Sensor Will Be Useful in Airports and Other High-Risk Areas

Chemical and Environmental Engineering Professor Yushan Yan is part of a multidisciplinary team working to develop an "electronic nose" - an ultra-sensitive sensor system that is designed to quickly detect trace quantities of explosives in high-traffic high-risk security areas, such as airports.

Currently, many explosives are found by dogs and other animals with highly sensitive olfactory senses. Developing an efficient hand-held device has been a challenge because volatile explosive vapors found in large open spaces are present at low concentrations that range from parts per billion or even parts per trillion.

Yan and his team at UCR are working to develop an ultra-thin molecular sieving membrane that will be part of the hand-held sensor that lead researcher Yu Lei, an assistant professor from the University of Connecticut (UConn), is working concurrently to design.

The membrane will have pores the size of a fraction of a nanometer, (100,000 times narrower than the diameter of a human hair).

This will allow nitrogen and oxygen to pass through but will trap larger molecules, including those of explosive vapors such as TNT, on its surface, said Yan, whose research focuses on utilizing nanomaterials for advancing technologies important to alternative energy and defense needs.

“The membrane must be immune to moisture in the air because that moisture can clog the pores,” said Yan. “And it is crucial that air flows quickly through the membrane. This will make possible a compact sensor and real time detection of explosives.”

While Yan is working to develop the membrane, Lei, who received his Ph.D. in 1994 in chemical and environmental engineering from UCR, and his team at UConn will work to develop the hand-held sensor, which will subject the molecules that stay on the membrane to an array of single-walled carbon nanotube-porphyrin conjugates, which signal the presence of explosives or other volatile compounds by a change of their conductivity.

The three-year project is funded by $792,404 grant from the National Science Foundation.