An on-chip microarray platform for material-temperature optimization and gas discrimination.

The demand for gases detection has promoted extensive research into gas sensors. Sensor arrays can be used as electronic olfaction units for complex gas detection and identification. However, the gas sensing properties are affected by various factors. The multi-dimensional nature of those interactions is crucial for creating a sensor array with diverse responses, but it is also challenging to fabricate an array considering all these parameters, particularly in miniaturization. In this paper, we present a framework that can take multiple factors into consideration and help to fabricate an optimal array for gas identification. 32 sensors including different MOS materials (SnO 2 , In 2 O 3 , WO 3), morphologies (microsphere, nanoparticle, nanoflake), doped/loaded metals (Pd, Pt, Ce, Ru, Rb), and operational temperatures (250℃∼370℃) are discussed. A material-temperature-analyte-response database was then established by storing their response characteristics towards low ppm concentrations of ammonia, ethyl acetate, toluene, methanol, ethanol, and acetone. An optimized array was then constructed out of 10,518,300 possible combinations of materials and operational temperatures. Results showed improvements in discrimination ability and the optimized array integrated into the chip can provide vector signals with high selectivity, completely recognizing all the 6 target gases and even for their multi-component mixture after linear discriminant analysis transformation. [Display omitted] • An on-chip microarray platform considering multiple factors. • An effective and operational method to reasonably optimize the array. • A highly selective array capable of distinguish different gases and mixtures. [ABSTRACT FROM AUTHOR]