A direct relationship between the sensitivity of the sensors and the intensity of IR CO2 peak in in situ FTIR-LCR meter chemi-impedance SnO2–carbon nanoparticles polymer-based sensors in the detection of organic compounds vapor

Viola O Okechukwu,Vuyo Mavumengwana,Messai A Mamo

doi:10.1063/5.0063604

Abstract

Three sensors were prepared with SnO2, carbon nanoparticles (CNPs), and cellulose acetate (CA) composites, and each sensor containing different amounts of SnO2 powder were prepared for the detection of n-dodecane, 2-hexanone, and 3-methylcyclopentanone vapor at room temperature. Sensors with a combination of CNP:CA, SnO2:CA, SnO2:CNPs, and SnO2 composites were used as control, and their performance was compared with that of the sensor based on SnO2:CNPs:CA toward a wide range of 2-hexanone and 3-methylcyclopentanone vapor concentrations. Sensors based on CNPs:CA and SnO2:CNPs:CA selectively responded very well to 3-methylcyclopentanone and 2-hexanone, respectively. The in situ FTIR study revealed that both sensors undergo a deep oxidation process during sensing and the sensitivity of the sensors directly relates to the IR intensity of the CO2 peak at 668 cm−1, and for highly sensitive sensors, the CO2 peak at 668 cm−1 is found to be very intense. The gradient area under the curve of the IR CO2 band at 668 cm−1 against time for more sensitive sensors toward the analyte is larger than that for less sensitive ones. The effect of the amount of SnO2 in the composites indicated that the sensors based on three sensing materials combined at a mass ratio of 1:1:3 were highly selective toward 3-methylcyclopentanone and less selective toward the other two analytes. Sensors based on the composition of SnO2:CNPs:CA at a mass ratio of 1.5:1:3 and those based on the composition of SnO2:CNPs:CA at a mass ratio of 2:1:3 were selective toward 2-hexanone and n-dodecane, respectively. All the fabricated sensors were found to have their sensing ability regenerated after the analytes were removed from the system without losing their sensing and recovery abilities.

Highlights

Composite materials prepared with conducting particles are expected to exhibit high sensitivity and show very low threshold and strong conductivity within the threshold.6,15 Carbon nanoparticles (CNPs) possess unique properties suitable for a diverse range of applications, and they are enriched with different functional groups.16,17 The synthesis methods for CNPs, like the synthesis of other nanomaterials, fall under two different approaches, namely, “top-down” and “bottom-up.” Usually, in the top-down approach, the carbon source is a large carbon material larger than graphite,18 soot,19 carbon nanotubes,20 and activated carbon,21 and this large structure can be broken into carbon nanoparticles using either mechanical or chemical processes
Liu et al.36 reported that non-conducting polymer membranes [such as cellulose acetate (CA)] can be used on metal oxide semiconductor gas sensors as a molecular sieve to improve the selectivity by introducing the sensitivity of the polymer layers
We report here for the first time the detection of dodecane, 3-hexanone, and 3-methylcyclopentanone vapors using metal oxide–carbon–polymer nanocomposite-based film solid-state sensors by varying the amount of SnO2 NTs in the composite

Summary

Introduction

Composite materials prepared with conducting particles are expected to exhibit high sensitivity and show very low threshold and strong conductivity within the threshold.6,15 Carbon nanoparticles (CNPs) possess unique properties suitable for a diverse range of applications, and they are enriched with different functional groups.16,17 The synthesis methods for CNPs, like the synthesis of other nanomaterials, fall under two different approaches, namely, “top-down” and “bottom-up.” Usually, in the top-down approach, the carbon source is a large carbon material larger than graphite,18 soot,19 carbon nanotubes,20 and activated carbon,21 and this large structure can be broken into carbon nanoparticles using either mechanical or chemical processes. To understand the sensing mechanism of the sensor based on CNPs:CA and SnO2:CNPs:CA, we set up an in situ FTIR while the sensor was exposed to the analyte vapor (see the setup in Fig. S2 in the supplementary material).

Results

Conclusion