Production of dry-state ketoprofen-encapsulated PMMA NPs by coupling micromixer-assisted nanoprecipitation and spray drying
Bibliographic record
Abstract
We present a two-step process to produce dry-state Ketoprofen-loaded poly(methyl methacrylate) nanoparticles (NPs) with controllable size and tunable drug release profile. A colloidal suspension of drug-loaded nanoparticles was first obtained from a nanoprecipitation process and then transferred into a commercial spray dryer. Three micromixers of different designs and mixing principles (molecular diffusion and impact mixing) were tested. After the first step, highly monomodal NPs in the size range 100 to 210 nm were obtained as seen by the low polydispersity index value (ca. PDI ∼ 0.2) returned by a dynamic light scattering detector. Physicochemical properties, encapsulation efficiency/ratio and drug release kinetics of NPs before and after drying were determined. For similar operating conditions, the best micromixer tested (impact mixing) allowed concluding that the NPs size was not significantly affected by the spray drying while encapsulation parameters and drug release rate were slightly decreased compared to the non spray-dried NPs. A sustained drug release was observed over 6 h and the drug release rate (up to 70%) was found to vary with the size of the NPs which in turn was a function of the flow rate ratio between the polymer solution and the non-solvent solution.
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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".