Influence of buffer on colloidal stability, microstructure, and rheology of cellulose nanocrystals in hyaluronic acid suspensions
Bibliographic record
Abstract
Hyaluronic acid (HA) is a natural biopolymer found in various human tissues, while cellulose nanocrystals (CNCs) extracted from pulp fibers have unique rheological properties and biocompatibility. Due to the superior biomechanical properties of CNC and HA, a CNC-based HA suspension may be useful in biomedical applications. While buffers are an essential constituent of any suspension used for biomedical applications to maintain the desired pH level, they can significantly affect the properties of the suspension, including colloidal stability, microstructure, and rheological characteristics. To our knowledge, this is the first study analyzing the influence of buffer solutions on the suspension characteristics of HA/CNC systems, integrating both theoretical and experimental approaches. The results revealed an alignment between predictions of the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory and results from experiments characterizing a buffer-specific trend in colloidal stability. Suspensions with a higher energy barrier showed higher colloidal stability, with a lower tendency for phase separation and agglomerate formations. The microstructural analysis of CNC tactoids in the suspension revealed the existence of the hedgehog defect when dispersed in different buffer solutions. The defect is predicted to be caused by the pH-dependent protonation and deprotonation of HA. Furthermore, steady shear viscometry showed a microstructural-dependent shear viscosity trend, which, in turn, depends on the buffer solution. The study provides novel insights into the microstructural and bulk properties of HA and CNC suspensions in various buffer solutions. The results highlight the importance of solvent choice in tailoring the properties of the suspension for specific biomedical applications. These findings may be helpful in formulating HA and CNC suspensions for different biomedical applications, including drug delivery systems and viscosupplement injections.
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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 | 0.002 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| 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".