Microfluidic electromanipulation with capacitive detection for the mechanical analysis of cells
Bibliographic record
Abstract
The mechanical behavior of cells offers insight into many aspects of their properties. We propose an approach to the mechanical analysis of cells that uses a combination of electromanipulation for stimulus and capacitance for sensing. To demonstrate this approach, polystyrene spheres and yeast cells flowing in a 25 mumx100 mum microfluidic channel were detected by a perpendicular pair of gold thin film electrodes in the channel, spaced 25 mum apart. The presence of cells was detected by capacitance changes between the gold electrodes. The capacitance sensor was a resonant coaxial radio frequency cavity (2.3 GHz) coupled to the electrodes. The presence of yeast cells (Saccharomyces cerevisiae) and polystyrene spheres resulted in capacitance changes of approximately 10 and 100 attoFarad (aF), respectively, with an achieved capacitance resolution of less than 2 aF in a 30 Hz bandwidth. The resolution is better than previously reported in the literature, and the capacitance changes are in agreement with values estimated by finite element simulations. Yeast cells were trapped using dielectrophoretic forces by applying a 3 V signal at 1 MHz between the electrodes. After trapping, the cells were displaced using amplitude and frequency modulated voltages to produce modulated dielectrophoretic forces. Repetitive displacement and relaxation of these cells was observed using both capacitance and video microscopy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".