Multi gas sensors using one nanomaterial, temperature gradient, and machine learning algorithms for discrimination of gases and their concentration

In this work, four identical micro sensors on the same chip with noble metal decorated tin oxide nanowires as gas sensing material were located at different distances from an integrated heater to work at different temperatures. Their responses are combined in highly informative 4D points that can qualitatively (gas recognition) and quantitatively (concentration estimate) discriminate all the tested gases. Two identical chips were fabricated with tin oxide (SnO2) nanowires decorated with different metal nanoparticles: one decorated with Ag nanoparticles and one with Pt nanoparticles. Support Vector Machine was used as the “brain” of the sensing system. The results show that the systems using these multisensor chips were capable of achieving perfect classification (100%) and good estimation of the concentration of tested gases (errors in the range 8–28%). The Ag decorated sensors did not have a preferential gas, while Pt decorated sensors showed a lower error towards acetone, hydrogen and ammonia. Combination of the two sensor chips improved the overall estimation of gas concentrations, but the individual sensor chips were better for some specific target gases. [Display omitted] •SnO2 nanowires were grown on-chip and used as resistive sensor.•4 nanosensors at different distances from a heater become a micro-chip.•2 different multi-sensors are decorated with Ag and Pt nanoparticles.•5 different gases are tested with each device and combining responses.•Combined sensor shows perfect classification and average concentration error