Bio-inspired gas sensing: boosting performance with sensor optimization guided by "machine learning"
Radislav A. Potyrailo, J. Brewer, B. Cheng, M. A. Carpenter, N. Houlihan, Andrei Kolmakov
Existing sensors for gaseous species often degrade their performance because of the loss of the measurement accuracy in the presence of interferences. Thus, new sensing approaches are required with improved sensor selectivity. We are developing a new generation of gas sensors, known as multivariable sensors, that have several independent responses for multi-gas detection with a single sensor. In this study, we analyze capabilities of natural and fabricated photonic three- dimensional (3-D) nanostructures as sensors for detection of different gaseous species such as vapors and non-condensable gases. We employed bare Morpho butterfly wing scales to control their gas selectivity with different illumination angles. Next, we chemically functionalized Morpho butterfly wing scales with a fluorinated silane to boost the response of such nanostructure to vapors of interest and to suppress the response to ambient humidity. Further, we followed our design rules of sensing nanostructures and fabricated bioinspired inorganic 3-D nanostructures to achieve a functionality beyond natural Morpho scales. These fabricated nanostructures have embedded catalytically active gold nanoparticles to operate at high temperatures of 300 oC for detection of gases for solid oxide fuel cell (SOFC) applications. Our performance advances in detection of multiple gaseous species with specific nanostructure designs were achieved by coupling spectral responses of these nanostructures with machine learning (a.k.a. multivariate analysis, chemometrics) tools. Our new acquired knowledge from studies of these natural and fabricated inorganic nanostructures coupled with machine learning data analytics allowed us to advance our design rules of sensing nanostructures toward needed gas selectivity for numerous gas monitoring scenarios at room and high temperatures for industrial, environmental, and other applications.
, Brewer, J.
, Cheng, B.
, Carpenter, M.
, Houlihan, N.
and Kolmakov, A.
Bio-inspired gas sensing: boosting performance with sensor optimization guided by “machine learning", Faraday Discussions, [online], https://doi.org/10.1039/D0FD00035C
(Accessed November 30, 2023)