Electronic detection of dielectrophoretic forces exerted on particles flowing over interdigitated electrodes
Bibliographic record
Abstract
Dielectric particles flowing through a microfluidic channel over a set of coplanar electrodes can be simultaneously capacitively detected and dielectrophoretically (DEP) actuated when the high (1.45 GHz) and low (100 kHz-20 MHz) frequency electromagnetic fields are concurrently applied through the same set of electrodes. Assuming a simple model in which the only forces acting upon the particles are apparent gravity, hydrodynamic lift, DEP force, and fluid drag, actuated particle trajectories can be obtained as numerical solutions of the equations of motion. Numerically calculated changes of particle elevations resulting from the actuation simulated in this way agree with the corresponding elevation changes estimated from the electronic signatures generated by the experimentally actuated particles. This verifies the model and confirms the correlation between the DEP force and the electronic signature profile. It follows that the electronic signatures can be used to quantify the actuation that the dielectric particle experiences as it traverses the electrode region. Using this principle, particles with different dielectric properties can be effectively identified based exclusively on their signature profile. This approach was used to differentiate viable from non-viable yeast cells (Saccharomyces cerevisiae).
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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) | 0.001 | 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".