Accession Number:

ADA445184

Title:

Classification Characteristics of Carbon Nanotube Polymer Composite Chemical Vapor Detectors

Descriptive Note:

Master's thesis

Corporate Author:

AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH

Personal Author(s):

Report Date:

2006-03-01

Pagination or Media Count:

169.0

Abstract:

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.

Subject Categories:

  • Industrial Chemistry and Chemical Processing
  • Polymer Chemistry
  • Miscellaneous Detection and Detectors

Distribution Statement:

APPROVED FOR PUBLIC RELEASE