In this volume, several computational methods which may be used for evaluation and selection of sensing materials have been discussed. These computational methods have ranged from first principles or de novo methods, such as those discussed in Part 1, to semi-empirical and statistical methods as discussed in Parts 2 and 3. Some chapters have focused on designing sensing materials to respond to specific analytes and some on combining sensors to create arrays to detect a suite of chemical species. Nevertheless, challenges in computational evaluation of chemical sensing materials remain. We see two principal challenges. One challenge is in refining the methods discussed to yield accurate prediction of sensor response [1, 2]; methods as presented here may certainly be used to evaluate and rank candidate materials and to determine which materials to test. The question is whether these approaches can be used to discover new materials for sensing applications and whether these approaches will be able to predict the response of known sensing materials to new analytes accurately. The second challenge is in constructing arrays, that is, selecting which combination of materials and sensor types to use in an array. In this emerging field, the question is how to select a suite of sensors for a suite of analytes and how to analyze and understand the information gathered from the array.
KeywordsSensor Response Sensor Array Hybrid Array Specific Analytes Data Analysis Algorithm
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