Inkjet‐Printed rGO/binary Metal Oxide Sensor for Predictive Gas Sensing in a Mixed Environment

Selectivity for specific analytes and high-temperature operation are key challenges chemiresistive-type gas sensors. Complementary hybrid materials, such as reduced graphene oxide (rGO) decorated with metal oxides enables realization of room-temperature sensors enhanced sensitivity. However, sensor training to identify target gases accurate concentration measurement from mixtures still remain very challenging. This work proposes hybridization rGO CuCoOx binary a sensing material. Highly stable, NO2 50 ppb detection limit is demonstrated using inkjet printing. A framework then developed machine-intelligent recognition good visibility predict under an interfering atmosphere single sensor. Using ten unique parameters extracted the response, machine learning-based classifier provides decision boundary 98.1% accuracy, able correctly previously unseen humidity concentrations in environment. approach implementation intelligent platform printable, mixed environment irrespective ambient humidity.