Development of Rhodamine 6G Thin Film as a Fluorescent Sensor for Explosive Vapor Detection

Mingyu Chapman, University of Rhode Island


With increasing public concern for possible future terrorist attacks involving novel explosives, there is a demand for advanced early detection technology. While trained canines are effective at detecting minute quantities of explosive vapors, canines also suffer from false positives, short attention spans, stress, expensive training, and require the assistance of an accompanying handler. Even with these disadvantageous, canines are currently more common in explosive detection due to conventional sensors. The extremely low vapor pressures at room temperature of most explosives limit the number of explosives molecules to be collected in a reasonable detection time pushes limits for most conventional sensors. Most of these sensor devices are big, as they require a vapor collection and pre-concentration system, and require time-consuming procedures. In addition, the concentration of explosive vapors decreases exponentially as a function of distance from the source, and as the function of time the explosive material is present in a location. The detection of trace quantities of explosives in the gas phase is very important in countering terrorist threats. Nanotechnology-enabled sensors could offer significant advantages over conventional sensors, such as better sensitivity and selectivity, lower production costs, reduced power consumption as well as improved stability. The purpose of this research is to provide a fundamental understanding of the materials and mechanisms required and aid in the development of small, inexpensive, effective portable sensor, which is capable for real time detecting explosives vapor at room temperature using only nature vapor pressure. (Abstract shortened by ProQuest.)

Subject Area

Analytical chemistry|Nanotechnology|Materials science

Recommended Citation

Mingyu Chapman, "Development of Rhodamine 6G Thin Film as a Fluorescent Sensor for Explosive Vapor Detection" (2017). Dissertations and Master's Theses (Campus Access). Paper AAI10271683.