A Robust Superhydrophobic Surface for Digital Microfluidics
Bibliographic record
Abstract
Digital microfluidics depends on efficient movement of individual drops for a variety of tasks, e.g. reagent delivery, mixing, sampling, etc. Superhydrophobic (SH) coatings generally show high repellency and low adhesion for a variety of liquids. Therefore, SH coatings can provide for an efficient drop delivery and hence low energy requirements for a fluidic chip. However, wide application of such coatings is hampered by fragile nature of such coatings to date. A new SH coating is developed that addresses the fragility challenge of such coatings. It is based on application of nanoparticles to fluoropolymers. The mechanical stability, wear resistance and durability under prolonged liquid exposure of this new coating is discussed. It is shown that the new SH coating can maintain high contact angles, low contact angle hysteresis needed for drop mobility under adverse conditions/application of digital microfluidic devices. The developed SH coating can also be sprayed onto various surfaces, including glass used in traditional lab-on-chip (LOC) devices, or even paper for enabling novel Lap-on-paper (LOP) devices.
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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".