Antifouling Properties of Pluronic and Tetronic Surfactants in Digital Microfluidics
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Bibliographic record
Abstract
Fouling at liquid-solid interfaces is a pernicious problem for a wide range of applications, including those that are implemented by digital microfluidics (DMF). There are several strategies that have been used to combat surface fouling in DMF, the most common being inclusion of amphiphilic surfactant additives in the droplets to be manipulated. Initial studies relied on Pluronic additives, and more recently, Tetronic additives have been used, which has allowed manipulation of complex samples like serum and whole blood. Here, we report our evaluation of 19 different Pluronic and Tetronic additives, with attempts to determine (1) the difference in antifouling performance between the two families, (2) the structural similarities that predict exceptional antifouling performance, and (3) the mechanism of the antifouling behavior. Our analysis shows that both Pluronic and Tetronic additives with modest molar mass, poly(propylene oxide) (PPO) ≥50 units, poly(ethylene oxide) (PEO) mass percentage ≤50%, and hydrophilic-lipophilic balance (HLB) ca. 13-15 allow for exceptional antifouling performance in DMF. The most promising candidates, P104, P105, and T904, were able to support continuous movement of droplets of serum for more than 2 h, a result (for devices operating in air) previously thought to be out of reach for this technique. Additional results generated using device longevity assays, intrinsic fluorescence measurements, dynamic light scattering, asymmetric flow field flow fractionation, supercritical angle fluorescence microscopy, atomic force microscopy, and quartz crystal microbalance measurements suggest that the best-performing surfactants are more likely to operate by forming a protective layer at the liquid-solid interface than by complexation with proteins. We propose that these results and their implications are an important step forward for the growing community of users of this technique, which may provide guidance in selecting surfactants for manipulating biological matrices for a wide range of applications.
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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 it