Exploring a direct injection method for microfluidic generation of polymer microgels
Bibliographic record
Abstract
Microfluidics (MFs) offers a promising method for the preparation of polymer microgels with exquisite control over their dimensions, shapes and morphologies. A challenging task in this process is the generation of droplets (precursors for microgels) from highly viscous polymer solutions. Spatial separation of MF emulsification and gelation of the precursor droplets on chip can address this challenge. In the present work, we explored the application of the "direct injection" method for the preparation of microgels by adding a highly concentrated polymer solution or a gelling agent directly into the precursor droplets. In the first system, primary droplets were generated from a dilute aqueous solution of agarose, followed by the injection of the concentrated agarose solution directly in the primary droplets. The secondary droplets served as precursors for microgels. In the second system, primary droplets were generated from the low-viscous solution of methyl-β-cyclodextrin and poly(ethylene glycol) end-terminated with octadecyl hydrophobic groups. Addition of surfactant directly into the primary droplets led to the binding of methyl-β-cyclodextrin to the surfactant, thereby releasing hydrophobized poly(ethylene glycol) to form polymer microgels. Our results show that, when optimized, the direct injection method can be used for microgel preparation from highly viscous liquids and thus this method expands the range of polymers used for MF generation of microgels.
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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 | 0.000 |
| 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 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".