Effect of various formulation parameters on the properties of polymeric nanoparticles prepared by multiple emulsion method
Bibliographic record
Abstract
This work evaluates and interprets underlying mechanisms behind various aspects related to preparation and physical characteristics of polymeric nanoparticles (NP). These were prepared from different biodegradable polymers according to a water-in-oil-in-water emulsion solvent evaporation method. Polymers used were poly(lactic-co-glycolic) acid (PLGA), poly (lactic acid) (PLA), (PLA-PEG-PLA) triblock and (PLA-PEG-PLA)n multi-block co-polymers. A model DNA, as an example of a hydrophilic drug, was encapsulated in the internal aqueous phase. The primary emulsion was prepared using a high shear turbine mixer. The secondary emulsion was prepared by high-pressure homogenization. Surface morphology and internal structure were characterized by scanning electron microscopy (SEM) and atomic force microscopy (AFM). Influence of process variables on the physical properties of NP has been studied. Release of DNA was evaluated. In addition, changes occurring to NP porosity and surface area during degradation were followed. Nanoparticle size was ranging between 200-700 nm, according to the preparation conditions. Homogenizing pressure, concentration of the emulsifying agent used, polymer concentration and type and the concentration of a cryoprotectant had variable effects on NP size, surface area and porosity. Batches of NP where no emulsifying agent was added were obtained successfully. The release rate of the DNA from NP was mainly dependent on porosity, which varied significantly among used polymers. The preparation technique was efficient in encapsulating the model DNA and will be used for plasmid encapsulation in a future work.
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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 | 0.001 |
| 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 | 0.000 | 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".