A Wireless Passive Sensor for Temperature Compensated Remote pH Monitoring
Bibliographic record
Abstract
Temperature must be accounted for in order to provide accurate measurements in electrode-based pH sensors. We present an integrated wireless passive sensor for remote pH monitoring employing temperature compensation. The sensor is a resonant circuit consisting of a planar spiral inductor connected in parallel to a temperature-dependent resistor (thermistor) and a voltage-dependent capacitor (varactor). A pH combination electrode consisting of an iridium/iridium oxide sensing electrode and a silver/silver chloride reference electrode, is connected in parallel with the varactor. A potential difference change across the electrodes due to pH variation of the solution changes the voltage-dependent capacitance and shifts the resonant frequency, while temperature of the solution affects the resistance and changes the quality factor of the sensor. An interrogator coil is inductively coupled to the sensor inductor and remotely tracks the resonant frequency and quality factor of the sensor. The sensor is calibrated for temperature over a range of 25°C -55°C and pH over a 1.5-12 dynamic range. By employing temperature compensation, a measurement accuracy of less than 0.1 pH is achieved and the response time of the sensor is demonstrated to be less than 1 s. The sensor overcomes the pH measurement error due to the temperature dependence of electrode-based passive pH sensors and has applications in remote pH monitoring where temperature varies over a wide range.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".