A dielectrophoretic-gravity driven particle focusing technique for digital microfluidic systems
Bibliographic record
Abstract
In the present study, a particle focusing technique functioning based on the cumulative effects of gravity and negative dielectrophoresis (nDEP) is developed for digital microfluidic (DMF) systems. This technique works using the conventional electrodes used for droplet manipulation without a need for geometrical modification. Particle manipulation is performed by applying an AC voltage to the electrode above which there is the droplet containing the non-buoyant particles. The particles sediment due to the difference between the gravitational and the vertical component of the nDEP forces, while the horizontal component of the nDEP force concentrates them on the center of the electrode. Therefore, the magnitude of the voltage must be kept within an effective range to have simultaneous effects of sedimentation (dominated by gravity) and concentration (due to the horizontal component of the nDEP force). The physics of the phenomenon is explained using simulation. The effects of the magnitude of the applied voltage, the particle size and density, and the electrode size on the focusing behavior of the particles are studied. Finally, a potential application of the present technique is illustrated for particle concentration in DMF.
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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 | 0.000 |
| 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) | 0.001 | 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".