DC-dielectrophoretic separation of microparticles using an oil droplet obstacle
Bibliographic record
Abstract
A new dielectrophoretic particle separation method is demonstrated and examined in the following experimental study. Current electrodeless dielectrophoretic (DEP) separation techniques utilize insulating solid obstacles in a DC or low-frequency AC field, while this novel method employs an oil droplet acting as an insulating hurdle between two electrodes. When particles move in a non-uniform DC field locally formed by the droplet, they are exposed to a negative DEP force linearly dependent on their volume, which allows the particle separation by size. Since the size of the droplet can be dynamically changed, the electric field gradient, and hence DEP force, becomes easily controllable and adjustable to various separation parameters. By adjusting the droplet size, particles of three different diameter sizes, 1 microm, 5.7 microm and 15.7 microm, were successfully separated in a PDMS microfluidic chip, under applied field strength in the range from 80 V cm-1 to 240 V cm-1. A very effective separation was realized at the low field strength, since the electric field gradient was proved to be a more significant parameter for particle discrimination than the applied voltage. By utilizing low strength fields and adaptable field gradient, this method can also be applied to the separation of biological samples that are generally very sensitive to high electric potential.
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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 | 0.001 |
| 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 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".