Light-Emitting Diode Array with Optical Linear Detector Enables High-Throughput Differential Single-Cell Dielectrophoretic Analysis
Bibliographic record
Abstract
This paper presents a lens-free imaging approach utilizing an array of light sources, capable of measuring the dielectric properties of many particles simultaneously. This method employs coplanar electrodes to induce velocity changes in flowing particles through dielectrophoretic forces, allowing the inference of individual particle properties from differential velocity changes. Both positive and negative forces are detectable. The light source utilized in this system is composed of LEDs with a wavelength of 470 nm, while detection is performed using a 256-element optical array detector. Measurements with 10 μm polystyrene beads demonstrate this method can resolve changes equivalent to a Clausius-Mossotti factor of 0.18. Simulations in this work, using values from the literature, predict that Clausius-Mossotti factor differences of 0.18 are sufficient to differentiate viable from nonviable cells and cancerous from multidrug-resistant cancerous cells. We demonstrate that for Chinese hamster ovary (CHO) cells, the method can collect a dielectric response spectrum for a large number of cells in several minutes. We demonstrate that for CHO cells, Clausius-Mossotti factor differences of 0.18 can be discriminated. Due to its simple detection apparatus and the utilization of high-throughput, wide, clog-resistant channels, this method holds promise for a wide range of applications.
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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 | 0.001 |
| 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".