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Title: Automatic signal decoding and sensor stability of a 3-electrode mixed-potential sensor for NO x/NH 3 quantification

Sensors to detect mixtures of NO x/NH 3 are needed to monitor emissions of diesel automobiles where a selective catalytic reduction system uses an NH 3 mediated reaction to reduce NO x. We report on the application of a three electrode La 0.8Sr 0.2CrO 3, Au 0.5Pd 0.5, Pt mixed potential sensor using yttria-stabilized-zirconia (YSZ) as a solid electrolyte to NO x/NH 3 sensing. Artificial neural networks were used to automatically decode the concentrations of NO x/NH 3 and errors of less than 15% are achieved. The optimal architecture for ANN decoding and the maximum density of training data points are also determined. The stability of the sensor was monitored by electrochemical impedance spectroscopy. The impedance associated with YSZ oxygen ion conduction and the electrochemical reactions at the three-phase interface are tracked for a period of over 100 days.
Authors:
 [1] ;  [1] ;  [2] ;  [3] ;  [4]
  1. Univ. of New Mexico, Albuquerque, NM (United States)
  2. ESL ElectroScience, King of Prussia, PA (United States)
  3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  4. Univ. of New Mexico, Albuquerque, NM (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Report Number(s):
SAND-2018-6731J; SAND2018-6731J
Journal ID: ISSN 0013-4686; 664711
Grant/Contract Number:
AC04-94AL85000
Type:
Accepted Manuscript
Journal Name:
Electrochimica Acta
Additional Journal Information:
Journal Volume: 283; Journal Issue: C; Journal ID: ISSN 0013-4686
Publisher:
Elsevier
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; NOx; NH3; mixed potential sensor; artificial neural network
OSTI Identifier:
1459908
Alternate Identifier(s):
OSTI ID: 1459926; OSTI ID: 1459935

Tsui, Lok-kun, Benavidez, Angelica, Palanisamy, Ponnusamy, Evans, Lindsey, and Garzon, Fernando. Automatic signal decoding and sensor stability of a 3-electrode mixed-potential sensor for NOx/NH3 quantification. United States: N. p., Web. doi:10.1016/j.electacta.2018.06.133.
Tsui, Lok-kun, Benavidez, Angelica, Palanisamy, Ponnusamy, Evans, Lindsey, & Garzon, Fernando. Automatic signal decoding and sensor stability of a 3-electrode mixed-potential sensor for NOx/NH3 quantification. United States. doi:10.1016/j.electacta.2018.06.133.
Tsui, Lok-kun, Benavidez, Angelica, Palanisamy, Ponnusamy, Evans, Lindsey, and Garzon, Fernando. 2018. "Automatic signal decoding and sensor stability of a 3-electrode mixed-potential sensor for NOx/NH3 quantification". United States. doi:10.1016/j.electacta.2018.06.133.
@article{osti_1459908,
title = {Automatic signal decoding and sensor stability of a 3-electrode mixed-potential sensor for NOx/NH3 quantification},
author = {Tsui, Lok-kun and Benavidez, Angelica and Palanisamy, Ponnusamy and Evans, Lindsey and Garzon, Fernando},
abstractNote = {Sensors to detect mixtures of NOx/NH3 are needed to monitor emissions of diesel automobiles where a selective catalytic reduction system uses an NH3 mediated reaction to reduce NOx. We report on the application of a three electrode La0.8Sr0.2CrO3, Au0.5Pd0.5, Pt mixed potential sensor using yttria-stabilized-zirconia (YSZ) as a solid electrolyte to NOx/NH3 sensing. Artificial neural networks were used to automatically decode the concentrations of NOx/NH3 and errors of less than 15% are achieved. The optimal architecture for ANN decoding and the maximum density of training data points are also determined. The stability of the sensor was monitored by electrochemical impedance spectroscopy. The impedance associated with YSZ oxygen ion conduction and the electrochemical reactions at the three-phase interface are tracked for a period of over 100 days.},
doi = {10.1016/j.electacta.2018.06.133},
journal = {Electrochimica Acta},
number = C,
volume = 283,
place = {United States},
year = {2018},
month = {6}
}