TY - JOUR
T1 - Bayesian decoding of the ammonia response of a zirconia-based mixed-potential sensor in the presence of hydrocarbon interference
AU - Tsitron, Julia
AU - Kreller, Cortney R.
AU - Sekhar, Praveen K.
AU - Mukundan, Rangachary
AU - Garzon, Fernando H.
AU - Brosha, Eric L.
AU - Morozov, Alexandre V.
N1 - Funding Information: This research was funded by the US DOE , EERE , Vehicle Technology Programs . The authors wish to thank Technology Development Manager Roland Gravel. A.V.M. acknowledges support from an Alfred P. Sloan Research Fellowship. Appendix A Funding Information: Julia Tsitron earned a combined B.A./M.A. degree in Physics from Hunter College, City University of New York (2008), and is pursuing a Ph.D. in Computational Biology and Molecular Biophysics at Rutgers University. Her research interests include developing mathematical models and software tools for analysis of sensor response, robustness, and optimization, to be used across a broad range of applications. She is co-inventor (patent pending) of Chemosensory arrays, a yeast cell-based sensor array and method of discriminating ligands in complex chemical mixtures. Cortney Kreller received her B.S. in Chemical Engineering from the University of Maryland, Baltimore County in 2004. She received her Ph.D. in Chemical Engineering from the University of Washington in 2011. Dr. Kreller is currently a post-doctoral researcher in the Materials Physics and Applications division at Los Alamos National Laboratory. Her research interests include electrochemical sensors, fuel cells, and electrosynthesis of fuels. Praveen Sekhar is an assistant professor in the School of Engineering and Computer Science at Washington State University Vancouver, WA since 2011. He was a postdoctoral research associate in the Materials Physics and Applications division (MPA-11) at the Los Alamos National Laboratory for two years. He received his B.E. (distinction) from Coimbatore Institute of Technology, India, in 2001. He received his Masters and Ph.D. in Electrical Engineering from the University of South Florida, Tampa, FL in 2005 and 2008, respectively. His dissertation focused on the synthesis, characterization and applications of silica nanowires won the ‘Outstanding Dissertation’ award. Currently, his research interests include the development of electrochemical gas sensors and biosensors in addition to materials science and characterization, impedance spectroscopy, microfabrication, and applied statistics. Rangachary Mukundan graduated from the University of Roorkee (India) with a Bachelor's degree in Metallurgical Engineering in 1991. He was a research fellow at the University of Pennsylvania, Philadelphia, Pennsylvania, and received his Ph.D. in Materials Science and Engineering in February 1997. His thesis titled “Characterization of Mixed-Conducting Barium Cerate-Based Perovskites for Potential Fuel Cell Applications” was awarded the S.J. Stein Prize for superior achievement in the field of new or unique materials in electronics. Mukundan is a staff member in the Materials Physics and Applications division (MPA-11) at the Los Alamos National Laboratory. Currently, his research interests include fuel cell materials, electrochemical gas sensors, proton-conductors, and permeation membranes. Fernando Garzon is the Team Leader for materials chemistry in Materials Physics and Applications division (MPA-11) at the Los Alamos National Laboratory. Dr. Garzon received his BSE in Metallurgy and Materials Science, and his Ph.D. in Materials Science and Engineering from the University of Pennsylvania (Philadelphia). His research interests include: the development of micro-electrochemical sensors, polymer fuel cell technology, solid oxide fuel cell technology, the thermochemistry of electronically conducting transition metal oxides, thin film growth of oxide materials and ceramic membrane technology for sensor, gas separation and fuel cell applications and the characterization of nano-scale materials by advanced X-ray scattering methods. Dr. Garzon has co-authored over 100 peer-reviewed scientific publications and made numerous invited conference presentations. Research highlights include: the first experimental determination of the thermodynamic metastability of high temperature superconductors published in Science, the development of very low surface resistance superconductor thin films for microwave applications, the invention of non-porous ceramic hydrogen separation membranes and the development of sulfur tolerant solid oxide fuel cells. He is also the co-inventor of an R&D 100 award-winning high temperature, combustion control sensor currently being licensed to industry, and a new class of solid-state gas sensors. He holds six patents in electrochemical technology and has three more pending. Dr. Garzon is the president of the Electrochemical Society. Eric Brosha is a staff member in the Materials Physics and Applications division (MPA-11) at the Los Alamos