Effect of various formulation parameters on the properties of polymeric nanoparticles prepared by multiple emulsion method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it