pH-Responsive Properties of Multilayered Poly(<scp>l</scp>-lysine)/Hyaluronic Acid Surfaces
Bibliographic record
Abstract
Multilayer films have been prepared by the sequential electrostatic adsorption of poly(L-lysine) and hyaluronic acid onto charged silicon surfaces from dilute aqueous solutions under various pH conditions. Microelectrophoresis was used to examine the local acid-base equilibria of the polyelectrolytes within the films as a function of the total number of layers in the film and the assembly solution pH. The acid-base dissociation constants were observed to deviate significantly from dilute solution values upon adsorption, to be layer dependent only within the first 3-4 layers, and to show sensitivity to the assembly solution pH. As a result, some of the physicochemical properties of the films have also been found to exhibit pH-responsive behavior. For example, the thickest films result when at least one of the polyelectrolytes is only partially dissociated in solution. As well, the pH-dependent degree of dissociation of the surface functional groups can be used to vary the contact angle of a water droplet by as much as 25 degrees and the coefficient of friction by up to an order of magnitude. In addition, the extent to which PLL/HA films can be made to swell in solution can be varied by a factor of 7 depending on the assembly solution and swelling solution pH. The anomalies found in the dissociation constants account for the trends in these pH-dependent properties. Here, we demonstrate that knowledge of the acid-base dissociation behavior in multilayer films is key to understanding and controlling the physical properties of the films, particularly surface friction and wettability, which are fundamentally important factors for many biomaterials applications. For example, we outline a mechanism whereby biopolymer thin films can be electrostatically adsorbed under highly charged "sticky" conditions and then quickly transformed into stable low-friction films simply by altering the pH environment.
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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".