Cellulose nanocrystals (CNCs)/hyaluronic acid (HA) suspensions: Influence of HA concentration and TEMPO modification of CNC on colloidal properties
Bibliographic record
Abstract
Hyaluronic acid (HA) and cellulose nanocrystals (CNCs) are widely used in biomedical applications due to their biocompatibility, tunable rheology, and other colloidal properties. This study presents a systematic analysis of the colloidal stability, microstructure, and rheology of HA/CNC and HA/TEMPO-oxidized CNC (TCNC) suspensions in phosphate-buffered saline (PBS). At a fixed CNC concentration (2 wt%), varying HA concentration showed a non-monotonic trend in interfacial and rheological properties. This behavior aligns with transitions predicted by the polymer reference interaction site model (PRISM), involving depletion attraction, steric stabilization, and polymer bridging. The optimal HA concentration of 1 mg/mL maximized steric stabilization, leading to enhanced stability and larger tactoid sizes. Steady-shear rheometry revealed an anomalous viscosity reduction, notably a drop in the low-shear apparent viscosity as the HA concentration was increased to 1 mg/mL. This might be due to tactoid slippage enabled by the HA-induced steric layer. The HA/TCNC suspensions demonstrated enhanced colloidal stability compared to pristine CNC in HA, attributed to the increased surface charge resulting from TEMPO oxidation. This finding was further supported by Derjaguin-Landau-Verwey-Overbeek (DLVO) theory plots, showing that TCNC has a higher energy barrier for interaction. Unlike HA/CNC suspensions, HA/TCNC suspensions exhibited no dual-yielding behavior, likely due to improved dispersibility. An increase in viscoelastic moduli and yield stress with TCNC concentration reflects a uniform evolution of the microstructure. These findings highlight the effects of HA concentration and CNC surface modification on suspension behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".