Using a Microfluidics System to Reproducibly Synthesize Protein Nanoparticles: Factors Contributing to Size, Homogeneity, and Stability
Bibliographic record
Abstract
The synthesis of Zein nanoparticles (NPs) using conventional methods, such as emulsion solvent diffusion and emulsion solvent evaporation, is often unreliable in replicating particle size and polydispersity between batch-to-batch syntheses. We have systematically examined the parameters for reproducibly synthesizing Zein NPs using a Y-junction microfluidics chip with staggered herringbone micromixers. Our results indicate that the total flow rate of the fluidics system, relative flow rate of the aqueous and organic phase, concentration of the base material and solvent, and properties of the solvent influence the polydispersity and size of the NPs. Trends such as increasing the total flow rate and relative flow rate lead to a decrease in Zein NP size, while increasing the ethanol and Zein concentration lead to an increase in Zein NP size. The solvent property that was found to impact the size of the Zein NPs formed the most was their hydropathy. Solvents that had a hydropathy index most similar to that of Zein formed the smallest Zein NPs. Synthesis consistency was confirmed within and between sample batches. Stabilizing agents, such as sodium caseinate, Tween 80, and Pluronic F-68, were incorporated using the microfluidics system, necessary for in vitro and in vivo use, into Zein-based NPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".