TY - JOUR
T1 - Virtual Sensor Array Based on Piezoelectric Cantilever Resonator for Identification of Volatile Organic Compounds
AU - Li, Dongsheng
AU - Zhu, Baoyi
AU - Pang, Kai
AU - Zhang, Qian
AU - Qu, Mengjiao
AU - Liu, Weiting
AU - Fu, Yongqing
AU - Xie, Jin
N1 - Funding Information: This work is supported by the National Key R&D Program of China (Grant No. 2018YFC0114900), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LZ19E050002), the National Natural Science Foundation of China (Grant Nos. NSFC 51875521 and 52175552), the Science Fund for Creative Research Groups of National Natural Science Foundation of China (Grant No. 51821093), the Engineering Physics and Science Research Council of UK (Grant No. EPSRC 10 EP/P018998/1), and International Exchange Grant (IEC/NSFC/201078) through Royal Society U.K. and the NSFC.
PY - 2022/5/27
Y1 - 2022/5/27
N2 - 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.
AB - 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.
KW - AlN piezoelectric cantilever
KW - Machine learning
KW - Plant diseases diagnosis
KW - Virtual sensor array
KW - VOC identification
UR - http://www.scopus.com/inward/record.url?scp=85131106765&partnerID=8YFLogxK
U2 - 10.1021/acssensors.2c00442
DO - 10.1021/acssensors.2c00442
M3 - Article
SN - 2379-3694
VL - 7
SP - 1555
EP - 1563
JO - ACS Sensors
JF - ACS Sensors
IS - 5
ER -