Electrohydrodynamic Vortex Imaging: A New Tool for Understanding Mass Transfer in Surface‐Based Biosensors
Bibliographic record
Abstract
Surface-based biosensor performance is generally limited by mass transfer, especially when detecting low-concentrated species. To address this, dielectrophoresis (DEP) and alternating current electroosmosis (ACEO) can be combined to enhance mass transfer, increasing the target concentration near the sensor. This article presents a method for real-time direct imaging of electrohydrodynamic (EHD) effects on a microparticle suspension within a microfluidic chamber enclosed by two opposing electrodes. This top-bottom configuration was poorly studied in the literature for ACEO. The system presented thereby allows measurements of fluid flow profiles perpendicular to the electrode surface. The velocity of fluorescent latex microsphere tracers was measured as a function of signal frequency, potential, and electrolyte conductivity. This setup enables direct observation of vortices and particle-depleted areas, offering a valuable tool for selecting optimal input parameters-such as electric field, conductivity, and electrode dimensions-to efficiently concentrate microparticles near the sensor. Additionally, a numerical model developed in COMSOL and adapted for this top-bottom configuration enhances understanding of key parameters influencing EHD phenomena.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".