Flow of microgel capsules through topographically patterned microchannels
Bibliographic record
Abstract
We investigated the flow dynamics of microgel capsules in topographically patterned microfluidic devices. For microgels flowing through channel constrictions, or orifices, we observed three phenomena: (i) the effect of confinement, (ii) the role of interactions between the microgels and the channel surface, and (iii) the effect of the velocities of microgels prior to their passage through an orifice. We studied negatively charged alginate microgels and positively charged alginate microgels coated with N-(2-hydroxy)propyl-3-trimethylammonium chitosan chloride (HTCC). Aqueous dispersions of microgels were driven through poly(dimethyl siloxane) microchannels carrying a weak negative surface charge. The velocity of the continuous phase, and hence, the velocity of the microgels increased as they passed through topographically patterned orifices. Alginate microgels were observed to have a larger increase in velocity relative to HTCC-coated alginate microgels. This effect, which was attributed to electrostatic attraction or repulsion, was found to be strongest for orifices with dimensions close to the microgel diameter. For example, when 75 microm-diameter microgels flowed through a 76 microm orifice, alginate gels (negatively charged) experienced a 2x greater increase in velocity than HTCC-coated (positively charged) microgels. This effect was exaggerated at lower initial flow rates. For example, when 75 microm-diameter microgels flowed through an 80 microm orifice, a two-fold difference in the velocity changes of the two microgel types was observed when the initial flow rate was 275 microm s(-1), while a three-fold difference in velocity changes was observed when the initial flow rate was 130 microm s(-1). We speculate that these studies will be useful for modeling the flow of suspensions of cells or other biologically relevant particles for a wide range of applications.
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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".