Sue Minkoff: Modeling trace-gas sensors to increase efficiency
Compact, portable trace-gas sensors are finding applications including disease diagnosis via breath analysis, monitoring of atmospheric pollutants and greenhouse gas emissions, and early-warning systems for homeland security applications. One such sensor is based on optothermal detection and uses a modulated laser source and a quartz tuning-fork amplifier to detect trace gases. Modeling and design optimization can reduce the cost associated with trying to maximize the performance of novel trace gas sensors.
Determining an optimally-designed sensor requires maximizing the signal as a function of the geometry of the quartz tuning fork. An optimal tuning fork constrained to resonate at a frequency close to the standard 32.8-kHz tuning fork leads to a signal that is three times larger than the one obtained with the current experimental design. Moreover, the optimal tuning fork found when dropping the resonance frequency constraint produces a signal that is 24 times greater than that produced by the current sensor.
Sue Minkoff is a Professor of Mathematical Sciences and an Affiliated Professor in the Department of Geosciences at the University of Texas at Dallas. Her research interests include geoscience modeling and photonics. She received her PhD in Computational and Applied Mathematics from Rice University in 1995. From 1995-1997 she was an NSF-Industrial Postdoc at the University of Texas at Austin and British Petroleum, and from 1997-2000 she held a von Neumann Fellowship in the Mathematics Department at Sandia National Laboratories in Albuquerque, New Mexico. In 2000 she was promoted to Senior Member of the Technical Staff in the Geophysics Department at Sandia. From 2000-2012 she served on the faculty in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County. At UMBC she was a part of MIRTHE (Mid-Infrared Technologies for Health and the Environment), a National Science Foundation Engineering Research Center.