Investigating the effect of phospholipids on droplet formation and surface property evolution in microfluidic devices for droplet interface bilayer (DIB) formation
Bibliographic record
Abstract
Despite growing interest in droplet microfluidic methods for droplet interface bilayer (DIB) formation, there is a dearth of information regarding how phospholipids impact device function. Limited characterization has been carried out for phospholipids, either computationally (in silico) or experimentally (in situ) in polydimethylsiloxane (PDMS) microfluidic devices, despite recent work providing a better understanding of how other surfactants behave in microfluidic systems. Hence, microfluidic device design for DIB applications relies heavily on trial and error, with many assumptions made about the impact of phospholipids on droplet formation and surface properties. Here, we examine the effects of phospholipids on interfacial tension, droplet formation, wetting, and hence device longevity, using DPhPC as the most widely used lipid for DIB formation. We use a customized COMSOL in silico model in comparison with in situ experimental data to establish that the stabilization of droplet formation seen when the lipid is dosed in the aqueous phase (lipid-in) or in the oil phase (lipid-out) is directly dependent on the effects of lipids on the device surface properties, rather than on how fast they coat the droplet. Furthermore, we establish a means to visually characterize surface property evolution in the presence of lipids and explore rates of device failure in the absence of lipid, lipid-out, and lipid-in. This first exploration of the effects of lipids on device function may serve to inform the design of microfluidic devices for DIB formation as well as to troubleshoot causes of device failure during microfluidic DIB experiments.
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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 | 0.002 | 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".