PNNL

Abstract

A new method of processing multivariate response data to extract chemical information has been developed. Sensor array response patterns are transformed into a vector containing values for descriptors of the detected vapor's properties. These results can be obtained by using a method similar to classical least squares, and equations have been derived for mass-transducing sensors or volume-transducing sensors. Polymer-coated acoustic wave devices are an example of mass-tranducing sensors. However, some acoustic wave sensors, such as polymer-coated surface acoustic wave (SAW) devices give responses resulting from both mass-loading and decreases in modulus. The latter effect can be modeled as a volume effect. In this paper, we derive solutions for obtaining descriptor values from arrays of mass-plus-volume-transducing sensors, and perform simulations to investigate the effectiveness of these solutions and the approximations used for cases having no closed form solutions. The results show that good estimations of vapor descriptors can be obtained by using a solution that is equivalent to the solution for mass-transducing sensor arrays, but with greater sensitivities due to the volume contribution. The most challenging case occurs when the volume sensitivities in the array vary widely and especially if polymers with similar interactive properties have very different volume sensitivities. Estimates from this most challenging case be improved using a nonlinear least squares optimization method. It is concluded that this new method of processing sensor array data can be applied to SAW sensor arrays even when the modulus changes contribute to the responses. The results also suggest ways to design SAW arrays to obtain the best results, either by minimizing the volume sensitivity or matching the volume sensitivities in the array.