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

这个PFI: AIR技术翻译项目的重点是翻译一种新的实时原位分析技术，以满足监测异质废物处理系统的需求。毫电极阵列（MEA）很重要，因为废物处理系统（例如废水、食物垃圾）一直像“黑匣子”一样运行，并使用“单点”探针进行监测，而无法获得系统运行状态的全貌。这项新技术将提高监测和控制这些系统运行的能力，并增加其将废料转化为有价值的燃料和其他产品的商业潜力。此外，经济地生产具有多参数传感能力的毫微米电极阵列的能力可能导致它们在其他系统监测情况下的应用，如其他类型的生化反应器。该项目将导致MEA技术的原型开发和全面演示。这款最先进的MEA传感器具有以下独特功能：坚固的配置，使用寿命长，测量程序简单，低成本耐用材料，易于部署和更换。这些特点提供了以下优势：多个参数的时空实时分析，持久廉价的快速传感，以及以最低资本成本反映系统运行状态的完整数据集。该项目解决了以下技术差距，因为它从研究发现转化为商业应用。首先，以厌氧消化器（AD）为试验平台，探索将MEA配置文件生成的异构数据集集成到厌氧消化器控制和管理中，实现对厌氧消化器运行状态的实时调整，适应整个系统的梯度，并在系统发生故障前及时发现系统问题的预警。其次，利用新型防污材料（如亲水膜、印在毫米尺寸电极上的金、银纳米颗粒）可以防止废水中颗粒和微生物产生生物污染。第三，阻抗读数将用于检测MEA传感器的早期污垢。定期测量工作的MEA传感器的阻抗，并与清洁的MEA传感器进行比较，以捕捉污垢下表面特性的变化，从而在长期分析中不牺牲MEA的精度。此外，参与该项目的人员（两名博士生，一名负责MEA原型开发，另一名负责MEA在AD系统中的部署）将通过在客户发现访谈、康涅狄格大学工程学院企业家领导力培训课程和MEA商业模型画布开发中与pi和co - pi合作，获得创新创业经验。该项目聘请了一家食品垃圾AD技术公司（Quantum Biopower Inc.）为MEA技术演示提供全面的AD测试场地，一家领先的环境生物技术研究实验室（李皮；康涅狄格大学）和一位世界级的传感器开发专家（雷CO-PI；康涅狄格大学）来增强这项技术从研究发现到商业现实的研究能力。

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.