On the mixing characteristics of a poorly water soluble drug through microfluidic‐assisted nanoprecipitation: Experimental and numerical study
Bibliographic record
Abstract
Abstract Nanoprecipitation of curcumin from its ethanolic solutions was carried out at a microfluidic scale by the liquid anti‐solvent technique, in the presence of SDS as a stabilizer. Attention was mainly paid to the mixing angle of the water and ethanol in three micro‐fabricated channels. The nanosuspension quality was measured by particle size distribution and polydispersity index. BET surface area, dissolution test, FTIR spectra, and XRD patterns of the optimized nanosuspension were also evaluated experimentally. In particular, narrow size distribution of curcumin particles was achieved under well‐controlled conditions of large confluence angles. The amorphous ultrafine curcumin powder exhibited enhanced dissolution properties when compared to the raw material. To explain the precipitation results, a species 3D model was created by computational fluid dynamics (CFD). The pressure drop was compared to the quantitative experimental data to validate the CFD computations. Altogether, the similarities in observations and the mass fraction and velocity predictions of the 3D model revealed that the injection angle of microfluidic devices is a key parameter for the resultant curcumin nanoparticle size.
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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".