Li Dongsheng, Zhu Boyi, Pang Kai, Zhang Qian, Qu Mengjiao, Liu Weiting, Fu YongQing, Xie Jin
State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, People's Republic of China.
MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, 38 Zheda Road, Hangzhou 310027, People's Republic of China.
ACS Sens. 2022 May 27;7(5):1555-1563. doi: 10.1021/acssensors.2c00442. Epub 2022 May 12.
Piezoelectric cantilever resonator is one of the most promising platforms for real-time sensing of volatile organic compounds (VOCs). However, it has been a great challenge to eliminate the cross-sensitivity of various VOCs for these cantilever-based VOC sensors. Herein, a virtual sensor array (VSA) is proposed on the basis of a sensing layer of GO film deposited onto an AlN piezoelectric cantilever with five groups of top electrodes for identification of various VOCs. Different groups of top electrodes are applied to obtain high amplitudes of multiple resonance peaks for the cantilever, thus achieving low limits of detection (LODs) to VOCs. Frequency shifts of multiple resonant modes and changes of impedance values are taken as the responses of the proposed VSA to VOCs, and these multidimensional responses generate a unique fingerprint for each VOC. On the basis of machine learning algorithms, the proposed VSA can accurately identify different types of VOCs and mixtures with accuracies of 95.8 and 87.5%, respectively. Furthermore, the VSA has successfully been applied to identify the emissions from healthy plants and "plants with late blight" with an accuracy of 89%. The high levels of identifications show great potentials of the VSA for diagnosis of infectious plant diseases by detecting VOC biomarkers.
压电悬臂梁谐振器是实时检测挥发性有机化合物(VOCs)最有前景的平台之一。然而,对于这些基于悬臂梁的VOC传感器而言,消除各种VOCs的交叉敏感性一直是一项巨大的挑战。在此,基于沉积在具有五组顶部电极的AlN压电悬臂梁上的GO薄膜传感层,提出了一种虚拟传感器阵列(VSA),用于识别各种VOCs。应用不同组的顶部电极以获得悬臂梁多个共振峰的高振幅,从而实现对VOCs的低检测限(LODs)。多个共振模式的频移和阻抗值的变化被用作所提出的VSA对VOCs的响应,并且这些多维响应为每种VOC生成独特的指纹。基于机器学习算法,所提出的VSA能够分别以95.8%和87.5%的准确率准确识别不同类型的VOCs及其混合物。此外,VSA已成功应用于识别健康植物和“晚疫病植物”的排放物,准确率为89%。高水平的识别显示了VSA通过检测VOC生物标志物诊断植物传染病的巨大潜力。