부산대학교 도서관

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 NO[sub.2] detection, classification and discrimination among some of the most relevant surrounding toxic chemicals, such as carbon monoxide (CO), ammonia (NH[sub.3]), benzene (C[sub.6]H[sub.6]) and acetone (C[sub.3]H[sub.6]O). 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 NO[sub.2] 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 NO[sub.2].
Author(s): Carlos Cruz (corresponding author) [1,2,*]; Daniel Matatagui (corresponding author) [1,3,*]; Cristina Ramírez [4]; Isidro Badillo-Ramirez [5,6]; Emmanuel de la O-Cuevas [5,7]; José M. Saniger [5]; Mari Carmen Horrillo [1] [...]