Chemical Gas Sensors Studied at SENSOR Lab, Brescia (Italy): From Conventional to EnergyEfficient and Biocompatible Composite Structures In this paper, we present the investigations on metal oxidebased gas sensors considering the works performed at SENSOR lab, University of Brescia (Italy). We reported the developments in synthesis techniques for the preparation of doped and functionalized lowdimensional metal oxide materials. Furthermore, we discussed our achievements in the fabrication of heterostructures with unique functional features. In particular, we focused on the strategies to improve the sensing performance of metal oxides at relatively low operating temperatures. We presented our studies on surface photoactivation of sensing structures considering the application of biocompatible materials in the architecture of the functional devices as well.

Genetic Algorithm Design of MOFbased Gas Sensor Arrays for CO2inAir Sensing Gas sensor arrays, also known as electronic noses, leverage a diverse set of materials to identify the components of complex gas mixtures. Metalorganic frameworks (MOFs) have emerged as promising materials for electronic noses due to their highsurface areas and chemical as well as structural tunability. Using our recently reported genetic algorithm design approach, we examined a set of 50 MOFs and searched through over 1.125 xc3x97 1015 unique array combinations to identify optimal arrays for the detection of CO2 in air. We found that despite individual MOFs having lower selectivity for O2 or N2 relative to CO2, intelligently selecting the right combinations of MOFs enables accurate prediction of the concentrations of all components in the mixture (i.e., CO2, O2, N2). We also analyzed the physical properties of the elements in the arrays to develop an intuition for improving array design. Notably, we found that an array whose MOFs have diversity in their volumetric surface areas has improved sensing. Consistent with this observation, we found that the best arrays consistently had greater structural diversity (e.g., pore sizes, void fractions, and surface areas) than the worst arrays.