Effect of Tannin and Nanocellulose on Properties of Sustainable Cellulose Acetate Films
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
The development of sustainable materials has encouraged the use of biopolymers as alternatives to synthetic polymers. Polymeric films have stood out for their high potential in environmentally sustainable applications. Conventional cellulose acetate (CA)-based films are attractive due to their biodegradability and film-forming ability. However, their functional performance often requires enhancement through the incorporation of additives. In this context, two bio-based additives were investigated: condensed tannin (0%, 5% and 10%wt.), a natural polyphenol known for its antioxidant and antimicrobial properties, and nanocrystalline cellulose (CNC) (0%, 0.5% and 1%wt.), which act as reinforcing agents to improve mechanical strength and barrier properties. The results showed that tannin generally enhanced mechanical strength and surface uniformity while imparting contact-based antimicrobial activity. CNC reduced water uptake and improved thermal stability, but when used alone, it tended to lower mechanical performance and increase surface roughness. The combination of CNC and tannin produced performance shifts that depended strongly on their relative concentrations, with no consistent synergistic effect across all properties. In certain balanced ratios, CNC benefited from tannin’s matrix-stabilizing effect, leading to improved strength or reduced moisture absorption. Antimicrobial activity in acetic acid–based films was linked to residual acidity, whereas in acetone-based films, tannin alone was responsible for the antimicrobial effect by contact. These findings highlight that the physicochemical, mechanical, and functional performance of CA films (CAFs) is governed not only by additive type but also by the precise interplay between CNC and tannin, underscoring the need for formulation strategies tailored to the requirements of specific applications.
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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 | 0.003 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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