Session: MNS-04-01: Micro/Nano IoT, Sensors, Digital Computing and Power

Paper Number: 90015

90015 - Novel MEMS Capacitive Sensor Excited at Electrical Resonance for Detecting Helium Based on Changes in Air Electrical Properties 

Dry cask storage (DCS) is increasingly used for extended long-term storage of spent nuclear fuel. The canister is vacuum dried and then pressurized with helium to ensure an inert atmosphere and efficient heat transfer. Thus, helium leakage detection plays an important part in ensuring the safety of nuclear waste storage facilities. However, as it is a noble light gas with no order or color, using conventional gas sensing techniques for detecting Helium is a challenge.  To overcome this challenge, in this paper we present the working principle of a simple electrostatic MEMS structure to detect Helium in ambient air.  The working principle of this novel sensor is based on the decrease in the air die-electric constant due to the presence of Helium. While this change is small, we show that activating the MEMS RLC circuit can significantly amplify the sensor response and hence increase its sensitivity. The sensor response is simulated at different Helium concentration levels using a finite element model. The simulations showed that an electrostatic MEMS sensor operating close to the electrical resonance frequency of an RLC circuit showed different deflection and as such a different capacitance at different levels of helium. A decrease up to 20% of the MEMS deflection was observed at 20% Helium concentration compared to near 0% concentration.

Presenting Author: Fadi Alsaleem University of Nebraska - Lincoln

Presenting Author Biography: Dr. Alsaleem joined the college of engineering at the University of Nebraska at Lincoln (UNL) in August 2016. Prior to this assignment, he worked for multiple years in the industry including four years as a Senior Lead Algorithm Engineer at Emerson Electric Inc to develop novel (cloud-based) sensor monitoring and learning algorithms used for fault diagnostics for mechanical systems. His current and future potential research goals are to vertically advance the fields of intelligent wearable sensing technologies and artificial intelligence algorithms and their use in many health and medical applications. In this research area, he has more 10 awarded patents, more than 100 publications, presentations, and invited talks, and over 6 million total (near 1.5 million to his own research team) of active funding to support his research work.

Authors:

Sulaiman Mohaidat University of Nebraska Lincoln
Fadi Alsaleem University of Nebraska - Lincoln