A High Dynamic Range Dual 8×16 Capacitive Sensor Array for Life Science Applications
Bibliographic record
Abstract
This paper presents a fully integrated complementary metal-oxide-semiconductor (CMOS) capacitive sensor array for life science applications. This sensing device consists of an array of 16 × 16 interdigitated electrodes (IDEs) integrated with a charge-based readout and multiplexing circuitries on the same chip. This chip was implemented in 0.35 μm AMS CMOS process. This sensing device has a wide input capacitance range (ICR) of about 100 fF and a resolution of 150 aF, and the capability of temporal, spatial, and dielectric sensing. It makes it possible to develop a low-cost, multimodal, calibration-free sensing platform for life science applications. Here, we demonstrate and discuss the functionality and applicability of the proposed sensing device by introducing various chemical solvents including ethanol, methanol, and pure water. The simulation and experimental results achieved in this work have taken us one step closer to a fully automated calibration-free multimodal capacitive sensing platform for high-throughput drug development and other purposes.
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.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".