Kinetic Modelling and Machine Learning Approaches on the Conductance Transients of Zn0.5Ni0.5Fe2O4 Sensors for Addressing Cross-sensitivity towards Ethanol and Acetone Vapors

The accurate discrimination among various volatile organic compounds, especially ethanol and acetone possess a serious concern for metal oxide based chemiresistive sensors. The work presents a systematic approach to address the issue by utilizing superior sensing potentiality of Zn0.5Ni0.5Fe2O4 coupled with efficient machine learning (ML) techniques. The work provides a thorough understanding on the synthesis, characterization of Zn0.5Ni0.5Fe2O4 nanoparticles and evaluating their sensing performance towards ethanol and acetone vapors. The optimized sensor performance recorded under varying concentrations (100-1000 ppm) of analytes across the range of temperature (225-300 °C) provides lesser selectivity between acetone and ethanol. The Langmuir-Hinshelwood reaction mechanism was invoked further to address the selectivity issue by modeling the response transients of the sensor to get an insight into sensing interaction at microscopic level. The estimated activation energy values of ethanol (0.26 eV) have been found to be smaller compared to that of acetone (0.34 eV), explaining little higher response of the sensor towards ethanol. Moreover, some efficient ML algorithms were employed to predictively analyze the acquired sensing data for achieving more precise discriminations between these two analytes.