Analytical solutions and validation of electric field and dielectrophoretic force in a bio‐microfluidic channel
Bibliographic record
Abstract
In a microbiological device, cell or particle manipulation and characterization require the use of electric field on different electrodes in several configurations and shapes. To efficiently design microelectrodes within a microfluidic channel for dielectrophoresis focusing, manipulation and characterization of cells, the designer will seek the exact distribution of the electric potential, electric field and hence dielectrophoresis force exerted on the cell within the microdevice. In this paper we describe the approach attaining the analytical solution of the dielectrophoretic force expression within a microchannel with parallel facing same size electrodes present on the two faces of channel substrates, with opposite voltages on the pair electrodes. Simple Fourier series mathematical expressions are derived for electric potential, electric field and dielectric force between two distant finite-size electrodes. Excellent agreement is found by comparing the analytical results calculated using MATLAB™ with numerical ones obtained by Comsol. This analytical result can help the designer to perform simple design parametric analysis. Bio-microdevices are also designed and fabricated to illustrate the theoretical solution results with the experimental data. Experiments with red blood cells show the dielectrophoretic force contour plots of the analytical data matched to the experimental results.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".