Carboxylated Single-Walled Carbon Nanotube Sensors with Varying pH for the Detection of Ammonia and Carbon Dioxide Using an Artificial Neural Network

Abstract

Single-walled carbon nanotubes (SWCNTs) have long been advocated for the detection of various gases and vapors. Often, strategies to modify the nanotubes have been shown to be successful in eliciting a more sensitive response from the nanotubes when exposed to the target gas relative to the pristine material. Carboxylation of the SWCNTs is one such strategy commonly used in gas sensor construction. Interestingly, addition of acid or base to the carboxylation process can change the pH of the resulting material and yield inks with varying pH values that can be used in a sensor array. Here we show orthogonal responses to NH3 and CO2 from eight such materials with controlled pH values in the range of 1.9–12.1. The results and the approach will be applicable to sensing other acidic and basic gases and also can expand further to other sensor systems to maximize and obtain orthogonal sensor responses. A neural network architecture is used successfully to convert the measured resistance change into analyte concentration.