Green Composites From Plasticized Cellulose Acetate Filled With Inorganic Fillers and Natural Fiber for Rigid Packaging Applications: Effect of Low Acetyl Content and Bio‐Based Plasticizer
Bibliographic record
Abstract
ABSTRACT This work aims to improve the processing window and melt strength of cellulose acetate (CA) with low acetyl content as a greener alternative to non‐biodegradable plastics for rigid‐packaging applications. It presents the first scientific evaluation of inorganic and organic fillers on plasticized CA (pCA) using two plasticizers, bio‐based triacetin (pCTA) and petro‐based triethyl citrate (pCTEC). Low‐acetyl CA is used to improve biodegradability over conventional high‐acetyl CA. Melt extrusion followed by injection molding is performed to make pCTA and pCTEC, and composites are formulated with organic/inorganic fillers including talc, clay, calcium carbonate (CaCO 3 ), and microcrystalline cellulose (MCC). Talc‐based composite reveals a well‐balanced mechanical performance and is thus combined with luperox (LUP), which further increases the matrix's tensile strength and Young's modulus. The pCTEC/10 wt.% talc shows ~130% rise in maximum extensional viscosity, while pCTA/0.01LUP/10 wt.% talc achieves a ~70% gain. The pCTEC/0.01LUP/15 wt.% talc improves the water vapor and oxygen barrier by 55.1% and 58.1%, respectively. Talc's platy morphology enhances interfacial adhesion, and its easier delamination in LUP's presence contributes to these barrier improvements. CA's thermal degradation shifts ~10°C higher after the addition of LUP and talc. These enhancements demonstrate the potential of pCA‐based composites for sustainable rigid packaging applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".