A Novel Composite Membrane for pH Responsive Permeation
Bibliographic record
Abstract
Objective(s) In this study, a kind of pH sensitive composite membrane was prepared and drug permeation through it was investigated in terms of pH. Rationale of this study originated from the fact that a pH change which may be a result of a disease state in the body can trigger drug release. Materials and Methods Here, a kind of pH sensitive composite membrane containing different nanoparticle [1:1 n-isopropyl acrylamide (Nipam): metacrylic acid (Maa)] contents in ethylcellulose was prepared by a casting method. Swelling ratios of these nanoparticles and composite membranes with different particle loadings were determined. Permeation of two different drug models with different hydrophilicity and molecular weights, vitamin B12 (vit B12) and paracetamol, through these membranes was studied in terms of pH. Results It was seen that swelling ratios of nanoparticles and the composite membranes went up as the particle content increased at each pH. Vit B12 and paracetamol permeation through the membranes in pH value below the pKa was much higher than that at pHs above it, but this difference was much more pronounced for vit B12 compared to paracetamol. Conclusion Permeation through these membranes showed a sharp sensitivity to pH changes. Nanoparticles in the composite membranes could act as nanovalves due to their sharp swelling/shrinkage around the pKa of Maa. These membranes could be considered as an ideal stimuli-sensitive barrier for modulating drug release with a small change in pH.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".