Novel CMOS Thermo-Capacitive Sensing Method for Lab-on-Chip Applications
Bibliographic record
Abstract
This paper introduces a novel droplet-sensing platform enabling precise monitoring of droplet evaporation. The platform includes a capacitive interface circuit connected to a 256-electrode array, allowing for the measurement of small capacitance changes when the electrodes are exposed to chemical solvents. While capacitive sensors offer advantages in measuring dielectric changes, their effectiveness in reliably monitoring small changes during droplet evaporation, especially in low-alcohol concentration water-alcohol mixtures, is limited. The study highlights the challenges associated with using capacitive sensors for this purpose and proposes a novel approach that analyzes the duration of droplet presence before evaporation as a crucial parameter for assessing suitable mixtures in life science applications. Experimental results from our thermo-capacitive sensing method demonstrate that, in open-top sensing conditions, the average time of evaporation (ToE) change ratio (TCR) for water-alcohol mixtures is approximately 5 to 7 times higher than the average capacitance change ratio (CCR). This research aims to advance droplet monitoring techniques in various scientific applications.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".