Selectivity for specific analytes and high-temperature operation are key challenges for chemiresistive-type gas sensors. Complementary hybrid materials, such as reduced graphene oxide (rGO) decorated with metal oxides enables realization of room-temperature sensors with enhanced sensitivity. However, sensor training to identify target gases and accurate concentration measurement from gas mixtures still remain very challenging. This work proposes hybridization of rGO with CuCoOx binary metal oxide as a sensing material. Highly stable, room-temperature NO2 sensors with a 50 ppb of detection limit is demonstrated using inkjet printing. A framework is then developed for machine-intelligent recognition with good visibility to identify specific gases and predict concentration under an interfering atmosphere from a single sensor. Using ten unique parameters extracted from the sensor response, the machine learning-based classifier provides a decision boundary with 98.1% accuracy, and is able to correctly predict previously unseen NO2 and humidity concentrations in an interfering environment. This approach enables implementation of an intelligent platform for printable, room-temperature gas sensors in a mixed environment irrespective of ambient humidity.
- gas prediction
- gas sensors
- inkjet printing
- machine learning
- metal oxide
- principal component analysis
FingerprintDive into the research topics of 'Inkjet-Printed rGO/binary Metal Oxide Sensor for Predictive Gas Sensing in a Mixed Environment'. Together they form a unique fingerprint.
Technological Platform Physical Chemistry and characterization
Facility/equipment: Technological Platform