Electronic Noses

Electronic Nose
Eric Nallon is a recent graduate of the PhD program offered by the Electrical and Computer Engineering department at George Mason University (GMU). Eric went to Penn State for his undergraduate degree in Electrical Engineering where he was introduced to semiconductors and cleanroom work for the first time. This experience opened up a whole new world of nanoscale electronics to him. He later started his Masters degree and met Dr. Qiliang Li at GMU. Around this time he also began working on using organic polymers and organic semiconductors as materials in chemical sensors. “At this point all I knew was "regular semiconductors" and never knew about the organic type”, says Eric. This work later led to his research on graphene-based chemical sensors for applications in an electronic nose and has been attracting a lot of attention in the field of sensors and actuators.

An electronic nose (e-nose) is a biologically inspired device designed to mimic the operation of the olfactory system. The e-nose utilizes a chemical sensor array consisting of broadly responsive vapor sensors, whose combined response produces a unique pattern for a given compound or mixture. The sensor array is inspired by the biological function of the receptor neurons found in the human olfactory system. The use of an e-nose is an attractive approach to predict unknown odors and is used in many fields for quantitative and qualitative analysis. If properly designed, an e-nose has the potential to adapt to new odors it was not originally designed for through laboratory training and algorithm updates. This would eliminate the lengthy and costly R&D costs associated with material and product development. Although e-nose technology has been around for over two decades, much research is still being undertaken in order to find new and more diverse types of sensors.

The sensing material used throughout his research is Graphene, a single-layer, 2D material comprised of carbon atoms arranged in a hexagonal lattice, with extraordinary electrical, mechanical, thermal and optical properties due to its 2D, sp2-bonded structure. Graphene has much potential as a chemical sensing material due to its 2D structure, which provides a surface entirely exposed to its surrounding environment. Graphene has gained much attention since its discovery in 2004, but has not been realized in many commercial electronics.

In Eric’s PhD research work, graphene is incorporated into a chemiresistor device and used as a chemical sensor, where its resistance is temporarily modified while exposed to chemical compounds. The sensor exhibits excellent selectivity and is capable of achieving high classification accuracies. At the culmination of his work a first example of a graphene-based, cross-reactive chemical sensor array was demonstrated by applying various polymers as coatings over an array of graphene sensors. The sensor array was tested against a variety of compounds, including solvents, chemical warfare agents, explosive related compounds and the complex odor of essential oils, hot sauces, and Scotch Whiskies.

Another subject covered in Eric’s dissertation is the use of machine learning techniques to perform pattern recognition and classification of sensor data. The sensor array is the hardware and separate from the software piece, which is where machine learning and pattern recognition become crucial. Eric used Python and its machine learning libraries throughout his work to process, analyze, establish patterns, and visualize the sensor array data.

Eric’s work was also featured in C&EN (Chemical and Engineering News) in November 2015 where his graphene sensing work published in ACS Sensor’s inaugural issue was highlighted. Since graduating Eric has left his position at the US Army NVESD and no longer works on graphene sensing (bittersweet). However, his experience with sensor data processing and machine learning helped him secure his current job as a Data Scientist at Commonwealth Computer Research, Inc in Charlottesville, VA.