An electroactive microfluidic platform integrated with AM-pDEP focusing and side-counter design for selective cell sorting and single-cell quantification
Bibliographic record
Abstract
Rapid isolation and precise quantification of target cancer cells are crucial for precision medicine applications. Conventional fluorescence-based methods require bulky optical instrumentation and specialized expertise. Recent advances in label-free microfluidic techniques have attempted to overcome these limitations but often suffer from reduced reliability and throughput when integrating multiple functions. Here, we demonstrate an electroactive microfluidic platform that integrates functions of selective concentration, particle focusing, and single-cell level quantification without introducing additional complex physical microstructures. Our device employs the dielectrophoresis (DEP) effect combined with an interdigitated cell sorter to achieve selective cell concentration. For quantification and characterization, the device leverages the impedimetric Coulter principle to achieve precise particle counting and characterization at single-cell resolution. To address the inherent tradeoff between sensitivity and throughput in the traditional Coulter counter, we utilize the tilted interdigitated electrode configuration excited by an amplitude-modulated (AM) Positive DEP (pDEP) signal instead of the commonly employed narrow channel or sheath flow to realize the effective focusing and alignment of the cell stream. The focusing module is combined with our side-counter design to detect cells. The performance of our device and its embedded modules has been verified with mixed breast cancer and blood model cell lines, where high consistency between optical and electrical detection has been demonstrated. We expect the integration of the proposed AM-pDEP focusing approach and the corresponding microfluidic design for cell sorter and on-chip flow cytometry would offer an alternative engineering solution to the stream-based on-chip single-cell analysis where reliable cell focusing and single-cell sensing are required.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".