{"title":"Analysis of the Uncertainty in Measurements of Polymer Pellets Using Microwave Resonant Sensors","authors":"Dania Covarrubias-Martínez;Humberto Lobato-Morales;Alonso Corona-Chávez;Juan Manuel Ramírez-Cortés;Germán Andrés Álvarez-Botero;Gabriela Méndez-Jerónimo;Tejinder Kaur Kataria","doi":"10.1109/JSEN.2024.3488556","DOIUrl":null,"url":null,"abstract":"The analysis and evaluation of the uncertainty in microwave measurements of some polymer plastic materials in the form of small pellets is presented in this article. Two different resonant sensors, cavity and planar, operating around 2.45 GHz are used to measure the materials. The presented uncertainty analysis is based on the measured resonant parameters from the sensors and represents a statistical tool capable of generating relevant information such as an adequate number of tests, uncertainty levels, correlation coefficient, covariance matrix, and confidence ellipses, which can be highly useful in the analysis of pellet or grained materials using microwave methods, and for fast and accurate decisions involving materials evaluation. It will be shown that a number of 40 tests for each sample is adequate for a stable uncertainty, and due to the E-field distribution and interaction with the samples, the cavity sensor develops lower uncertainty in resonant frequency compared to the planar circuit, thus, it can be a more reliable sensor for polymer pellet measurements.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"40839-40846"},"PeriodicalIF":4.3000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/10745230/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The analysis and evaluation of the uncertainty in microwave measurements of some polymer plastic materials in the form of small pellets is presented in this article. Two different resonant sensors, cavity and planar, operating around 2.45 GHz are used to measure the materials. The presented uncertainty analysis is based on the measured resonant parameters from the sensors and represents a statistical tool capable of generating relevant information such as an adequate number of tests, uncertainty levels, correlation coefficient, covariance matrix, and confidence ellipses, which can be highly useful in the analysis of pellet or grained materials using microwave methods, and for fast and accurate decisions involving materials evaluation. It will be shown that a number of 40 tests for each sample is adequate for a stable uncertainty, and due to the E-field distribution and interaction with the samples, the cavity sensor develops lower uncertainty in resonant frequency compared to the planar circuit, thus, it can be a more reliable sensor for polymer pellet measurements.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
-Sensor Phenomenology, Modelling, and Evaluation
-Sensor Materials, Processing, and Fabrication
-Chemical and Gas Sensors
-Microfluidics and Biosensors
-Optical Sensors
-Physical Sensors: Temperature, Mechanical, Magnetic, and others
-Acoustic and Ultrasonic Sensors
-Sensor Packaging
-Sensor Networks
-Sensor Applications
-Sensor Systems: Signals, Processing, and Interfaces
-Actuators and Sensor Power Systems
-Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting
-Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data)
-Sensors in Industrial Practice