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The first step in combating a chemical weapons threat is contamination avoidance. This is accomplished by the detection and identification of chemical agents. The Air Force has several instruments to detect chemical vapors, but is always looking for lighter, faster, and more accurate technology for a better capability. This research is focused on using carbon nanotube polymer composite sensors for chemical detection. More specifically, models are developed to classify three sets of sensor data according to vapor using various multivariate techniques. Also, prediction models of a mixed sensor output are developed using neural networks and regression analysis. The classifiers developed are able to accurately classify three vapors for a specific set of data, but have problems when tested against data from aged sensors as well as data generated from a different set of new sensors. These results indicate that further research should be conducted to ensure accuracy in identifying chemical vapors using these types of sensors.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
