Numerical studies of manipulation and separation of Janus particles in nano-orifice based DC-dielectrophoretic microfluidic chips
Bibliographic record
Abstract
Abstract The direct current dielectrophoretic (DC-DEP) manipulation and separation of polystyrene-based Janus particles and the homogeneous polystyrene particles in microchannels are numerically investigated. To induce the DEP force, a small electric potential difference is applied across the microchannel via a smaller nano-orifice on one side of the channel walls and a larger micro-orifice on the opposite channel wall. A strong non-uniform electric field gradient is generated by the asymmetric orifices, and the particles will undergo the DEP forces when moving with the flow through the vicinity of the small orifice, where the strongest electrical field gradient exists. By adjusting the electrical conductivity of the suspending solution, one kind of the particles will experience the negative DEP force while another will undergo the positive DEP force. In this way, the separation of 5 µ m Janus particles and homogeneous polystyrene particles, and the separation of 3 µ m and 5 µ m Janus particles were numerically demonstrated. Moreover, in order to further understand the dielectrophoretic motion of the Janus particles, the DC-DEP force on the Janus particles was analyzed and the effects of the electric fields, as well as the coating coverage, thickness, and electric conductivity of the Janus particles were studied. The results show that the Janus particles with gold coating coverage over 50% will experience positive DEP forces and be attracted towards the maximum electric fields. It is also found that the effect of the gold coating thickness of the Janus particles on their trajectories can be neglected when using the DC-DEP method.
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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".