A Comprehensive Review of One Decade of Microfluidic Platforms Applications in Synthesis of Enhanced Carriers Utilized in Controlled Drug Delivery
Bibliographic record
Abstract
Abstract Novel nanocarriers such as multifunctional nanoparticles (NPs) have recently attracted attention due to their various applications, specifically in medicine and treatment. However, it is vital that these particles be synthesized with meticulous control of different structural, chemical, and physical properties. In response to this demand, microfluidic (MF) technology as a reliable procedure can provide promising results in the development of desired NPs and efficient drug delivery systems. By controlling the flow rates of multiphase fluids and conditions of chemical reactions, MF technology enables the fabrication of uniform and highly stable particles with enhanced surfaces, higher encapsulation efficiency, and controlled release of therapeutic agents compared with conventional bulk methods. This review article investigates the MF‐based methods utilized in the synthesis of NPs and their advantages in developing novel drug delivery systems. It also provides a comprehensive comparison with conventional methods from a different point of view, emphasizing a novel category of nanocarriers’ critical characteristics. In addition, a summary of the most recent representative works on NPs fabrication by MF procedures is presented, and their potential and applications in drug delivery are discussed.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".