Evidence map›Paper›PMID 35888851›Full record

ArticleMicromachines2022

Design and Research of Wireless Passive High-Temperature Sensor Based on SIW Resonance.

Fujia Xu, Shujing Su, Lili Zhang, Ting Ren

Abstract read
In one paragraph

Article in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Fujia XuState Key Laboratory of Dynamic Measurement Technology, School of Instrument and Electronics, North University of China, No.3 Xueyuan Road, Taiyuan 030051, China.
Shujing SuState Key Laboratory of Dynamic Measurement Technology, School of Instrument and Electronics, North University of China, No.3 Xueyuan Road, Taiyuan 030051, China.
Lili ZhangState Key Laboratory of Dynamic Measurement Technology, School of Instrument and Electronics, North University of China, No.3 Xueyuan Road, Taiyuan 030051, China.
Ting RenState Key Laboratory of Dynamic Measurement Technology, School of Instrument and Electronics, North University of China, No.3 Xueyuan Road, Taiyuan 030051, China.

Funding

National Nature Science Foundation of China 51821003National Nature Science Foundation of China 51875534
6 · The paper itself

Abstract

The temperature of advanced components in aviation and aerospace fields is difficult to obtain timely. In this study, we aimed to investigate microwave backscattering technology combined with the theory of substrate integrated waveguide and resonant cavity to design a wireless passive temperature sensor and explore its potential in this field. We employed silicon carbide and aluminum ceramic as the substrate to make sensors. The interrogation antenna was designed to test the sensor, which could completely cover the working frequency of the sensor and had good radiation characteristics. Based on the test results, the silicon carbide sensor was capable of bearing a temperature limit of about 1000 °C compared to the alumina sensor. From 25 °C to 500 °C, its sensitivity was 73.68 kHz/°C. Furthermore, the sensitivity was 440 kHz/°C in the range of 501 °C to 1000 °C. Moreover, we observed the surface of this sensor by using the scanning electron microscope, and the results showed that the damage to the sensor surface film structure caused by long-term high temperature is the major reason for the failure of the sensor. In conclusion, the performance of the silicon carbide sensor is superior to the alumina sensor.

Indexed as

high temperature sensorresonant cavitysilicon carbidesubstrate integrated waveguidewireless passive

Identifiers

PMID35888851
PMCPMC9318039

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.