Focused electrophoretic motion and selected electrokinetic dispensing of particles and cells in cross‐microchannels
Bibliographic record
Abstract
The electrokinetic focusing and the resultant accelerated electrophoretic motion of polystyrene particles and red blood cells were visualized in microfluidic cross-channels. The experimentally measured width of the focused stream and the measured velocity increase of particles and cells at different voltage ratios follow the proposed analytical formula within the experimental error. The attained velocity increase is insensitive to the particle size, particle property (i.e., particle or cell), and particle trajectory. By solving the electrical potential field in the cross-channel at the experimental conditions, we demonstrate that the squeezed electrical field lines in the channel intersection determine the shape of the focused stream, and the nonuniform distribution of axial electrical field strength underlies the variation of particle/cell electrophoretic velocity through the focusing region. However, the dielectrophoretic force resulting from the nonuniform electrical field in the intersection seems to push the acceleration region of particles and cells slightly in the downstream direction. We have also achieved the single particle/cell dispensing by instantly triggering an electrical pulse perpendicular to the focused particulate flow in a double-cross microchannel. The electrokinetic manipulation of particle/cell in microchannels demonstrated in this work can be used for developing integrated lab-on-a-chip devices for studies of cells.
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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) | 0.000 | 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".