Probing the Stability of Biocompatible Sodium Hyaluronate/Chitosan Nanocoatings Against Changes in Salinity and pH
Bibliographic record
Abstract
The stability of polyelectrolyte multilayer assemblies was investigated with emphasis on the effects of solution ionic strength, pH, and polymer molecular weight on the film thickness and surface topography. The multilayers consisting of two polysaccharides, the polyanion sodium hyaluronate (HA) and the polycation chitosan (CH) were studied using surface plasmon resonance (SPR) spectroscopy, impedance quartz crystal microbalance (QCM), and atomic force microscopy (AFM). SPR/QCM experiments show that coatings consisting of four HA/CH bilayers assembled at pH 4.5 in the presence of 0.15 M NaCl are stable in NaCl solutions of concentration less than 0.8 M. These multilayers are stable when placed in contact with aqueous solutions ranging in pH from 3.5 to 9. The molecular weight of the polysaccharides has only a marginal effect on the stability of the films in the range explored here (HA: Mn = 360,000 or 31,000 g/mol; CH: Mn = 160,000 or 30,000 g/mol). AFM imaging reveals that different mechanisms may account for the multilayers stability versus salt and pH treatments. While increasing the ionic strength induces reorganization of the surface topography from isolated spherical islets to elongated worm-like features, changes in pH have no appreciable effects on the coating topography prior to complete disintegration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".