A Microwave Stripline Ring Resonator Sensor Exploiting the Thermal Coefficient of Dielectric Constant for High-Temperature Sensing
Bibliographic record
Abstract
A stripline transmission-line (TL)-based temperature sensor for use in harsh environments is designed to exploit the thermal coefficient of the dielectric constant (TCD) of the microwave (MW) substrate material. Rogers 3210 substrate is selected owing to its high TCD of −459 ppm/°C and a dielectric constant of 10.8. A ring resonator is designed for 2.4 GHz, with the ring selected for the simple geometric dependence of its resonant frequency on radius. TLs are gap-coupled to the ring, with widths designed for 50- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> impedance matching and lengths optimized for a compact form factor. Upper and lower ground planes shield the dielectric, resonator, and TLs from environmental disturbances, such as debris and moisture, which could otherwise disrupt temperature measurement. Furthermore, the electric field is completely contained within the homogeneous dielectric, providing improved sensitivity compared to similar designs using microstrip technology. The fabricated sensor requires uniform compression of layers to mitigate the effects of air pockets and thermal expansion of materials. It is found that the sensor requires temperature-conditioning, approximately 70 h cycling between 30 °C and 80 °C, before its resonant frequency reaches a steady state suitable for instantaneous temperature measurement. Subsequent 10 °C- and 2 °C-step experiments are performed in the 30 °C–80 °C and 30 °C–40 °C ranges, respectively. As a result, a linear sensitivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 500$ </tex-math></inline-formula> kHz/°C is identified. The duration of these experiments and time-based data are representative of applications where long-term temperature monitoring is required.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".