Preparation of Monodisperse Biodegradable Polymer Microparticles Using a Microfluidic Flow‐Focusing Device for Controlled Drug Delivery
Bibliographic record
Abstract
Degradable microparticles have broad utility as vehicles for drug delivery and form the basis of several therapies approved by the US Food and Drug Administration. Conventional emulsion-based methods of manufacturing produce particles with a wide range of diameters (and thus kinetics of release) in each batch. This paper describes the fabrication of monodisperse, drug-loaded microparticles from biodegradable polymers using the microfluidic flow-focusing (FF) devices and the drug-delivery properties of those particles. Particles are engineered with defined sizes, ranging from 10 microm to 50 microm. These particles are nearly monodisperse (polydispersity index = 3.9%). A model amphiphilic drug (bupivacaine) is incorporated within the biodegradable matrix of the particles. Kinetic analysis shows that the release of the drug from these monodisperse particles is slower than that from conventional methods of the same average size but a broader distribution of sizes and, most importantly, exhibit a significantly lower initial burst than that observed with conventional particles. The difference in the initial kinetics of drug release is attributed to the uniform distribution of the drug inside the particles generated using the microfluidic methods. These results demonstrate the utility of microfluidic FF for the generation of homogenous systems of particles for the delivery of drugs.
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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) | 0.000 | 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 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".