Characterization of Nanoscale Loaded Liposomes Produced by 2D Hydrodynamic Flow Focusing
Bibliographic record
Abstract
This paper presents the continuous flow formation by two-dimensional (2D) hydrodynamic flow focusing (HFF) of nanosized liposomes in microfluidic systems. The size distribution and concentration of the nanosized liposomes, as well as the polydispersity index (PDI) and zeta potential (ZP) of the liposomal dispersions, were investigated under various flow rate ratios (FRRs) and lipid formulations, by the selective incorporation of either positively charged DDAB (didodecyl-dimethylammonium bromide) or negatively charged DOPG (1,2 dioleoyl- sn -glycero-3- phosphoglycerol) lipids to the main bilayer DPPC (1,2-dipalmitoyl- sn -glycero-3-phosphocholine) constituent. The challenges of encapsulating an FITC (fluorescein isothiocyanate)-labeled LC-TAT peptide (long chain of transactivator of transcription peptide), which plays a direct role in the HIV regulation and transcription, overcame and could be achieved via one-step nanoliposomes synthesis, in order to validate the potential of this device as an all-in-one nanoparticle synthesis and loading platform. Liposomes with sizes ranging between 60 to 800 nm were produced with low polydispersity and high particle throughput from alteration of the flow rate ratio and lipid concentration. We introduced the use of nanoparticle tracking analysis (NTA) to estimate for the first time the throughput of microfluidic synthesized liposomal NPs by measuring quantitatively the concentration of the synthesized particles at the outlet. These measurements showed that stable and unilamellar liposomes are generated at a maximum concentration of 1740 × 10 8 particles/mL in less than 2 min, with higher FRR enabling the most rapid generation of liposomes with similar diameter and significant lower polydispersity index than those obtained by other batch techniques.
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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".