Combined microfluidics and drying processes for the continuous production of micro-/nanoparticles for drug delivery: a review
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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