Reconfigurable Prototyping Microfluidic Platform for DEP Manipulation and Capacitive Sensing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it