NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.
Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.
An official website of the United States government
Here’s how you know
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Bio-inspired gas sensing: boosting performance with sensor optimization guided by "machine learning"
Published
Author(s)
Radislav A. Potyrailo, J. Brewer, B. Cheng, M. A. Carpenter, N. Houlihan, Andrei Kolmakov
Abstract
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.
Potyrailo, R.
, Brewer, J.
, Cheng, B.
, Carpenter, M.
, Houlihan, N.
and Kolmakov, A.
(2020),
Bio-inspired gas sensing: boosting performance with sensor optimization guided by “machine learning", Faraday Discussions, [online], https://doi.org/10.1039/D0FD00035C
(Accessed October 6, 2025)