Sessile drop response to a single wave electrokinetic excitation
Bibliographic record
Abstract
The response time for maximum drop deformation and its comparison with different time scales is established and verified with experiments. The applied fluctuation is achieved by applying a single wave perturbation of electrowetting with desired amplitude and frequency. To pinpoint the importance of the initial actuation conditions, the variance in the maximum drop deformation for a single wave perturbation is studied. The focus of this study was to analyze the maximum deformation of a drop for a wide range of actuation mechanism with a varied drop or surrounding medium viscosities. The drop response to this cyclic actuation is compared with the equivalent mass–spring–dampener system, and limitations of this approach are identified. Interestingly, the qualitative results were similar between the air and liquid medium cases, but the attainment of equilibrium configuration was dissimilar. As anticipated, the higher actuation magnitude and frequency deformed the drop significantly and thus altered the drop configuration. Higher viscosity of drops and the surrounding medium delayed the time to achieve the maximum deformation. Accurately predicting the time required for a drop to attain the maximum deformation is paramount for optimizing processes and based on microfluidics technology.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".