Nonequilibrium sensing of volatile compounds using active and passive analyte delivery.

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.

Designing angle-independent structural colors using Monte Carlo simulations of multiple scattering.

Disordered nanostructures with correlations on the scale of visible wavelengths can show angle-independent structural colors. These materials could replace dyes in some applications because the color is tunable and resists photobleaching. However, designing nanostructures with a prescribed color is difficult, especially when the application-cosmetics or displays, for example-requires specific component materials. A general approach to solving this constrained design problem is modeling and optimization: Using a model that predicts the color of a given system, one optimizes the model parameters under constraints to achieve a target color. For this approach to work, the model must make accurate predictions, which is challenging because disordered nanostructures have multiple scattering. To address this challenge, we develop a Monte Carlo model that simulates multiple scattering of light in disordered arrangements of spherical particles or voids. The model produces quantitative agreement with measurements when we account for roughness on the surface of the film, particle polydispersity, and wavelength-dependent absorption in the components. Unlike discrete numerical simulations, our model is parameterized in terms of experimental variables, simplifying the connection between simulation and fabrication. To demonstrate this approach, we reproduce the color of the male mountain bluebird (Sialia currucoides) in an experimental system, using prescribed components and a microstructure that is easy to fabricate. Finally, we use the model to find the limits of angle-independent structural colors for a given system. These results enable an engineering design approach to structural color for many different applications.

Colorimetric Ethanol Indicator Based on Instantaneous, Localized Wetting of a Photonic Crystal.

Easy-to-use sensors for ethanol solutions have broad applications ranging from monitoring alcohol quality to combating underage drinking. Although there are a number of electronic and colorimetric sensors available for determining alcohol concentration, there is currently no device that can concurrently provide a prompt, well-defined, quickly recoverable readout and remain readily affordable. Here, we developed a field-ready, colorimetric indicator that provides a fast, clear identification of ethanol-water mixtures between 0 and 40% based on the discoloration of a wetted photonic crystal. We cooperatively exploit the iridescence and the geometrical gating in silica inverse opal films (IOFs), together with a fine-tuned surface chemistry gradient, to distinguish ethanol concentrations by their wettability patterns in the different segments of the IOFs. The resultant all-in-one colorimetric sensor delivers a striking and instantaneous optical response at an ethanol concentration as low as 5%. We further improve the ease of use by seamlessly integrating this colorimetric platform with drinking glassware (a glass stirrer and a vial). This research provides an optimal means for colorimetric ethanol detection and is a step toward the immersible sensing of diverse molecules (e.g., biomarkers) in aqueous solutions without expensive laboratory tests.