Dielectrophoretic Mixing With Novel Electrode Geometry
Bibliographic record
Abstract
Electrokinetic mixing of analytes at micro-scale is important in several biochemical applications like cell activation, DNA hybridization, protein folding, immunoassays and enzyme reactions. This paper deals with the modeling and numerical simulation of micromixing of two different types of colloidal suspensions based on principle of dielectrophoresis (DEP). A mathematical model is developed based on Laplace, Navier-Stokes, and convection-diffusion-migration equations to calculate electric field, velocity, and concentration distributions, respectively. Mixing of two colloidal suspensions is simulated in a three-dimensional computational domain using finite element analysis considering dielectrophoretic, gravitational and convective (advective)–diffusive forces. Phase shifted AC signal is applied to the alternating electrodes for achieving the mixing of two different colloidal suspensions. The results indicate that the electric field and DEP forces are maximum at the edges of the electrodes and become minimum elsewhere. As compared to curved edges, straight edges of electrodes have lower electric field and DEP forces. The results also indicate that DEP force decays exponentially along the height of the channel. The effect of DEP forces on the concentration profile is studied. It is observed that, the concentration of colloidal particles at the electrodes edges is very less compared to elsewhere. Mixing of two colloidal suspensions due to diffusion is observed at the interface of the two suspensions. The improvement in mixing after applying the repulsive DEP forces on the colloidal suspension is observed. Most of the mixing takes place across the slant edges of the triangular electrodes. The effect of electrode pairs and the mixing length on degree of mixing efficiency are also observed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".