Dielectrophoresis-Based Integrated Lab-on-Chip for Nano and Micro-Particles Manipulation and Capacitive Detection
Bibliographic record
Abstract
We present in this paper a new Lab-on-Chip (LoC) architecture for dielectrophoresis-based cell manipulation, detection, and capacitive measurement. The proposed LoC is built around a CMOS full-custom chip and a microfluidic structure. The CMOS chip is used to deliver all parameters required to control the dielectrophoresis (DEP) features such as frequency, phase, and amplitude of signals spread on in-channel electrodes of the LoC. It is integrated to the LoC and experimental results are related to micro and nano particles manipulation and detection in a microfluidic platform. The proposed microsystem includes an on-chip 27-bit frequency divider, a digital phase controller with a 3.6° phase shift resolution and a 2.5 V dynamic range. The sensing module is composed of a 3 × 3 capacitive sensor array with 10 fF per mV sensitivity, and a dynamic range of 1.5 V. The obtained results show an efficient nano and micro-particles (PC05N, PA04N and PS03N) separation based on frequency segregation with low voltages less than 1.7 V and a fully integrated and reconfigurable system.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".