Effect of Surface Functionality on the Rheological and Self-Assembly Properties of Chitin and Chitosan Nanocrystals and Use in Biopolymer Films
Bibliographic record
Abstract
Chitin nanocrystals (ChNCs) are unique to all other bio-derived nanomaterials in one aspect: the inherent presence of a nitrogen moiety. By tuning the chemical functionality of this nanomaterial, and thus its charge and hydrogen bonding capacity, one can heavily impact its macroscopic properties such as its rheological and self-assembly characteristics. In this study, two types of ChNCs are made using acid hydrolysis (AH-ChNCs) and oxidative (OX-ChNCs) pathways, unto which deacetylation using a solvent-free procedure is utilized to create chitosan nanocrystals (ChsNCs) of varying degree of deacetylation (DDA). These nanocrystals were then studied for their rheological behavior and liquid crystalline ordering. It was found that with both deacetylation and carboxylation of ChNCs, viscosity continually increased with increasing concentrations from 2 to 8 wt %, contrary to AH-ChNC dispersions in the same range. Interestingly, increasing the amine content of ChNCs was not proportional to the storage modulus, where a peak saturation of amines provided the most stiffness. Conversely, while the introduction of carboxylation increased the elastic modulus of OX-ChNCs by an order of magnitude from that of AH-ChNCs, it was decreased by increasing DDA. Deacetylation and carboxylation both inhibited the formation of a chiral nematic phase. Finally, these series of nanocrystals were incorporated into biodegradable pectin-alginate films as a physical reinforcement, which showed increased tensile strength and Young's modulus values for the films incorporated with ChsNCs. Overall, this study is the first to investigate how surface functionalization of chitin-derived nanocrystals can affect their rheological and liquid crystalline properties and how it augments pectin/alginate films as a physical reinforcement nanofiller.
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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) | 0.000 | 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".