Hydrodynamic focusing to synthesize lipid-based nanoparticles: Computational and experimental analysis of chip design and formulation parameters
Bibliographic record
Abstract
Microfluidic hydrodynamic focusing (HF) has emerged as a powerful platform for the controlled synthesis of lipid nanoparticles (LNPs) and liposomes, offering superior precision, reproducibility, and scalability compared to traditional batch methods. However, the impact of HF inlet configuration and channel geometry on nanoparticle formation remains poorly understood. In this study, we present a comprehensive experimental and computational analysis comparing 2-inlet (2-way) and 4-inlet (4-way) HF designs across various sheath inlet angles (45°, 90°, 135°) and cross-sectional geometries (square vs. circular), assessing their influence on particle size, polydispersity index (PDI), and percentage encapsulation efficiency (%EE) of siRNA and FITC-Dextran. Using 3D-printed microfluidic chips, empty and loaded liposomes and LNPs were synthesized across a range of lipid concentrations (1-8 mg/mL) and total flow rates (0.12-16 mL/min). Computational fluid dynamics (CFD) simulations revealed significant differences in mixing profiles and ethanol diffusion across configurations, correlating with observed nanoparticle properties. Interestingly, 2-way focusing outperformed 4-way designs at low flow rates due to broader diffusive interfaces, while 4-way 45° configurations provided superior control over nanoparticle formation at high flow rates. Circular channels produced smaller, more uniform nanoparticles than square channels, likely due to symmetric flow patterns and reduced stagnation zones. Higher lipid concentrations decreased PDI and improved encapsulation, particularly for siRNA-loaded LNPs. Encapsulation efficiencies were similar across most configurations; however, a statistically significant increase was observed in the 4-way 135° design at 4 mL/min. This likely reflects size-related effects rather than a specific advantage of the configuration. Furthermore, LNPs produced at higher flow rates exhibited enhanced cellular uptake, attributed to their smaller particle size. Overall, our results demonstrate that optimal nanoparticle synthesis via HF is governed by an interplay of flow rate, inlet geometry, and formulation parameters (nanoparticle type and lipid concentration) rather than the number of inlets alone. This study provides a design framework for selecting HF configurations tailored to specific throughput and encapsulation requirements in therapeutic nanocarrier production.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".