Microdroplet evaporation in closed digital microfluidic biochips
Bibliographic record
Abstract
In this paper, microdroplet evaporation in the closed digital microfluidic systems is studied for hydrophobic and hydrophilic surfaces. The contact angle and contact radius are measured by an enhanced automated polynomial fitting approach. It is observed that the contact angle for both hydrophobic and hydrophilic surfaces remains constant during the evaporation process. However, a higher evaporation rate is observed for hydrophilic droplets compared to the hydrophobic droplets. Since no contact line pinning is observed, first, an analytical model based on the uniform vapor mass flux along the liquid–vapor interface is proposed. Interestingly, it is observed that in the hydrophobic case, the analytical model gives a higher evaporation rate, whereas for the hydrophilic case, the analytical model gives a lower evaporation rate. The discrepancy between the results of the analytical modeling and the experimental values is hypothesized to be due the constant flux assumption. To verify the hypothesis, a finite volume-based numerical model is developed to find the local flux along the liquid–vapor interface. The numerical modeling results confirm that for hydrophilic droplets, the evaporation flux increases very close to the three-phase contact line. In the case of the hydrophobic droplets, on the other hand, the flux decreases close to the contact line due to vapor saturation; as a result the uniform flux assumption overestimates the mass loss.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".