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Title: Using sensor arrays to decode NO x/NH 3/C 3H 8 gas mixtures for automotive exhaust monitoring

Abstract

In this work, an array of four mixed-potential sensors is employed to identify and quantify gases in complex mixtures of unknown composition which mimic diesel engine exhaust. The sensors use dense metal and metal oxide electrodes with a porous ceramic electrolyte, yttria-stabilized zirconia (YSZ). Since the sensors exhibit cross-specificity toward target gases, we develop a computational model for predicting gas concentrations in the mixtures. Our model is based on fundamental principles of gas-sensor interactions and, furthermore, takes into account the non-linearity of the observed sensor voltage response. Our approach enables accurate predictions of gas concentrations from the voltage output of the sensor array exposed to an extensive set of mixtures involving C 3H 8, NH 3, NO and NO 2. We find that our predictions remain accurate even if the model is trained using a reduced set of mixtures, or if the number of sensors is decreased to three or two. Finally, our experimental and computational framework can be used to decipher contents of complex gas mixtures of unknown composition in numerous industrial, automotive, and national security settings.

Authors:
 [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2];  [1]
  1. Rutgers Univ., Piscataway, NJ (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1467332
Report Number(s):
LA-UR-17-25585
Journal ID: ISSN 0925-4005
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
Sensors and Actuators. B, Chemical
Additional Journal Information:
Journal Volume: 264; Journal Issue: C; Journal ID: ISSN 0925-4005
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; Energy Sciences; Mixed Potential Sensors; YSZ; Exhaust gas monitoring

Citation Formats

Javed, Unab, Ramaiyan, Kannan, Mukundan, Rangachary, Brosha, Eric Lanich, Kreller, Cortney, and Morozov, Alexandre. Using sensor arrays to decode NOx/NH3/C3H8 gas mixtures for automotive exhaust monitoring. United States: N. p., 2018. Web. doi:10.1016/j.snb.2018.02.069.
Javed, Unab, Ramaiyan, Kannan, Mukundan, Rangachary, Brosha, Eric Lanich, Kreller, Cortney, & Morozov, Alexandre. Using sensor arrays to decode NOx/NH3/C3H8 gas mixtures for automotive exhaust monitoring. United States. doi:10.1016/j.snb.2018.02.069.
Javed, Unab, Ramaiyan, Kannan, Mukundan, Rangachary, Brosha, Eric Lanich, Kreller, Cortney, and Morozov, Alexandre. Thu . "Using sensor arrays to decode NOx/NH3/C3H8 gas mixtures for automotive exhaust monitoring". United States. doi:10.1016/j.snb.2018.02.069. https://www.osti.gov/servlets/purl/1467332.
@article{osti_1467332,
title = {Using sensor arrays to decode NOx/NH3/C3H8 gas mixtures for automotive exhaust monitoring},
author = {Javed, Unab and Ramaiyan, Kannan and Mukundan, Rangachary and Brosha, Eric Lanich and Kreller, Cortney and Morozov, Alexandre},
abstractNote = {In this work, an array of four mixed-potential sensors is employed to identify and quantify gases in complex mixtures of unknown composition which mimic diesel engine exhaust. The sensors use dense metal and metal oxide electrodes with a porous ceramic electrolyte, yttria-stabilized zirconia (YSZ). Since the sensors exhibit cross-specificity toward target gases, we develop a computational model for predicting gas concentrations in the mixtures. Our model is based on fundamental principles of gas-sensor interactions and, furthermore, takes into account the non-linearity of the observed sensor voltage response. Our approach enables accurate predictions of gas concentrations from the voltage output of the sensor array exposed to an extensive set of mixtures involving C3H8, NH3, NO and NO2. We find that our predictions remain accurate even if the model is trained using a reduced set of mixtures, or if the number of sensors is decreased to three or two. Finally, our experimental and computational framework can be used to decipher contents of complex gas mixtures of unknown composition in numerous industrial, automotive, and national security settings.},
doi = {10.1016/j.snb.2018.02.069},
journal = {Sensors and Actuators. B, Chemical},
number = C,
volume = 264,
place = {United States},
year = {2018},
month = {2}
}

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