Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous Abstract[Research advances in the effects of elevated atmospheric carbon dioxide and ozone on biogenic VOCs emission]    Next AbstractSelective catalytic oxidation of formaldehyde on single V- and Cr-atom decorated magnetic C(4)N(3) substrate: A first principles study »

ACS Appl Mater Interfaces


Title:Virtual Sensor Array Based on Butterworth-Van Dyke Equivalent Model of QCM for Selective Detection of Volatile Organic Compounds
Author(s):Li D; Xie Z; Qu M; Zhang Q; Fu Y; Xie J;
Address:"State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, China. Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, U.K"
Journal Title:ACS Appl Mater Interfaces
Year:2021
Volume:20210921
Issue:39
Page Number:47043 - 47051
DOI: 10.1021/acsami.1c13046
ISSN/ISBN:1944-8252 (Electronic) 1944-8244 (Linking)
Abstract:"Recently virtual sensor arrays (VSAs) have been developed to improve the selectivity of volatile organic compound (VOC) sensors. However, most reported VSAs rely on detecting single property change of the sensing material after their exposure to VOCs, thus resulting in a loss of much valuable information. In this work, we propose a VSA with the high dimensionality of outputs based on a quartz crystal microbalance (QCM) and a sensing layer of MXene. Changes in both mechanical and electrical properties of the MXene film are utilized in the detection of the VOCs. We take the changes of parameters of the Butterworth-van Dyke model for the QCM-based sensor operated at multiple harmonics as the responses of the VSA to various VOCs. The dimensionality of the VSA's responses has been expanded to four independent outputs, and the responses to the VOCs have shown good linearity in multidimensional space. The response and recovery times are 16 and 54 s, respectively. Based on machine learning algorithms, the proposed VSA accurately identifies different VOCs and mixtures, as well as quantifies the targeted VOC in complex backgrounds (with an accuracy of 90.6%). Moreover, we demonstrate the capacity of the VSA to identify 'patients with diabetic ketosis' from volunteers with an accuracy of 95%, based on the detection of their exhaled breath. The QCM-based VSA shows great potential for detecting VOC biomarkers in human breath for disease diagnosis"
Keywords:"Biomarkers/analysis Breath Tests/methods Humans Neural Networks, Computer Principal Component Analysis Quartz Crystal Microbalance Techniques/*methods Support Vector Machine Titanium/*chemistry Volatile Organic Compounds/*analysis MXene Qcm VOCs sensor br;"
Notes:"MedlineLi, Dongsheng Xie, Zihao Qu, Mengjiao Zhang, Qian Fu, Yongqing Xie, Jin eng 2021/09/22 ACS Appl Mater Interfaces. 2021 Oct 6; 13(39):47043-47051. doi: 10.1021/acsami.1c13046. Epub 2021 Sep 21"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 25-12-2024