Multifunctional low temperature-cured PVA/PVP/citric acid-based hydrogel forming microarray patches: Physicochemical characteristics and hydrophilic drug interaction
Bibliographic record
Abstract
The characteristics of multifunctional polymeric hydrogel-forming microarray patches based on poly(vinyl alcohol)/poly(vinylpyrrolidone)/citric acid composite crosslinked at 80 °C were investigated. The swelling study showed that this composite possesses a higher swelling degree than the same polymer heated at 130 °C due to a lower crosslink density, which was then confirmed by FTIR examination. Solid-state studies revealed that lower-temperature crosslinking does not provide enough energy for the polymer to rearrange itself into a crystalline form. However, this composite polymer was shown to possess acceptable mechanical strength to insert/penetrate into the skin. The patch can function both as a means to sample model hydrophilic drugs from the skin and to deliver them when combined with a melt-type polyethylene glycol reservoir. The hydrophilic interaction between the hydrogel and drugs was investigated. A drug with a higher diffusion coefficient, modelled by theophylline (diffusion coefficient = 16.17 × 10-6 cm2/s), can be delivered more efficiently than fluorescent sodium (diffusion coefficient = 2.32 × 10-6 cm2/s) or cyanocobalamin (diffusion coefficient = 7.31 × 10-6 cm2/s). This is mainly due to theophylline’s high permeability (permeability coefficient = 7.40 × 10-5 cm/s) and weak ability to interact with the hydrogel (coefficient of partitioning = 1.3). These results indicated that the diffusion coefficient could be a useful predictive parameter to determine the delivery efficiency of the system. Furthermore, the results provide insight into how to select a suitable hydrogel for drug monitoring or delivery involving hydrophilic compounds based on the hydrophilic interaction between the polymer and the drug.
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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) | 0.001 | 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".