用于医疗保健应用的氧气检测微波气体传感器的特性与神经建模。

Characterization and Neural Modeling of a Microwave Gas Sensor for Oxygen Detection Aimed at Healthcare Applications.

机构信息

Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, 18000 Niš, Serbia.

MIFT Department, University of Messina, Viale F. Stagno d'Alcontres 31, 98166 Messina, Italy.

出版信息

Sensors (Basel). 2020 Dec 13;20(24):7150. doi: 10.3390/s20247150.

DOI:10.3390/s20247150

PMID:33322232

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7764220/

Abstract

The studied sensor consists of a microstrip interdigital capacitor covered by a gas sensing layer made of titanium dioxide (TiO). To explore the gas sensing properties of the developed sensor, oxygen detection is considered as a case study. The sensor is electrically characterized using the complex scattering parameters measured with a vector network analyzer (VNA). The experimental investigation is performed over a frequency range of 1.5 GHz to 2.9 GHz by placing the sensor inside a polytetrafluoroethylene (PTFE) test chamber with a binary gas mixture composed of oxygen and nitrogen. The frequency-dependent response of the sensor is investigated in detail and further modelled using an artificial neural network (ANN) approach. The proposed modelling procedure allows mimicking the measured sensor performance over the whole range of oxygen concentration, going from 0% to 100%, and predicting the behavior of the resonant frequencies that can be used as sensing parameters.

摘要

该研究传感器由一个微带交指电容器组成，其表面覆盖有一层由二氧化钛 (TiO) 制成的气体敏感层。为了探索所开发传感器的气体传感性能，以氧气检测为例进行了研究。使用矢量网络分析仪 (VNA) 测量的复散射参数对传感器进行了电气特性表征。通过将传感器放置在由聚四氟乙烯 (PTFE) 制成的测试室内的二元气体混合物（由氧气和氮气组成）中，在 1.5GHz 至 2.9GHz 的频率范围内进行了实验研究。详细研究了传感器的频率相关响应，并使用人工神经网络 (ANN) 方法对其进行了进一步建模。所提出的建模过程允许模拟整个氧气浓度范围内（从 0%到 100%）的传感器性能，并预测可作为传感参数的谐振频率的行为。