Model‐based analysis of a dielectrophoretic microfluidic device for field‐flow fractionation
Bibliographic record
Abstract
We present the development of a dynamic model for predicting the trajectory of microparticles in microfluidic devices, employing dielectrophoresis, for Hyperlayer field-flow fractionation. The electrode configuration is such that multiple finite-sized electrodes are located on the top and bottom walls of the microchannel; the electrodes on the walls are aligned with each other. The electric potential inside the microchannel is described using the Laplace equation while the microparticles' trajectory is described using equations based on Newton's second law. All equations are solved using finite difference method. The equations of motion account for forces including inertia, buoyancy, drag, gravity, virtual mass, and dielectrophoresis. The model is used for parametric study; the geometric parameters analyzed include microparticle radius, microchannel depth, and electrode/spacing lengths while volumetric flow rate and actuation voltage are the two operating parameters considered in the study. The trajectory of microparticles is composed of transient and steady state phases; the trajectory is influenced by all parameters. Microparticle radius and volumetric flow rate, above the threshold, do not influence the steady state levitation height; microparticle levitation is not possible below the threshold of the volumetric flow rate. Microchannel depth, electrode/spacing lengths, and actuation voltage influence the steady-state levitation height.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".