Flow-Directed Loading of Block Copolymer Micelles with Hydrophobic Probes in a Gas–Liquid Microreactor
Bibliographic record
Abstract
We investigate the loading efficiencies of two chemically distinct hydrophobic fluorescent probes, pyrene and naphthalene, for self-assembly and loading of polystyrene-block-poly(acrylic acid) (PS-b-PAA) micelles in gas-liquid segmented microfluidic reactors under different chemical and flow conditions. On-chip loading efficiencies are compared to values obtained via off-chip dropwise water addition to a solution of copolymer and probe. On-chip, probe loading efficiencies depend strongly on the chemical probe, initial solvent, water content, and flow rate. For pyrene and naphthalene probes, maximum on-chip loading efficiencies of 73 ± 6% and 11 ± 3%, respectively, are obtained, in both cases using the more polar solvent (DMF), an intermediate water content (2 wt % above critical), and a low flow rate (∼5 μL/min); these values are compared to 81 ± 6% and 48 ± 2%, respectively, for off-chip loading. On-chip loading shows a significant improvement over the off-chip process where shear-induced formation of smaller micelles enables increased encapsulation of probe. As well, we show that on-chip loading allows off-chip release kinetics to be controlled via flow rate: compared to vehicles produced at ∼5 μL/min, pyrene release kinetics from vehicles produced at ∼50 μL/min showed a longer initial period of burst release, followed by slow release over a longer total period. These results demonstrate the necessity to match probes, solvents, and running conditions to achieve effective loading, which is essential information for further developing these on-chip platforms for manufacturing drug delivery formulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".