(Invited) Quantitative Decoding of Complex Gas Mixtures for Environmental Monitoring Using Mixed-Potential Sensors
We have developed two distinct sensing platforms for NOx and NH3 detection. The NOx sensor consists of La1-xSrxCrO3 and Pt electrodes. Under open circuit conditions, this sensor responds to many of the constituents of concern in emissions monitoring, however, the selectivity may be tuned by operating conditions (3). By operating at a small positive current bias, this sensor becomes preferentially selective to NOx species. Selectivity towards non-methane hydrocarbons (NMHC) is improved by operation at elevated temperatures, however sensitivity is reduced. The NH3 sensor consists of Au/Pd and Pt electrodes and is minimally sensitive to NOxspecies with varying sensitivity to NMHCs depending on operating temperature. While these devices exhibit preferential selectivity to target analytes, a single device with absolute selectivity remains elusive. The work herein presents an alternative strategy to absolute selectivity in which the cross-sensitivity of varying sensor elements at varying operating conditions is exploited through the use of Bayesian inference predictive algorithms based on physical models of sensor-analyte interactions.
We have previously shown that the individual concentration of individual analyte species can be uniquely determined from a mixtures of two gases based on the sensor voltage response of a single sensor operating at four different current bias points by employing a Bayesian interference model(4). In this work, we seek to expand our proof-of-concept demonstration to uniquely identifying the concentration of NO, NO2, NH3, and C3H6 in complex mixtures relevant to diesel exhaust. In this study we are employing two different sensors, the aforementioned LSCr|YSZ|Pt and Au|YSZ|Pt planar sensor devices, operating at multiple temperatures. Sensor voltage response data collected on a wide range of mixing ratios are used to train the Bayesian model. Complex mixtures not used in the training set will be used to test the accuracy of the model. This research represents the first stages of developing miniaturized mixed-potential electrochemical sensor (MPES) arrays as an all-in-one NOx/NH3/NMHC sensor package that will provide quantitative emissions monitoring and onboard diagnostics (OBD) for all lean-burn engine applications. The MPES arrays have potential to be used in Environmental monitoring of complex gas mixtures. The characteristics of micro-systems that are required to realize the potential of MPES arrays will be discussed and preliminary results to realize these concepts will be presented.
The research was funded by the Los Alamos National Laboratory Directed Research Development Exploratory Research program.
1. R. Mukundan, E. L. Brosha, F. H. Garzon, US 7,575,709 (2009).
2. R. Mukundan, E. L. Brosha, F. H. Garzon, Journal of The Electrochemical Society 150, H279 (2003).
3. C. R. Kreller, P. K. Sekhar, W. Li, P. Palanisamy, E. L. Brosha, R. Mukundan, F. H. Garzon, ECS Transactions 50, 307-314 (2012).
4. J. Tsitron, C. R. Kreller, P. K. Sekhar, R. Mukundan, F. H. Garzon, E. L. Brosha, A. V. Morozov, Sensors and Actuators B: Chemical 192, 283-293 (2014).