Machine Learning-Based Rapid Detection of Volatile Organic Compounds in a Graphene Electronic Nose

Abstract

Rapid detection of volatile organic compounds (VOCs) is growing in importance in many sectors. Noninvasive medical diagnoses may be based upon particular combinations of VOCs in human breath; detecting VOCs emitted from environmental hazards such as fungal growth could prevent illness; and waste could be reduced through monitoring of gases produced during food storage. Electronic noses have been applied to such problems, however, a common limitation is in improving selectivity. Graphene is an adaptable material that can be functionalized with many chemical receptors. Here, we use this versatility to demonstrate selective and rapid detection of multiple VOCs at varying concentrations with graphene-based variable capacitor (varactor) arrays. Each array contains 108 sensors functionalized with 36 chemical receptors for cross-selectivity. Multiplexer data acquisition from 108 sensors is accomplished in tens of seconds. While this rapid measurement reduces the signal magnitude, classification using supervised machine learning (Bootstrap Aggregated Random Forest) shows excellent results of 98% accuracy between 5 analytes (ethanol, hexanal, methyl ethyl ketone, toluene, and octane) at 4 concentrations each. With the addition of 1-octene, an analyte highly similar in structure to octane, an accuracy of 89% is achieved. These results demonstrate the important role of the choice of analysis method, particularly in the presence of noisy data. This is an important step toward fully utilizing graphene-based sensor arrays for rapid gas sensing applications from environmental monitoring to disease detection in human breath.

A Chemiresistor Sensor Array Based on Graphene Nanostructures: From the Detection of Ammonia and Possible Interfering VOCs to Chemometric Analysis

“…On the basis of these results, the present sensing layers do not display high selectivity: the extent of the response to ammonia ( Figure 4 a) is higher than the response to the other gases, as it is expected since the response to alcohols is usually low for carbon-based sensors [ 38 , 46 ], nevertheless the latter responses are not completely negligible ( Figure 4 b).…”

“…Carbon nanotubes have been largely studied as single sensors [ 26 , 27 , 28 ] and, even to a lesser extent, also as electronic noses [ 17 , 29 ], while, only in the past few years, graphene, thanks to its high sensitivity to the surface adsorption of gas molecules [ 30 ], its high in-plane conductivity, and its low electrical intrinsic noise [ 31 ], has attracted the interest or researchers working in the sensors field. Although recent studies have clearly demonstrated the possibility to develop highly sensitive graphene sensors after proper functionalization [ 32 , 33 , 34 , 35 , 36 , 37 ], still few are the works exploiting graphene to develop sensors array [ 38 , 39 , 40 ], especially in a chemiresistor configuration [ 41 ].…”

Targeting biomarkers in the gas phase through a chemoresistive electronic nose based on graphene functionalized with metal phthalocyanines

“…So far only few Gr-based enoses driven in a FET conguration have been reported, [33][34][35] which requires micro-electronic grade preparation of the device, or e-noses based on capacitors. 36 In turn, Gr-based enoses in a chemiresistive conguration can be assembled following facile preparation routes, but they are still missing.…”

Technology Roadmap for Flexible Sensors

“…For instance, graphene functionalized with various ligands and coupled with Au nanoparticles was used to construct an 8-sensor array that could classify 13 individual plant VOCs at >97% classification accuracy (Figure c) . A recent approach achieved the fabrication and utilization of an array of 108 graphene-based sensors functionalized with 36 chemical receptors for the discrimination of 6 gas species within a minute, shedding light on rapid VOC detection using large-scale sensor arrays. Overall, recent advances in flexible room-temperature gas sensor arrays have achieved lower power consumption, reduced fabrication cost, and greater wearability without sacrificing sensing performance. ,− Although machine learning algorithms capable of higher prediction accuracy can compensate for sensor selectivity shortfalls, ,, improving the specificity of each sensor remains a critical challenge.…”