Modeling and Data Analysis of Conductive Polymer Composite Sensors Modeling and Data Analysis of Conductive Polymer Composite Sensors
A novel method was developed to optimize the selection of polymeric materials to be used within a chemiresistor array for anticipated samples without performing preliminary experiments. It is based on the theoretical predicted responses of chemiresistors and the criterion of minimizing the mean square error (MSE) of the chemiresistor array. After the number of chemiresistors to be used in an array and the anticipated sample chemistry are determined, the MSE values of all combinations of the candidate chemiresistors are calculated. The combination which has the minimum MSE value is the best choice. This can become computationally intensive for selection of polymers for large arrays from candidates in a large database. The number of combinations can be reduced by using the branch and bound method to save computation time. This method is suitable for samples at low concentrations where thermodynamic multi-component interactions are linear.
To help users apply this polymer selection method for the sensors, a website including 10 solvents and 10 polymers was developed. Users can specify a target sample and obtain the best set of polymers for a sensor array to detect the sample.
The activities of trichloroethylene and toluene in polyisobutylene were measured at very low concentrations. The activities for toluene are consistent with published values at higher concentrations. The values for trichloroethylene are a new contribution to the literature.
School:Brigham Young University
School Location:USA - Utah
Source Type:Master's Thesis
Keywords:chemical sensor chemiresistor array conductive polymer composite model selection method activity measurement
Date of Publication:10/20/2006