Targeting biomarkers in the gas phase through a chemoresistive electronic nose based on graphene functionalized with metal phthalocyanines. Issue 1 (21st December 2022)

• Record Type: Journal Article
• Authors: Freddi, Sonia; Marzuoli, Camilla; Pagliara, Stefania; Drera, Giovanni; Sangaletti, Luigi
• Is Part Of: RSC advances. Volume 13:Issue 1(2023)
• Page Start: 251
• Page End: 263
• Publication Date: 2022-12-21
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d2ra07607a
• Languages: English
• ISSNs: 2046-2069

Abstract

Abstract : An electronic nose based on graphene chemiresistor sensors functionalized with phthalocyanines has been developed to detect selected biomarkers in the gas phase for breathomics, environmental monitoring, and food control applications. Abstract : Electronic noses (e-noses) have received considerable interest in the past decade as they can match the emerging needs of modern society such as environmental monitoring, health screening, and food quality tracking. For practical applications of e-noses, it is necessary to collect large amounts of data from an array of sensing devices that can detect interactions with molecules reliably and analyze them via pattern recognition. The use of graphene (Gr)-based arrays of chemiresistors in e-noses is still virtually missing, though recent reports on Gr-based chemiresistors have disclosed high sensing performances upon functionalization of the pristine layer, opening up the possibility of being implemented into e-noses. In this work, with the aim of creating a robust and chemically stable interface that combines the chemical properties of metal phthalocyanines (M-Pc, M = Fe, Co, Ni, Zn) with the superior transport properties of Gr, an array of Gr-based chemiresistor sensors functionalized with drop-cast M-Pc thin layers has been developed. The sensing capability of the array was tested towards biomarkers for breathomics application, with a focus on ammonia (NH3 ). Exposure to NH3 has been carried out drawing the calibration curve and estimating the detection limit for all the sensors. The discrimination capability of the array has then been tested, carrying out exposure to several gases (hydrogen sulfide, acetone, ethanol, 2-propanol, water vapour and benzene) and analysing the data through principal component analysis (PCA). The PCA pattern recognition results show that the developed e-nose is able to discriminate all the tested gases through the synergic contribution of all sensors.