Continuous Cell Characterization and Separation by Microfluidic Alternating Current Dielectrophoresis
Bibliographic record
Abstract
A novel alternating current (ac)-dielectrophoretic (DEP) microfluidic chip for continuous cell characterization and separation is presented in this paper. To generate DEP forces, two electrode-pads are embedded in a set of asymmetric orifices on the opposite sidewalls to produce the nonuniform electric fields. In the vicinity of a small orifice, the cells experience the strongest nonuniform gradient and are drawn toward it by the positive DEP forces, while the cells experiencing a negative DEP force are repelled away and move toward the large orifice. The DEP behaviors of yeast cells in suspending media with different ionic concentrations, i.e., different electrical conductivities, and over a large range of the ac electric field frequency were investigated. Furthermore, the lateral migrations of yeast cells as a function of the ac frequency were measured. The trends of measured lateral migrations of yeast cells are similar to the corresponding Clausius-Mossotti (CM) factors. In addition, by adjusting the frequency and strength of the ac electric field, the continuous separation of live and dead yeast cells as well as the yeast cells with targeted diameter and dielectric property can be easily achieved. This is the first time that the measurement of ac-DEP lateral migration of yeast cells in solutions with different electrical conductivities as a function of the applied frequency in a microfluidic chip was reported. This ac-DEP system provides a method to characterize the crossover frequency of the specific cells and manipulate the targeted 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".