Experimental and Computational Study on the Microfluidic Control of Micellar Nanocarrier Properties
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Microfluidic-based synthesis is a powerful technique to prepare well-defined homogenous nanoparticles (NPs). However, the mechanisms defining NP properties, especially size evolution in a microchannel, are not fully understood. Herein, microfluidic and bulk syntheses of riboflavin (RF)-targeted poly(lactic- co -glycolic acid)-poly(ethylene glycol) (PLGA-PEG-RF) micelles were evaluated experimentally and computationally. Using molecular dynamics (MD), a conventional “random” model for bulk self-assembly of PLGA-PEG-RF was simulated and a conceptual “interface” mechanism was proposed for the microfluidic self-assembly at an atomic scale. The simulation results were in agreement with the observed experimental outcomes. NPs produced by microfluidics were smaller than those prepared by the bulk method. The computational approach suggested that the size-determining factor in microfluidics is the boundary of solvents in the entrance region of the microchannel, explaining the size difference between the two experimental methods. Therefore, this computational approach can be a powerful tool to gain a deeper understanding and optimize NP synthesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".