Orthogonal gas sensor arrays by chemoresistive material design

AbstractGas sensor arrays often lack discrimination power to different analytes and robustness to interferants, limiting their success outside of research laboratories. This is primarily due to the widely sensitive (thus weakly-selective) nature of the constituent sensors. Here, the effect of orthogonality on array accuracy and precision by selective sensor design is investigated. Therefore, arrays of (2–5) selective and non-selective sensors are formed by systematically altering array size and composition. Their performance is evaluated with 60 random combinations of ammonia, acetone and ethanol at ppb to low ppm concentrations. Best analyte predictions with high coefficients of determination (R2) of 0.96 for ammonia, 0.99 for acetone and 0.88 for ethanol are obtained with an array featuring high degree of orthogonality. This is achieved by using distinctly selective sensors (Si:MoO3 for ammonia and Si:WO3 for acetone together with Si:SnO2) that improve discrimination power and stability of the regression coefficients. On the other hand, arrays with collinear sensors (Pd:SnO2, Pt:SnO2 and Si:SnO2) hardly improve gas predictions having R2 of 0.01, 0.86 and 0.28 for ammonia, acetone and ethanol, respectively. Sometimes they even exhibited lower coefficient of determination than single sensors as a Si:MoO3 sensor alone predicts ammonia better with a R2 of 0.68. Graphical abstractConventional arrays (red) with weakly-selective sensors span a significantly smaller volume in the analyte space than arrays containing distinctly-selective sensors (orthogonal array, green). Orthogonal arrays feature better accuracy and precision than conventional arrays in mixtures of ammonia, acetone and ethanol.

[1]  N. L. Johnson,et al.  Multivariate Analysis , 1958, Nature.

[2]  Ingemar Lundström,et al.  Artificial neural networks and gas sensor arrays: quantification of individual components in a gas mixture , 1991 .

[3]  Sotiris E Pratsinis,et al.  Sniffing Entrapped Humans with Sensor Arrays , 2018, Analytical chemistry.

[4]  Osvaldo N. Oliveira,et al.  A review on chemiresistive room temperature gas sensors based on metal oxide nanostructures, graphene and 2D transition metal dichalcogenides , 2018, Microchimica Acta.

[5]  Nicolae Barsan,et al.  Direct formation of highly porous gas-sensing films by in situ thermophoretic deposition of flame-made Pt/SnO2 nanoparticles , 2006 .

[6]  Sotiris E. Pratsinis,et al.  Selective sensing of NH3 by Si-doped α-MoO3 for breath analysis , 2016 .

[7]  P. Španěl,et al.  Volatile metabolites in the exhaled breath of healthy volunteers: their levels and distributions , 2007, Journal of breath research.

[8]  Toshio Itoh,et al.  Development of an Exhaled Breath Monitoring System with Semiconductive Gas Sensors, a Gas Condenser Unit, and Gas Chromatograph Columns , 2016, Sensors.

[9]  N. Bârsan,et al.  Electronic nose: current status and future trends. , 2008, Chemical reviews.

[10]  Sotiris E Pratsinis,et al.  Highly Selective and Rapid Breath Isoprene Sensing Enabled by Activated Alumina Filter. , 2018, ACS sensors.

[11]  R. Kubinec,et al.  Analysis of volatile organic compounds in the breath of patients with stable or acute exacerbation of chronic obstructive pulmonary disease , 2018, Journal of breath research.

[12]  Gary King,et al.  How Not to Lie with Statistics: Avoiding Common Mistakes in Quantitative Political Science , 1986 .

[13]  David Smith,et al.  Time variation of ammonia, acetone, isoprene and ethanol in breath: a quantitative SIFT-MS study over 30 days. , 2003, Physiological measurement.

[14]  Evor L. Hines,et al.  Enhancing electronic nose performance by sensor selection using a new integer-based genetic algorithm approach , 2005 .

[15]  Alexander G. Fung,et al.  An Easy to Manufacture Micro Gas Preconcentrator for Chemical Sensing Applications. , 2017, ACS sensors.

[16]  Noriane A. Sievi,et al.  Noninvasive Body Fat Burn Monitoring from Exhaled Acetone with Si-doped WO3-sensing Nanoparticles. , 2017, Analytical chemistry.

[17]  Michael Phillips,et al.  Can the electronic nose really sniff out lung cancer? , 2005, American journal of respiratory and critical care medicine.

[18]  H. Troy Nagle,et al.  Handbook of Machine Olfaction: Electronic Nose Technology , 2003 .

[19]  Paul Geladi,et al.  Chemometric analysis of multisensor arrays , 1986 .

[20]  J. Gardner A diffusion-reaction model of electrical conduction in tin oxide gas sensors , 1989 .

[21]  Sotiris E. Pratsinis,et al.  Selective sensing of isoprene by Ti-doped ZnO for breath diagnostics. , 2016, Journal of materials chemistry. B.

[22]  Raed A. Dweik,et al.  Can the Electronic Nose Really Sniff out Lung Cancer , 2005 .

[23]  P. Španěl,et al.  Quantitative analysis of ammonia on the breath of patients in end-stage renal failure. , 1997, Kidney international.

[24]  Sotiris E Pratsinis,et al.  Breath acetone monitoring by portable Si:WO3 gas sensors. , 2012, Analytica chimica acta.

[25]  David J. Olive Linear Regression , 2019, Machine Learning and Big Data with kdb+/q.

[26]  Sotiris E. Pratsinis,et al.  Zeolite membranes for highly selective formaldehyde sensors , 2018 .

[27]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[28]  K. Persaud,et al.  Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose , 1982, Nature.

[29]  S. Pratsinis,et al.  Optimal Doping for Enhanced SnO2 Sensitivity and Thermal Stability , 2008 .

[30]  P. T. Moseley,et al.  Tin oxide based gas sensors , 1987 .

[31]  H. T. Nagle,et al.  Handbook of Machine Olfaction , 2002 .

[32]  J. A. Ragazzo‐Sánchez,et al.  Electronic nose discrimination of aroma compounds in alcoholised solutions , 2006 .

[33]  Joseph C Anderson Measuring breath acetone for monitoring fat loss: Review , 2015, Obesity.

[34]  Sotiris E. Pratsinis,et al.  Aerosol-based technologies in nanoscale manufacturing: from functional materials to devices through core chemical engineering , 2010 .

[35]  Kiran Chikkadi,et al.  E-Nose Sensing of Low-ppb Formaldehyde in Gas Mixtures at High Relative Humidity for Breath Screening of Lung Cancer? , 2016 .

[36]  E. Steyerberg,et al.  [Regression modeling strategies]. , 2011, Revista espanola de cardiologia.

[37]  Nicolae Barsan,et al.  Sensing low concentrations of CO using flame-spray-made Pt/SnO2 nanoparticles , 2006 .