Excellence in Research: Aptamer integrated graphene-gold conjugates for machine learning aided pesticide residue screening

本提案的主要目的是设计用于构建比色农药传感器的石墨烯-金适配体生物偶联物。设计的生物偶联物将适用于柔性基板上的喷墨印刷。色度输出将由集成手持设备的相机或智能手机相机捕获。将使用机器学习算法来纠正对捕获设备和照明条件的依赖，以提取与农药水平相关的精确颜色信息。为了证明该平台的通用性，将对草甘膦、马拉硫磷、啶虫脒和毒死蜱四种农药的检测进行案例研究。作为该项目的一部分，诺福克州立大学工程系的研究生和本科生将通过应用工程、纳米技术、图像处理和机器学习的概念，在设计传感系统方面获得宝贵的多学科经验。该项目还将通过提供独立学习，暑期实习和高级顶点项目等课程机会来加强。项目展示和演示将包括在工程部门外展诺福克地区的高中学生和教师。所提出的生物偶联物是基于石墨烯-金偶联物的过氧化物酶活性，适体的选择性和机器学习的可靠性。所提出的结构可以作为一个通用的比色传感平台，通过改变不同目标的适体序列，提供一个强大的、通用的工程系统，可扩展，允许测试广泛的农药。本课题的研究成果不仅将为农药分析提供一种快速的分析工具，而且将对适配体-小分子靶标结合产生前所未有的新认识。为了准确地将色度变化与农药水平相关联，将使用机器学习方法将照度元素与色度因素分离。不同的照明条件对使用RGB、LAB和HSV色彩空间的传感器的比色输出的影响将被研究。机器学习方法还将提供理解实验参数（如样品pH和电导率、适体长度和%GC）对适体-目标亲和力的影响所需的方法框架。开发的平台将能够测量结合动力学，监测适体的平衡亲和力，并调整非特异性相互作用的存在。此外，该技术具有很高的商业潜力，因为传感器的制造是基于低成本的技术，如喷墨印刷和热压印。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The main objective of this proposal is to design graphene-gold-aptamer bioconjugates for building colorimetric pesticide sensors. The bioconjugates designed will be tailored for inkjet printing on flexible substrates. The colorimetric output will be captured by a camera integrated handheld device or a smartphone camera. Dependency on capture device and illumination conditions will be corrected using a machine learning algorithm to extract the precise chromatic information that correlate to pesticide levels. In order to prove the versatility of the proposed platform, a case study on detection of four pesticides namely Glyphosate, Malathion, Acetamiprid, and Chlorpyrifos will be performed. By being part of this project, graduate and undergraduate students of the Department of Engineering, Norfolk State University, will gain valuable multidisciplinary experience in designing sensing systems by applying concepts of engineering, nanotechnology, image processing and machine learning. This project will also be enhanced by offering curricular opportunities such as independent study, summer internships, and senior capstone projects. Project displays and demonstrations will be included in Engineering Department outreach to Norfolk area high school students and teachers. The proposed bioconjugate is based on the peroxidase like nanozyme activity of the Graphene-gold conjugate, selectivity of the aptamer and reliability of machine learning. The proposed architecture can serve as a universal colorimetric sensing platform by changing the aptamer sequence for different targets, providing a powerful and versatile engineered system which is scalable, allowing testing of a wide range of pesticides. Products of the research tasks will not only result in a rapid analysis tool for pesticide analysis but will also yield unprecedented new knowledge on aptamer-small molecule target binding. In order to accurately correlate chrominance changes to pesticide levels, the illuminance element will be separated from the chrominance factor using a machine learning approach. The effect of varying illumination conditions on the colorimetric output of the sensor using RGB, LAB and HSV color spaces will be investigated. The machine learning approach will also provide the methodological framework needed to understand the influence of experimental parameters such as sample pH and conductivity, aptamer length, and %GC on aptamer-target affinity. The platform developed will be capable of measuring binding kinetics, monitoring equilibrium affinities of aptamers and adjusting for the presence of nonspecific interactions. Moreover, this technology has high commercial potential as the sensor fabrication is based on low-cost techniques like inkjet printing and thermal embossing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.