Polylactic Acid Cellulose Nanocomposite Films Comprised of Wood and Tunicate CNCs Modified with Tannic Acid and Octadecylamine
Bibliographic record
Abstract
Herein, a one-pot strategy was used to prepare hydrophobic cellulose nanocrystals (CNCs) surface-modified with tannic acid and octadecylamine. By this strategy, CNCs derived from wood (W-CNC) and tunicates (T-CNC) were modified in situ and incorporated into a polylactic acid (PLA) matrix using two methods, without first drying the CNCs. Films of PLA-CNC nanocomposites were prepared both by solution casting and by wet compounding in a thermo-kinetic mixer, followed by melt extrusion. Various properties of these PLA nanocomposites were evaluated herein, along with an assessment of how these properties vary with the type of CNC reinforcement. Cast films with a hybrid mixture of wood and tunicate CNCs displayed improved mechanical properties compared to either wood or tunicate CNCs, but extruded films did not show this hybrid effect. The water vapor permeability of the extruded nanocomposite films with 1% CNCs was reduced by as much as 60% compared to the PLA films. The composite films also showed enhanced biodegradation compared to neat PLA films. These results demonstrate that wet compounded PLA composites produced with wood or tunicate CNCs modified using a one-pot, water-based route have improved barrier and biodegradation properties, indicating a potential for packaging applications without having to dry the CNCs.
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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 | 0.001 |
| 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".