Production of menthol‐loaded nanoparticles by solvent displacement
Bibliographic record
Abstract
Abstract The production of menthol‐loaded poly‐ϵ‐caprolactone nanoparticles (NPs) for dermal application was investigated. The nanoparticles were produced in three different mixers: a confined impinging jet mixer (CIJM), a two‐inlet vortex mixer (VM), and a four‐inlet vortex mixer (MIVM), testing their performances in the same operating conditions. The effects of various process parameters such as polymer and menthol concentration, flow rate, solvent type (acetone, acetonitrile, or THF), and quench ratio, on mean nanoparticle size, menthol loading, and encapsulation efficiency were compared and discussed. The amount of menthol encapsulated inside the nanoparticles was quantified by GC analysis and the structure and shape of the NPs were analyzed by TEM. Nanoparticles of sizes between 200 nm to 800 nm were obtained using the CIJM, the VM, and the MIVM with different feeding sequences. It was observed that mixer geometry had a strong effect on particle size (at the same operating conditions the size decreased from MIVM with two inlets to VM and to CIJM) and the smallest particles were obtained using the MIVM using one solvent and three antisolvent streams. By using acetonitrile, the mean nanoparticle size was larger. Incorporation efficiency and menthol loading values up to 80 % and 60 % respectively were obtained depending on the inlet menthol and polymer concentrations.
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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".