Combination of ac electroosmosis and dielectrophoresis for particle manipulation on electrically-induced microscale wave structures
Bibliographic record
Abstract
Abstract This work presents a simple method to fabricate controllable microscale wave structures on the top of regular interdigitated electrode (IDE) arrays using electrically-assisted lithography techniques. Smooth wave structures are extremely difficult, if not impossible, to fabricate using traditional multilayer photolithography technology. The fabricated wave structures were carefully measured using an optical profiler and the measured wave profiles were used in the numerical simulation of electrical field and for evaluating the parameters influencing the fabricated wave structure. It is demonstrated that the combined smooth wave structure and IDE array offer unique capability for particle manipulation including particle concentration, aggregation and separation. Particle motion manipulated via the combined wave structure and IDE array is governed by ac electroosmosis (ACEO), dielectrophoresis (DEP) or a combination of both depending on the applied frequency. At lower frequencies (~30 kHz), ACEO dominates and particles are driven to move along the valleys of the wave structures; while at higher frequencies (~200 kHz), DEP force dominates which concentrates particles at the peaks of the wave structures. In addition, varying the ac waveform from sine-wave to square-wave allows for dynamic control of particle motion. Size-dependent particle separation over the wave structure is also demonstrated for a mixture of 0.5 µm and 2 µm particles that are separated into two populations by the joint effects of drag and DEP forces when being pumped to flow via ACEO.
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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) | 0.000 | 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".