Abstract
In this research, a compact electronic nose (e-nose) based on a shear horizontal surface acoustic wave (SH-SAW) sensor array is proposed for the NO2 detection, classification and discrimination among some of the most relevant surrounding toxic chemicals, such as carbon monoxide (CO), ammonia (NH3), benzene (C6H6) and acetone (C3H6O). Carbon-based nanostructured materials (CBNm), such as mesoporous carbon (MC), reduced graphene oxide (rGO), graphene oxide (GO) and polydopamine/reduced graphene oxide (PDA/rGO) are deposited as a sensitive layer with controlled spray and Langmuir–Blodgett techniques. We show the potential of the mass loading and elastic effects of the CBNm to enhance the detection, the classification and the discrimination of NO2 among different gases by using Machine Learning (ML) techniques (e.g., PCA, LDA and KNN). The small dimensions and low cost make this analytical system a promising candidate for the on-site discrimination of sub-ppm NO2.
Highlights
Chemical sensors play a relevant role in our modern society for mobile applications, traffic safety and health care
To verify that sensor materials are suitable as NO2 gas sensors we further investigate the sensors’ behavior by measuring the response in short periods (2 min) and continuous cycles of exposition and purging
The e-nose successfully detected nitrogen dioxide (NO2), monoxide (CO), ammonia (NH3), benzene (C6H6) and acetone (C3H6O), in a wide variety of concentrations, with high selectivity and sensitivity, showing a pattern for each toxic agent and high efficiency to discriminate between interfering gases and NO2
Summary
Chemical sensors play a relevant role in our modern society for mobile applications, traffic safety and health care. One of the principal functions of these sensors is the monitoring of chemical compounds It has been becoming increasingly challenging in several applications related to air quality assessment [1,2,3,4,5,6,7] and medical diagnostics [8,9]. These needs have led to the emergence of new generations of low cost, portable and reliable gas sensor devices with high potential discrimination among low concentrations of analytes of interest. We demonstrate that the elastic and the mass loading effects of carbon-based sensors are suitable for NO2 discrimination by supervised and unsupervised ML techniques in a versatile and compact system
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
