Automatic particle detection and sorting in an electrokinetic microfluidic chip
Bibliographic record
Abstract
This paper reports a lab-on-a-chip device that can automatically detect and sort particles based on their size differences with a high resolution. The PDMS-glass microfluidic chip is made by soft-lithography technique. A differential resistive pulse sensor is employed to electrically detect the sizes of the particles in EOF generated by applying DC voltages across channels. The detected resistive pulse sensor signals, whose amplitudes are proportional to particles' sizes, will automatically trigger the sorting process that is controlled by applying a voltage pulse (36 V) whenever a target particle is detected. This method was applied to automatically detect and sort polystyrene particles and microalgae in aqueous solutions. Sorting 5 μm polymer particle from a mixture of 4- and 5-μm polystyrene particles in aqueous solution, i.e. 1 μm sorting resolution, was demonstrated. The device described in this paper is simple, automatic, and label-free with high sorting resolution. It has wide applications in sample pretreatment and target particles detection.
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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".