PFI:AIR-TT: Prototype Development and Demonstration of Milli-electrode Array (MEA) as Real-time In situ Profiling Device in Waste Treatment Systems

University Of Connecticut, Storrs CT

Investigators

Abstract

This PFI: AIR Technology Translation project focuses on translating a novel real-time in situ profiling technology to fill the need for monitoring heterogeneous waste treatment systems. The milli-electrode array (MEA) is important because waste treatment systems (e.g. wastewater, food waste) have been operated like a "black box" and monitored using "single point" probes without obtaining a whole picture of system operational status. This new technology will lead to an improved ability to monitor and control the operation of these systems and increase their commercial potential to transform waste material into valuable fuels and other products. In addition, the ability to economically produce milli-electrode arrays with multiple parameter sensing capability could lead to their application in other system monitoring situations such as other types of biochemical reactors. The project will result in a prototype development and full-scale demonstration of MEA technology. This state-of-the-art MEA sensor has following unique features: sturdy configuration with long lifetime, simple measurement procedure, low-cost durable materials, and easy deployment and replacement. These features provide the following advantages: real-time profiling of multiple parameters spatiotemporally, durable inexpensive rapid sensing, and complete datasets to reflect system operational status at lowest capital cost. This project addresses the following technology gaps as it translates from research discovery toward commercial application. First, with an anaerobic digestor (AD) as the testbed, integration of the heterogeneous datasets generated by MEA profiles into AD control and management will be explored, which will enable the adjustment of AD operational status in real-time, accommodate gradients across the whole system, and catch the early warnings of system problems before malfunction. Second, the occurrence of biofouling from particles and microorganisms in wastewater will be prevented by utilizing novel anti-fouling materials (e.g. hydrophilic film, gold and silver nanoparticles printed on mm-sized electrodes). Third, an impedance reading will be used to detect early fouling of MEA sensors. The impedance of working MEA sensors will be periodically measured and compared with the clean MEA sensors to catch the surface property changes under fouling, so that the MEA accuracy will not be sacrificed in the long-term profiling. In addition, personnel involved in this project (two doctorate students with one for MEA prototype development and the other for MEA deployment in AD systems) will receive hands on innovation entrepreneurship experiences by working with PIs and Co-PIs in customer discovery interviews, UConn School of Engineering Entrepreneur Leadership Training Course, and MEA business model canvas development. The project engages a food waste AD technology company (Quantum Biopower Inc.) to provide full-scale AD test site for MEA technology demonstration, a leading environmental biotechnology research laboratory (Li PI; University of Connecticut) and a world-class expert in sensor development (Lei CO-PI; University of Connecticut) to augment research capability in this technology translation effort from research discovery toward commercial reality.