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.

Transfer of 1D Photonic Crystals via Spatially Resolved Hydrophobization.

1D photonic crystals (1DPCs) are well known from a variety of applications ranging from medical diagnostics to optical fibers and optoelectronics. However, large-scale application is still limited due to complex fabrication processes and bottlenecks in transferring 1DPCs to arbitrary substrates and pattern creation. These challenges were addressed by demonstrating the transfer of millimeter- to centimeter-scale 1DPC sensors comprised of alternating layers of H3 Sb3 P2 O14 nanosheets and TiO2 nanoparticles based on a non-invasive chemical approach. By depositing the 1DPC on a sacrificial layer of lithium tin sulfide nanosheets and hydrophobizing only the 1DPC by intercalation of n-octylamine via the vapor phase the 1DPC can be detached from the substrate by immersing the sample in water. Upon exfoliation of the hydrophilic sacrificial layer, the freestanding 1DPC remains at the water-air interface. In a second step, it can be transferred to arbitrary surfaces such as curved glass. In addition, the transfer of patterned 1DPCs is demonstrated by combining the sacrificial layer approach with area-resolved intercalation and etching. The fact that the sensing capability of the 1DPC is not impaired and can be modified after transfer renders this method a generic platform for the fabrication of photonic devices.

Tracking Molecular Diffusion in One-Dimensional Photonic Crystals.

The intuitive use, inexpensive fabrication, and easy readout of colorimetric sensors, including photonic crystal architectures and Fabry-Pérot interference sensors, have made these devices a successful commercial case, and yet, understanding how the diffusion of analytes occurs throughout the structure is a key ingredient for designing the response of these platforms on demand. Herein, the diffusion of amines in a periodic multilayer system composed of two-dimensional nanosheets and dielectric nanoparticles is tracked by a combination of spectroscopic measurements and theoretical modelling. It is demonstrated that diffusion is controlled by the molecular size, with larger molecules showing larger layer swelling and slower diffusion times, which translates into important sensor characteristics such as signal change and saturation time. Since the approach visualizes the analyte impregnation process in a time- and spatially resolved fashion, it directly relates the macroscopic color readout into microscopic processes occurring at the molecular level, thus opening the door to rational sensor design.