Combined microfluidics and drying processes for the continuous production of micro-/nanoparticles for drug delivery: a review
Bibliographic record
Abstract
Drug nanonization and encapsulation efficiency enhancement are prerequisites for hydrophobic and hydrophilic drugs to be delivered at the targeted site. Microfluidic technology has emerged as an efficient technique to achieve these objectives due to its ability to provide intensive mixing and yield relatively uniform nanosized particles. Furthermore, microfluidic technology has been established as a promising method to develop novel drug delivery systems with uniform particle size and distribution, reducing batch variation with controlled drug delivery capabilities. This extensive review introduces various applications of microfluidic systems for synthesizing controlled-sized organic and inorganic nanoparticles, followed by a discussion on micromixers and their recent advancements in drug delivery systems. We have reviewed the vital role of spray and freeze-drying in nanoparticle production. In addition, we have highlighted the concept and compared a microreactor-assisted spray and freeze dryer for developing a new innovative drug delivery platform. Finally, a critical discussion is presented on several recent patents on microfluidics along with applicable drying technologies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".