(186v) Nanowire Organic Sensor for Ammonia Detection | AIChE

(186v) Nanowire Organic Sensor for Ammonia Detection


Kilani, M. - Presenter, Wayne State University
Yu, X., Wayne State University
Mao, G., Wayne State University
Ammonia is one of the largest produced chemicals with a global production of 150 million tons in 2017, expected to increase 8% in the next 4 years. It is widely used in several industries mainly in fertilizers manufacturing, but also in refrigeration systems, production of plastics, and as intermediates for pharmaceuticals synthesis. However, ammonia is highly corrosive and can damage cells in the body in contact, posing serious health risks. Despite the developments made in the detection systems, ammonia is still one of the most dangerous substances, responsible for the second highest number of chemical exposure casualties. The current detection solutions have serious limitations to be broadly used in industrial safety. Detection systems can generally fall under two categories, high-end equipment that is expensive, not user-friendly and can be only used in lab settings, and affordable devices that is prone to false alarms in a varying environment (humidity, temperature, etc.).

In this presentation, we will describe our Nanowire Organic Sensor technology as an alternative solution platform for ammonia monitoring that is affordable and can provide real-time and wide-area coverage. Our patented technology is a solution-based, room-temperature process, which facilitates scalable manufacturing directly on substrates and devices. Compared with the existing nanowire sensor fabrication methods, out innovation has four main unfair advantages. 1) By direct deposit of a solution droplet, nanowires are created right on chips or micro-fabricated devices. 2) It is a room temperature fabrication process, thus enabling less complex and capital-intensive manufacturing process. 3) It is a modular method that enables combinatory synthesis of a wide range of new organic nanowires. 4) It is a fabrication technology compatible with flexible electronics platforms. We will show how the combination of the sensor arrays, impedance spectroscopy and artificial neural networks model enhances the selectivity in varying levels of humidity. We will also describe how the technology is compatible with the internet of things technology and cloud services, and how the combination could be leveraged to ensure more flexible monitoring of ammonia for industrial safety.