Reconfigurable Prototyping Microfluidic Platform for DEP Manipulation and Capacitive Sensing
Bibliographic record
Abstract
In this paper, we present a new rapid prototyping platform dedicated to dielectrophoretic microfluidic manipulation and capacitive cell sensing. The proposed platform offers a reconfigurable design including 4 independently programmable output channels to be distributed across 64 electrodes. Although its range of frequency covers up to 3.4 MHz, signal amplitude accuracy ( +/-10%) was demonstrated for frequencies up to 1 MHz and channel-to-channel phase shift setting was stable up to 1.5 MHz. A test of maximum resistive load showed a 10% attenuation of a 12 V peak-to-peak signal with a 22 Ω load. The platform has an advanced capacitive sensor to measure capacitance variation between in-channel electrodes with a sampling frequency up to 5 kH z. Experimental data of capacitive sensor showed a sensitivity of 100 fF. The sensor can be extended to 4 parallel measurements with lower frequency. We also present a new assembly technique for reusable microfluidic chip based on anisotropic adhesive conductive film, epoxy and PDMS. The proposed platform provides a wide range of control signals depending on the type of manipulation as sine, rectangular or square wave. The frequency range is extendible up to 3.4 MHz, in addition to a programmable phase shift circuit with a minimum phase step of 3.6(°) for each signal.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".