ArticleFaraday discussions2020
Bio-inspired gas sensing: boosting performance with sensor optimization guided by "machine learning".
Article in Faraday discussions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Individual optical multi-gas sensors as next generation second-order unobtrusive and continuous operation analytical instruments.Mikrochimica acta · 2025Article
- Insights into Target Gas-Oxygen Interactions in Highly Sensitive Gas Sensors Using Data-Driven Methods.ACS sensors · 2024Article
- Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities.Diagnostics (Basel, Switzerland) · 2024Review
- Skin-Interfaced Wearable Sweat Sensors for Precision Medicine.Chemical reviews · 2023Review
- Editorial: Current development on wearable biosensors towards biomedical applications.Frontiers in bioengineering and biotechnology · 2023Article
- Stability and Selective Vapor Sensing of Structurally Colored Lepidopteran Wings Under Humid Conditions.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The performance of existing gas sensors often degrades in field conditions because of the loss of 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 the capabilities of natural and fabricated photonic three-dimensional (3-D) nanostructures as sensors for the 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 these nanostructures to the vapors of interest and to suppress the response to ambient humidity. Further, we followed our previously developed design rules for sensing nanostructures and fabricated bioinspired inorganic 3-D nanostructures to achieve functionality beyond natural Morpho scales. These fabricated nanostructures have embedded catalytically active gold nanoparticles to operate at high temperatures of ≈300 °C for the detection of gases for solid oxide fuel cell (SOFC) applications. Our performance advances in the detection of multiple gaseous species with specific nanostructure designs were achieved by coupling the spectral responses of these nanostructures with machine learning (a.k.a. multivariate analysis, chemometrics) tools. Our newly acquired knowledge from studies of these natural and fabricated inorganic nanostructures coupled with machine learning data analytics allowed us to advance our design rules for sensing nanostructures toward the required gas selectivity for numerous gas monitoring scenarios at room and high temperatures for industrial, environmental, and other applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.