Probing molecular interactions of PEGylated chitosan in aqueous solutions using a surface force apparatus
Bibliographic record
Abstract
PEGylation can modify the physicochemical properties of native chitosan and improves its water solubility. PEGylated chitosan has been widely used as a gene/drug delivery vector by forming a polyelectrolyte complex (PEC) in biomedical engineering. The molecular interactions of PEGylated chitosan play a critical role in forming the core-shell structure of the complexes. In this work, we systematically investigated the cohesive interaction between PEGylated chitosan films using a surface forces apparatus (SFA) under different solution conditions, and the corresponding morphology change was characterized using atomic force microscopy (AFM). The force measurements demonstrated that the cohesion could be enhanced by increasing the contact time and the PEGylation degree, but could be weakened by increasing the solution pH, which is closely related to the morphology change of the PEGylated chitosan films. The strong cohesion of PEGylated chitosan, as compared to that of native chitosan, is primarily attributed to improved polymer solubility and flexibility, and enhanced formation of hydrogen bonds between the polymer chains. In addition, continuously increasing the PEGylation degree was found to be less effective in further strengthening the cohesion at relatively high pH (e.g., pH ∼ 8.5), which is most likely due to the repulsion originating from the formation of dense hydration PEG shells. Our results provide useful nanomechanical insights into the fundamental understanding of the interaction mechanism of PEGylated chitosan, with implications for the development of novel and effective gene/drug carriers in bioengineering.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".