA and aPLA, respectively, but decreased by 4% in PBAT nanocomposites. Also, in dynamic mechanical thermal analysis (DMA) the storage modulus of scPLA and PBAT/CNC systems increased significantly, especially in the rubbery region (5–85 MPa and 105–155 MPa, respectively). Using a percolation model, the strength of the percolating CNC was found to be dependent on temperature and affected by traces of solvent mostly in the scPLA nanocomposites.
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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".