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Although sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch, the development of artificial noses is significantly behind their biological counterparts. This largely stems from the sophistication of natural olfaction, which relies on both fluid dynamics within the nasal anatomy and the response patterns of hundreds to thousands of unique molecular-scale receptors. We designed a sensing approach to identify volatiles inspired by the fluid dynamics of the nose, allowing us to extract information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) rather than relying on a large sensor array. By accentuating differences in the nonequilibrium mass-transport dynamics of vapors and training a machine learning algorithm on the sensor output, we clearly identified polar and nonpolar volatile compounds, determined the mixing ratios of binary mixtures, and accurately predicted the boiling point, flash point, vapor pressure, and viscosity of a number of volatile liquids, including several that had not been used for training the model. We further implemented a bioinspired active sniffing approach, in which the analyte delivery was performed in well-controlled 'inhale-exhale' sequences, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. Our results outline a strategy to build accurate and rapid artificial noses for volatile compounds that can provide useful information such as the composition and physical properties of chemicals, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.

The majority of industrial chemical processes—from production of organic and inorganic compounds to air and water treatment—rely on heterogeneous catalysts. The performance of these catalysts has improved over the past several decades; in parallel, many innovations have been presented in publications, demonstrating increasingly higher efficiency and selectivity. One common challenge to adopting novel materials in real-world applications is the need to develop robust and cost-effective synthetic procedures for their formation at scale. Herein, we focus on the scalable production of a promising new class of materials—raspberry-colloid-templated (RCT) catalysts—that have demonstrated exceptional thermal stability and high catalytic activity. The unique synthetic approach used for the fabrication of RCT catalysts enables great compositional flexibility, making these materials relevant to a wide range of applications. Through a series of studies, we identified stable formulations of RCT materials that can be utilized in the common industrial technique of spray drying. Using this approach, we demonstrate the production of highly porous Pt/Al2O3 microparticles with high catalytic activity toward complete oxidation of toluene as a model reaction.

Droplet microarray technology is of great interest in biology and chemistry as it allows for significant reactant savings and massive parallelization of experiments. Upon scaling down the footprint of each droplet in an array, it becomes increasingly challenging to produce the array drop‐by‐drop. Therefore, techniques for parallelized droplet production are developed, e.g., dip‐coating of biphilic substrates. However, it is in general difficult to tailor the characteristics of individual droplets, such as size and content, without updating the substrate. Here, the method of dip‐coating of uniformly patterned biphilic substrates in so‐called “acceleration‐mode” to produce droplet arrays featuring gradients in droplet height for fixed droplet footprint is developed. The results herein present this method applied to produce drops with base diameters varying over orders of magnitude, from as high as 6 mm to as small as 50 µm; importantly, the experimentally measured power‐law‐dependency of volume on capillary‐number matches analytical theory for droplet formation on heterogenous substrates though the precise quantitative values likely differ due to 2D substrate patterning. Gradient characteristics, including average droplet volume, steepness of the gradient, and its monotonicity, can all be tuned by changing the dip‐coating parameters, thus providing a robust method for high‐throughput screening applications and experiments.