Polyethyleneimine-Starch Functionalization of Single-Walled Carbon Nanotubes for Carbon Dioxide Sensing at Room Temperature

Abstract

There is an ever-growing interest in the detection of carbon dioxide (CO2) due to health risks associated with CO2 emissions. Hence, there is a need for low-power and low-cost CO2 sensors for efficient monitoring and sensing of CO2 analyte molecules in the environment. This study reports on the synthesis of single-walled carbon nanotubes (SWCNTs) that are functionalized using polyethyleneimine and starch (PEI-starch) in order to fabricate a PEI-starch functionalized SWCNT sensor for reversible CO2 detection under ambient room conditions (T = 25 °C; RH = 53%). Field-emission scanning electron microscopy, high-resolution transmission electron microscopy, Raman spectroscopy, and Fourier transform infrared spectroscopy are used to analyze the physiochemical properties of the as-synthesized gas sensor. Due to the large specific surface area of SWCNTs and the efficient CO2 capturing capabilities of the amine-rich PEI layer, the sensor possesses a high CO2 adsorption capacity. When exposed to varying CO2 concentrations between 50 and 500 ppm, the sensor response exhibits a linear relationship with an increase in analyte concentration, allowing it to operate reliably throughout a broad range of CO2 concentrations. The sensing mechanism of the PEI-starch-functionalized SWCNT sensor is based on the reversible acid-base equilibrium chemical reactions between amino groups of PEI and adsorbed CO2 molecules, which produce carbamates and bicarbonates. Due to the presence of hygroscopic starch that attracts more water molecules to the surface of SWCNTs, the adsorption capacity of CO2 gas molecules is enhanced. After multiple cycles of analyte exposure, the sensor recovers to its initial resistance level via a UV-assisted recovery approach. In addition, the sensor exhibits great stability and reliability in multiple analyte gas exposures as well as excellent selectivity to carbon dioxide over other interfering gases such as carbon monoxide, oxygen, and ammonia, thereby showing the potential to monitor CO2 levels in various infrastructure.