Evidence map›Paper›PMID 32749434›Full record

ArticleFaraday discussions2020

Bio-inspired gas sensing: boosting performance with sensor optimization guided by "machine learning".

R A Potyrailo, J Brewer, B Cheng, M A Carpenter, N Houlihan, A Kolmakov

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

R A PotyrailoGE Research, Niskayuna, NY, USA. potyrailo@ge.com.ORCID 0000-0002-4823-1962
J BrewerGE Research, Niskayuna, NY, USA. potyrailo@ge.com.
B ChengGE Research, Niskayuna, NY, USA. potyrailo@ge.com.
M A CarpenterSUNY Polytechnic Institute, Albany, NY, USA.
N HoulihanSUNY Polytechnic Institute, Albany, NY, USA.
A KolmakovNational Institute of Standards and Technology, Gaithersburg, MD, USA.ORCID 0000-0001-5299-4121

Funding

Intramural NIST DOC 9999-NIST
6 · The paper itself

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.

Indexed as

Machine LearningBiosensing TechniquesGasesGoldMetal NanoparticlesOptics and PhotonicsReproducibility of ResultsGasesGold

Identifiers

PMID32749434
PMCPMC7986473

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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.