Optimal Coating Selection for the Analysis of Organic Vapor Mixtures with Polymer-Coated Surface Acoustic Wave Sensor Arrays

Abstract

A method for determining the optimal set of polymer sensor coatings to include in a surface acoustic wave (SAW) sensor array for the analysis of organic vapors is described. The method combines an extended disjoint principal components regression (EDPCR) pattern recognition analysis with Monte Carlo simulations of sensor responses to rank the various possible coating selections and to estimate the ability of the sensor array to identify any set of vapor analytes. A data base consisting of the calibrated responses of 10 polymer-coated SAW sensors to each of six organic solvent vapors from three chemical classes was generated to demonstrate the method. Responses to the individual vapors were linear over the concentration ranges examined, and coatings were stable over several months of operation. Responses to binary mixtures were additive functions of the individual component responses, even for vapors capable of strong hydrogen bonding. The EDPCR-Monte Carlo method was used to select the four-sensor array that provided the least error in identifying the six vapors, whether present individually or in binary mixtures. The predicted rate of vapor identification (87%) was experimentally verified, and the vapor concentrations were estimated within 10% of experimental values in most cases. The majority of errors in identification occurred when an individual vapor could not be differentiated from a mixture of the same vapor with a much lower concentration of a second component. The selection of optimal coating sets for several ternary vapor mixtures is also examined. Results demonstrate the capabilities of polymer-coated SAW sensor arrays for analyzing of solvent vapor mixtures and the advantages of the EDPCR-Monte Carlo method for predicting and optimizing performance.

Comparison study of conductometric, optical and SAW gas sensors based on porous sol–gel silica films doped with NiO and Au nanocrystals

“…An ac voltage is applied to the interdigitated transducer, thus creating an oscillating acoustic (mechanical) wave due to the piezoelectric substrate used as a positive feedback element in a closed loop circuit with an amplifier. When a continuous layer is deposited on top of this substrate, changes in the film physical properties, due for example to absorption/desorption processes, or to oxidation/reduction reactions, promote a change in the oscillator frequency [25,26]. A frequency counter was used to measure the operational frequency of the sensor, that was found to be approximately 55 MHz in dry synthetic air at a temperature of 170 • C. Fig.…”

Nanoporous-carbon adsorbers for chemical microsensors.

“…However this relatively weak response for alcohols is still significantly stronger than that for methanol with polymer-coated SAW devices which range from 0.4 -5 ppm. 24 Note that dendrimer polymer coated 98 MHz SAW devices demonstrate shifts of 5, 4, and 8 ppm for exposures to P/P sat (analyte) = 0.25 for carbon tetrachloride, benzene, and trichloroethylene, respectively. 25 This compares with responses of 1070, 698, and 622 ppm, respectively, for NPC-coated SAW devices at exposures a factor of 5 less than those used in the dendrimer study.…”

Development of Surface Acoustic Wave Electronic Nose

“…Some SAW sensor array-based electronic nose products have been launched and many more have been reported from research and development laboratories [23][24][25][26] . Having a fairly mature SAW device technology, the critical bottleneck in the performance of SAW sensor array-based electronic nose systems comes from selection of chemical selective polymer coatings and signal processing for vapour pattern recognition [27][28][29][30][31][32] . Most of the electronic components, electromechanical parts and data acquisition systems involved in the construction of an electronic nose system are commercially available.…”

“…However, selection of proper polymers requires experimenting with a large set of potentially useful polymers, either by making sensor arrays in various combinations and evaluating these for target vapour discrimination [27][28][29][30][31][32] or by alternate thermochemical characterisation such as thermogravimetric analysis, Fourier transform spectrometry, and quartz crystal microbalance-based sorption-desorption analysis 33,34 . This is quite an expensive and time-consuming process.…”