An Analytical Conductance Model for Gas Detection Based on a Zigzag Carbon Nanotube Sensor

Recent trends in gas sensing via carbon nanomaterials: outlook and challenges

The presence of harmful and poisonous gases in the environment can have dangerous effects on human health, and therefore portable, flexible, and highly sensitive gas sensors are in high demand for environmental monitoring, pollution control, and medical diagnosis. Currently, the commercialized sensors are based on metal oxides, which generally operate at high temperatures. Additionally, the desorption of chemisorbed gas molecules is also challenging. Hence, due to the large surface area, high flexibility, and good electrical properties of carbon nanomaterials (CNMs) such as carbon nanotubes, graphene and their derivatives (graphene oxide, reduced graphene oxide, and graphene quantum dots), they are considered to be the most promising chemiresistive sensing materials, where their electrical resistance is affected by their interaction with the analyte. Further, to increase their selectivity, nanocomposites of CNMs with metal oxides, metallic nanoparticles, chalcogenides, and polymers have been studied, which exhibit better sensing capabilities even at room temperature. This review summarizes the state-of-the-art progress in research related to CNMs-based sensors. Moreover, to better understand the analyte adsorption on the surface of CNMs, various sensing mechanisms and dependent sensing parameters are discussed. Further, several existing challenges related to CNMs-based gas sensors are elucidated herein, which can pave the way for future research in this area.

Modeling the Conductivity Response to NO2 Gas of Films Based on MWCNT Networks

This work proposes a model describing the dynamic behavior of sensing films based on functionalized MWCNT networks in terms of conductivity when exposed to time-variable concentrations of NO2 and operating with variable working temperatures. To test the proposed model, disordered networks of MWCNTs functionalized with COOH and Au nanoparticles were exploited. The model is derived from theoretical descriptions of the electronic transport in the nanotube network, of the NO2 chemisorption reaction and of the interaction of these two phenomena. The model is numerically implemented and then identified by estimating all the chemical/physical quantities involved and acting as parameters, through a model fitting procedure. Satisfactory results were obtained in the fitting process, and the identified model was used to further the analysis of the MWCNT sensing in dynamical conditions.