Controllable Microfluidic Production of Drug-Loaded PLGA Nanoparticles Using Partially Water-Miscible Mixed Solvent Microdroplets as a Precursor
Bibliographic record
Abstract
We present a versatile continuous microfluidic flow-focusing method for the production of Doxorubicin (DOX) or Tamoxifen (TAM)-loaded poly(D,L-lactic-co-glycolic acid) (PLGA) nanoparticles (NPs). We use a partially water-miscible solvent mixture (dimethyl sulfoxide DMSO+ dichloromethane DCM) as precursor drug/polymer solution for NPs nucleation. We extrude this partially water-miscible solution into an aqueous medium and synthesized uniform PLGA NPs with higher drug loading ability and longer sustained-release ability than conventional microfluidic or batch preparation methods. The size of NPs could be precisely tuned by changing the flow rate ratios, polymer concentration, and volume ratio of DCM to DMSO (VDCM/VDMSO) in the precursor emulsion. We investigated the mechanism of the formation of NPs and the effect of VDCM/VDMSO on drug release kinetics. Our work suggests that this original, rapid, facile, efficient and low-cost method is a promising technology for high throughput NP fabrication. For the two tested drugs, one hydrophilic (Doxorubicin) the other one hydrophobic (Tamoxifen), encapsulation efficiency (EE) as high as 88% and mass loading content (LC) higher than 25% were achieved. This new process could be extended as an efficient and large scale NP production method to benefit to fields like controlled drug release and nanomedicine.
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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".