Microwave MIMO E-Nose for Wireless Communication and Selective Detection of VOC Mixtures with Concentration Estimation

IF 9.1 1区 化学 Q1 CHEMISTRY, ANALYTICAL
Mohammad Mahmudul Hasan*, , , Onur Alev, , , Pavel Skrabanek, , , Gabriela Soukupová, , , Fatima Hassouna, , and , Michael Cheffena, 
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引用次数: 0

Abstract

We present the first dual-functional microwave electronic nose (E-nose) that enables wireless communication, VOC mixture detection, and reliable concentration estimation, designed for seamless integration with wireless sensor networks. The proposed E-nose features multiple-input multiple-output (MIMO) antenna system functionalized with molecularly imprinted polymer (MIP) and multiwalled carbon nanotube-based sensing materials for the selective detection of individual or mixed volatile organic compounds (VOCs). We addressed several novel challenges such as managing cross-reactivity under electromagnetic interference with wideband decoupling, employing a dual-branch neural network (NN) with feature prioritization and transducer behavior insights, and optimizing sensor placement for spatial isolation in a compact design. The developed E-nose demonstrated high sensitivity and selectivity for VOC mixtures at room temperature, with detection limits below safety thresholds, ensuring practical applicability. The optimized NN model achieved high predictive accuracy (R2 = 0.982 to 0.991), delivering near-perfect concentration estimations with negligible errors for pure (0.35%) and mixtures of 2–4 VOCs (2.0 to 3.5%). The proposed framework, customizable for detecting diverse VOCs and toxic gases, enables scalable indoor and outdoor air-quality monitoring. Its seamless dual functionality ensures uninterrupted wireless communication services during gas sensing, establishing a new paradigm in advanced sensor technology with microwave MIMO E-Nose systems.

用于无线通信的微波MIMO电子鼻及VOC混合物的选择性检测与浓度估计。
我们提出了第一个双功能微波电子鼻(E-nose),它可以实现无线通信,VOC混合物检测和可靠的浓度估计,旨在与无线传感器网络无缝集成。该电子鼻采用分子印迹聚合物(MIP)和多壁碳纳米管传感材料功能化的多输入多输出(MIMO)天线系统,用于选择性检测单个或混合挥发性有机化合物(VOCs)。我们解决了几个新的挑战,例如通过宽带解耦管理电磁干扰下的交叉反应性,采用具有特征优先级和换能器行为洞察力的双分支神经网络(NN),以及在紧凑的设计中优化传感器的空间隔离放置。所开发的电子鼻在室温下对VOC混合物具有较高的灵敏度和选择性,检测限低于安全阈值,确保了实用性。优化后的NN模型获得了很高的预测精度(R2 = 0.982至0.991),对纯(0.35%)和2-4 VOCs混合物(2.0至3.5%)提供了近乎完美的浓度估计,误差可以忽略。提出的框架,可定制检测各种挥发性有机化合物和有毒气体,实现可扩展的室内和室外空气质量监测。其无缝的双重功能确保了气体传感过程中不间断的无线通信服务,在微波MIMO E-Nose系统的先进传感器技术中建立了新的范例。
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来源期刊
ACS Sensors
ACS Sensors Chemical Engineering-Bioengineering
CiteScore
14.50
自引率
3.40%
发文量
372
期刊介绍: ACS Sensors is a peer-reviewed research journal that focuses on the dissemination of new and original knowledge in the field of sensor science, particularly those that selectively sense chemical or biological species or processes. The journal covers a broad range of topics, including but not limited to biosensors, chemical sensors, gas sensors, intracellular sensors, single molecule sensors, cell chips, and microfluidic devices. It aims to publish articles that address conceptual advances in sensing technology applicable to various types of analytes or application papers that report on the use of existing sensing concepts in new ways or for new analytes.
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