SBIR Phase II: Sensory System for Autonomous Area-Wide Disease and Agriterror Detection and Reporting

这项SBIR第二阶段研究项目将使用微电子机械系统(MEMS)技术制造纳米传感阵列。这种生物分子友好型导电聚合物的电化学聚合被开发并成功地用于构建功能强大、高度可靠的纳米传感器。这项研究将解决自动化制造抗体功能化导电纳米线的关键技术挑战，这些纳米线可单独寻址并可扩展到高密度生物传感器阵列，用于检测黄龙冰(HLB)。由此产生的纳米传感阵列将为开发小型、有效、廉价、具有现场价值、自主和自动化的病原体检测设备奠定基础。这些单位将允许无人值守处理大量现场样本，从而增加数据收集的时间和地理密度，提供卓越的病原体和农业恐怖检测。目前的疾病管理技术通常缺乏避免流行病所需的数据收集技术；诊断仪器不适合无人值守的自主操作。目前使用的设备速度慢、价格高、体积大，而且必须与人类交互。因此，在引起广泛的疾病或流行病之前，只检测到很少的病原体引入。这项研究将提高检测植物病原体和病原体的效率，允许采取预防性而不是危机或补救控制行动。这个自动化系统的开发可以减轻农业病虫害造成的约3000亿美元的损失。

This SBIR Phase II research project will fabricate nanosensory arrays using the Micro-Electro-Mechanical Systems (MEMS) technique. This electrochemical polymerization of biomolecule-friendly conducting polymers was developed and successfully tested to build functional, highly reliable nanosensors. This research will address key technical challenges in automating the fabrication of antibody-functionalized conducting nanowires that are individually addressable and scalable to high-density biosensor arrays for the detection of Huanglongbing (HLB). The resultant nanosensory-arrays will form the base for the development of small, effective, inexpensive, field worthy, autonomous and automated pathogen detection devices. These units will permit the unattended processing of a large number of field samples, thus increasing the temporal and geographical density of data collection, providing superior pathogen and agri-terror detection.Current disease management techniques typically lack the data collection technologies needed to avert epidemics; diagnostic instruments are not amenable to unattended autonomous operation. Devices currently used are slow, expensive, bulky, and must interface with humans. Consequently, only few pathogen introductions are detected before causing widespread disease or epidemics. This research will increase the efficiency in detection of plant pathogens and agents of disease, allowing for preventative rather than crisis or remedial control actions. The development of this automated system can mitigate the estimated $300 billion loss due to agricultural pests.