A CMOS Time-of-Evaporation Measurement Technique for Binary Chemical Solvent Monitoring
Bibliographic record
Abstract
This paper introduces a novel Complementary Metal-Oxide-Semiconductor (CMOS)-based sensing mechanism for measuring the Time of Evaporation (ToE) of binary chemical solvents. Utilizing an integrated CMOS capacitive sensor, the system detects evaporation from micro-liter volumes of chemical solvents deposited on the surface of interdigitated electrodes. This platform enables precise ToE measurement, which varies based on the composition of the binary solvent mixture. The functionality and practicality of the proposed sensing method were validated through experiments involving mixtures of pure water, ethanol, and methanol. Over 66 experimental results demonstrate that at room temperature, ToE serves as an indicator of the relative composition of two chemical solvents. Specifically, the ToE Change Ratio (TCR) and Capacitance Change Ratio (CCR) for water-ethanol mixtures were approximately 21.13% and 1.19%, respectively. Despite the droplet being freely placed on the CMOS chip at room temperature, the characterization curve between ToE and alcohol concentration exhibits linearity, with an R² value exceeding 0.945 for water-ethanol mixtures. These findings underscore the sensor's ability to provide rapid, accurate, and cost-effective liquid characterization. This evaporation-based sensing platform holds significant potential for advancing life sciences, offering a reliable and efficient tool for liquid analysis.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".