Development of passive wireless temperature sensors using metamaterials
Bibliographic record
Abstract
Wireless passive temperature sensors have been receiving increasing attention due to the ever-growing need of higher energy efficiency and precise monitoring of temperatures in high temperature energy conversion systems such as gas turbines and coal-based power plants. Unfortunately, the harsh environment such as high temperature and corrosive atmosphere present in these systems has significantly limited the reliability and increased the cost of current solutions. Therefore, this paper presents the concept and design of a low cost, passive, and wireless temperature sensor that can withstand high temperature and harsh environment. The temperature sensor was designed following the principle of metamaterials by utilizing Closed Ring Resonators (CRR) in a dielectric matrix. The proposed wireless, passive temperature sensor behaves like an LC circuit, which has a resonance frequency that is temperature dependent. A full wave electromagnetic solver Ansys Ansoft HFSS was used to validate the model and to evaluate the effect of different geometry and combination of SRR structures on the resonance frequency and sensitivity of the proposed sensor. Conventional compression method using a die-punch assembly was used to fabricate the sensors. To simplify the sensor design, commercially available metal washers were used as CRR structures. Barium Titanate (BTO) was used as dielectric materials. Free space testing was done using a pair of horn antenna connected with a network analyzer to analyze the sensor response at room temperature.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".