ABSTRACT
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.
Subject(s)
Nose , Smell , Humans , Electronic Nose , Machine Learning , GasesABSTRACT
Volatile organic compounds (VOCs) are prevalent in daily life, from the lab environment to industrial applications, providing tremendous functionality but also posing significant health risk. Moreover, individual VOCs have individual risks associated with them, making classification and sensing of a broad range of VOCs important. This work details the application of electrochemically dealloyed nanoporous gold (NPG) as a VOC sensor through measurements of the complex electrical frequency response of NPG. By leveraging the effects of adsorption and capillary condensation on the electrical properties of NPG itself, classification and regression is possible. Due to the complex nonlinearities, classification and regression are done through the use of a convolutional neural network. This work also establishes key strategies for improving the performance of NPG, both in sensitivity and selectivity. This is achieved by tuning the electrochemical dealloying process through manipulations of the starting alloy and through functionalization with 1-dodecanethiol.
ABSTRACT
Volatile organic compounds (VOC) are ubiquitous in industrial applications creating a pressing desire for novel transduction pathways to build a broad family of new gas sensors. Nanoporous gold (NPG) is a material with a vast range of untapped potential applications; offering a high surface area found generally in nanomaterials, while also being comparatively simple to fabricate. NPG based sensors can also leverage the unique physics of gold at the nanoscale. In this work, we leverage the multiple unique nanoscale phenomena associated with NPG to demonstrate two novel transduction mechanisms to sense humidity, a model compound. Through direct electrical measurements of NPG, we were able to sense changes in the electronic properties of NPG induced by ambient humidity. We propose two novel transduction mechanisms: chemoresistive changes induced by surface adsorption and electrochemical capacitive changes induced by the electric double layer to detect humidity. To our knowledge this is the first reported application of both these mechanisms for sensing any volatile compounds utilizing NPG.
