A Novel Thermo-FET Sensing System for Binary Chemical Solvent Monitoring Applications
Bibliographic record
Abstract
This article introduces a new field-effect transistor (FET)-based sensing system designed to monitor charge/polarity changes during the evaporation of microsize water droplets containing polar substances such as ethanol and methanol. The system utilizes a high transconductance interdigital open-gate junction FET (ID-OGJFET) with a sizeable direct-on-channel sensing area (>16 mm2) in direct contact with the liquid droplet. A new electrofluidic package has also been developed to facilitate sample access on the FET chip, which prevents leakage and connects the sensor to readout systems. An experimental procedure is designed to demonstrate the functionality and applicability of this FET platform sensing system for chemical analysis, such as measuring the concentration of ethanol or methanol in water using the evaporation-based sensing mechanism enabled by the ID-OGJFET. Real-time video capture is employed for monitoring and control. The sensor measures ethanol and methanol concentrations ranging from 0% to 100% with steps of 10% EtOH/MetOH in water, utilizing the evaporation-based sensing mechanism and current output of the ID-OGJFET sensor. The minimum meaningful measured concentration is 1% (v/v) for ethanol and 1.49% (v/v) for methanol. The combined uncertainty analysis of methanol’s concentration predictions ranged from 3.29 to 77.91 s. The expanded uncertainty varied from 6.58 to 155.82 s at a 95% confidence level for concentrations from 0% v/v to 100% with 10% intervals. For ethanol, the combined uncertainties ranged from 4.68 to 98.95 s. The expanded uncertainties, calculated with a coverage factor 2, varied from 9.36 to 197.90 s at a 95% confidence level.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".