National Laboratory. He received his B.A. (Summa Cum Laude) in Physics from Rider College, Lawrenceville, NJ in 1989. He was awarded an Ashton Fellowship from the University of Pennsylvania, Philadelphia, PA, in 1989 and received his Ph.D. in Materials Engineering in August 1993. Currently, his research interests include synthesis of PEM fuel cell catalysts, electrochemical gas sensors, materials chemistry and electrochemistry of high-temperature solid-oxide fuel cell electrolyte and electrode materials, X-ray diffraction, X-ray fluorescence spectroscopy and thermal analysis of materials. Alexandre V. Morozov was born in Rostov-on-Don, Russia. He received his Ph.D. in Physics from the University of Washington in 2003. From 2003 to 2007 he was a postdoctoral associate at the Center for Studies in Physics and Biology, Rockefeller University. In 2007 he joined the Department of Physics and Astronomy at Rutgers University, where he is now an Associate Professor. He is also a faculty member at the BioMaPS Institute for Quantitative Biology. In 2009, he was a recipient of an Alfred P. Sloan Research Fellowship. His current research interests include biological physics, non-equilibrium dynamics, and Bayesian modeling.
PY - 2014/3/1
Y1 - 2014/3/1
N2 - Zirconia-based mixed-potential sensors are a promising technology for monitoring levels of nitrogen oxides and ammonia in diesel engine exhaust. However, in addition to the target gases these sensors react to unburned hydrocarbons present in the gas mixture. The observed cross-interference between target and non-target gases cannot be fully mitigated by applying different bias currents to the sensor. On the other hand, sensor sensitivity and selectivity toward various components of the mixture depend on the bias current setting, allowing us to effectively create an array of sensors by applying different bias currents to the same device. Here we show how such an array can be used to predict absolute concentrations of ammonia in the presence of propylene. Our Bayesian framework can be easily generalized to other types of sensors and to more complex chemical mixtures. It consists of two steps: the calibration step, in which the parameters of the model are determined a priori in the laboratory setting, and the prediction step, which mimics the deployment of the device in real-world conditions. We investigate a linear model, in which response of the sensor to each gas is assumed to be additive, and a nonlinear model, which takes interference between gases into account. We find that the nonlinear model, although more complex, yields more accurate predictions. We also find that relatively few sensor readings and bias current settings are required to make reliable predictions of gas concentrations in the mixture, making our approach feasible in a variety of automotive and other technological settings.
AB - Zirconia-based mixed-potential sensors are a promising technology for monitoring levels of nitrogen oxides and ammonia in diesel engine exhaust. However, in addition to the target gases these sensors react to unburned hydrocarbons present in the gas mixture. The observed cross-interference between target and non-target gases cannot be fully mitigated by applying different bias currents to the sensor. On the other hand, sensor sensitivity and selectivity toward various components of the mixture depend on the bias current setting, allowing us to effectively create an array of sensors by applying different bias currents to the same device. Here we show how such an array can be used to predict absolute concentrations of ammonia in the presence of propylene. Our Bayesian framework can be easily generalized to other types of sensors and to more complex chemical mixtures. It consists of two steps: the calibration step, in which the parameters of the model are determined a priori in the laboratory setting, and the prediction step, which mimics the deployment of the device in real-world conditions. We investigate a linear model, in which response of the sensor to each gas is assumed to be additive, and a nonlinear model, which takes interference between gases into account. We find that the nonlinear model, although more complex, yields more accurate predictions. We also find that relatively few sensor readings and bias current settings are required to make reliable predictions of gas concentrations in the mixture, making our approach feasible in a variety of automotive and other technological settings.
KW - Bayesian modeling
KW - Electrochemical sensor
KW - Engine exhaust analysis
KW - Mixed-potential sensor
UR - http://www.scopus.com/inward/record.url?scp=84889063181&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84889063181&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.snb.2013.10.115
DO - https://doi.org/10.1016/j.snb.2013.10.115
M3 - Article
VL - 192
SP - 283
EP - 293
JO - Sensors and Actuators, B: Chemical
JF - Sensors and Actuators, B: Chemical
SN - 0925-4005
ER -