Porous Devices Derived from Co-Continuous Polymer Blends as a Route for Controlled Drug Release
Bibliographic record
Abstract
In this study we examine the release profile of bovine serum albumin (BSA) from a porous polymer matrix derived from a co-continuous polymer blend. The porosity is generated through the selective extraction of one of the continuous phases. This is the first study to examine the approach of using morphologically tailored co-continuous polymer blends as a template for generating porous polymer materials for use in controlled release. A method for the preparation of polymeric capsules is introduced, and the effect of matrix pore size and surface area on the BSA release profile is investigated. Furthermore, the effect of surface charge on release is examined by surface modification of the porous substrate using layer-by-layer deposition techniques. Synthetic, nonerodible polymer, high-density polyethylene (HDPE), was used as a model substrate prepared by melt blending with two different styrene-ethylene-butylene copolymers. Blends with HDPE allow for the preparation of porous substrates with small pore sizes (300 and 600 nm). A blend of polylactide (PLA) and polystyrene was also used to prepare porous PLA with a larger pore size (1.5 microm). The extents of interconnectivity, surface area, and pore dimension of the prepared porous substrates were examined via gravimetric solvent extraction, BET nitrogen adsorption, mercury porosimetry, and image analysis of scanning electron microscopy micrographs. With a loading protocol into the porous HDPE and PLA involving the alternate application of pressure and vacuum, it is shown that virtually the entire porous network was accessible to BSA loading, and loading efficiencies of between 80% and 96% were obtained depending on the pore size of the carrier and the applied pressure. The release profile of BSA from the microporous structure was monitored by UV spectrophotometry. The influence of pore size, surface area, surface charge, and number of deposited layers is demonstrated. It is shown that an effective closed-cell structure in porous PLA can be prepared, effectively eliminating all short-term BSA release.
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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".