A method for estimating the responses of polymer-coated acoustic wave vapor sensors has been developed. Polymer/gas partition coefficients, determined experimentally

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.

Responses from an array of four polymer-coated surface acoustic wave sensors exposed to a series of 39 organic vapors were used to investigate sensor response models based on vapor boiling point, solubility parameters, and solvation parameters in conjunction with linear solvation energy relationships. As part of this effort, sensor response data were used to estimate the solubility parameters and solvation parameters of the sensor coatings by adaptation of methods originally developed for use with gas-liquid chromatographic retention data. Values of these parameters were found to be consistent with the structures of the coatings though in some cases different from those determined by other methods. Discrepancies were attributed to differences in the conditions used for the determinations. Sensor responses were linear over the concentration ranges examined and could be summarized using the empirically determined partition coefficient, Ke, for each vapor-coating pair. Linear correlations were found between log Ke and vapor boiling point, and the slopes of the regressions lines were similar to those expected for ideal behavior. The strength of the correlations decreased with increasing coating polarity, and it was necessary to divide the vapors into two or three broad chemical classes in order to obtain satisfactory results. Improved correlations were found by use of Hildebrand solubility parameters in a model based on regular solution theory which attempts to account for nonideal vapor-coating interactions. The use of solvation parameters in linear solvation energy relationships, however, provided the strongest correlations, with modeled K values falling within a factor of 2 of experimental values in all cases and within +/- 25% of experimental values in 83% of the cases. Application of these models to the prediction of sensor array response patterns appears promising.

Hybrid organic/inorganic polymers have been prepared incorporating fluoroalkyl-substituted bisphenol groups linked using oligosiloxane spacers. These hydrogen-bond acidic materials have glass-to-rubber transition temperatures below room temperature and are excellent sorbents for basic vapors. The physical properties such as viscosity and refractive index can be tuned by varying the length of the oligosiloxane spacers and the molecular weight. In addition, the materials are easily cross-linked to yield solid elastomers. The potential use of these materials for chemical sensing has been demonstrated by applying them to surface acoustic wave devices as thin films and detecting the hydrogen-bond basic vapor dimethyl methylphosphonate with high sensitivity. It has also been demonstrated that one of these materials with suitable viscosity and refractive index can be used to clad silica optical fibers; the cladding was applied to freshly drawn fiber using a fiber drawing tower. These fibers have potential as evanescent wave optical fiber sensors.