The effects of aspect ratio of cellulose nanocrystals on the properties of all CNC films: Tunicate and wood CNCs
Bibliographic record
Abstract
In this study, the physicochemical, mechanical, and thermal properties of films made from cellulose nanocrystals (CNCs) obtained from two sources – wood and tunicate - and their hybrids were investigated. It was found that different morphologies affect the properties of CNC films. Specifically, the surface morphology of tunicate CNC (TCNC) films differed significantly from wood CNC (WCNC) films, and the structure of hybrid CNC (HCNC) films was also different. Compared to wood CNC-containing films, tunicate CNC films exhibited significantly higher BET surface areas, superior thermal properties, greater surface roughness, and very low (≈1 wt.%) dispersibility in water due to their ability to form long-range connectivity. The average BET surface area of TCNC films was measured at 91.9 ± 2.1 m2/g, and this value decreased with the addition of WCNCs to the film structure. The pore diameter also decreased from 11.362 nm for TCNC films to 0.437 nm for WCNC films, indicating the filler effect of WCNCs on the TCNC films. The chiral nematic phase exhibited by WCNC films was confirmed by their iridescent appearance, as observed through cross-sectional SEM images. The TS for TCNC and HCNC3 was measured at 185.2 ± 10.5 MPa and 182.9 ± 13.7 MPa, respectively, which were the highest values among the samples. The lowest TS value of 113.1 ± 9.5 MPa was observed in the WCNC films. A similar decline was observed in the EB values, from 1.76 ± 0.37% for TCNC to 0.9 ± 0.22% for WCNC. This study provides information and knowledge useful for understanding the impact of biomass source on the properties of CNC films for a variety of purposes, including packaging materials, biodegradable coatings, flexible electronics, and membrane 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 | 0.000 | 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 | 0.002 |
| 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".