Controlling Structure and Function of Polymeric Drug Delivery Nanoparticles Using Microfluidics
Bibliographic record
Abstract
We demonstrate control of multiscale structure and drug delivery function for paclitaxel (PAX)-loaded polycaprolactone-block-poly(ethylene oxide) (PCL-b-PEO) polymeric nanoparticles (PNPs) via synthesis and flow-directed shear processing in a two-phase gas-liquid microfluidic reactor. This strategy takes a page from the engineering of commodity plastics, where processing rather than polymer chemistry provides an experimental handle on properties and function. PNPs formed from copolymers with three different PCL block lengths show sizes, morphologies, and loading efficiencies that depend on both the PCL block length and the microfluidic flow rate. By varying flow rate and comparing with a conventional bulk method of PNP preparation, we show that flow-variable shear processing provides control of PNP sizes and morphologies and enables slower PAX release times (up to 2 weeks) compared to bulk-prepared PNPs. Antiproliferative effects against cultured MCF-7 breast cancer cells were greatest for PNPs formed at an intermediate flow rate, corresponding to small and low-polydispersity spheres formed uniquely at this flow condition. Formation and flow-directed nanoscale shear processing in gas-liquid microfluidic reactors provides a manufacturing platform for drug delivery PNPs that could enable more effective and selective nanomedicines through multiscale structural control.
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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".