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

卓越研究：适体集成石墨烯-金缀合物，用于机器学习辅助农药残留筛查

• 批准号: 2100930
• 负责人: Renny Fernandez
• 金额: $ 50万
• 依托单位: Norfolk State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2100930&HistoricalAwards=false
• 关键词: Excellence; Research; Aptamer; integrated; graphene

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.

该提案的主要目标是设计用于构建比色农药传感器的石墨烯-金适体生物缀合物。设计的生物结合物将专门用于柔性基材上的喷墨印刷。比色输出将由集成相机的手持设备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色彩信息。为了证明所提出的平台的多功能性，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。通过参与该项目，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来设计传感系统，从而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系向诺福克地区高中学生和教师的推广活动中。所提出的生物缀合物基于石墨烯-金缀合物的过氧化物酶样纳米酶活性、适体的选择性和机器学习的可靠性。通过改变不同目标的适体序列，所提出的架构可以作为通用比色传感平台，提供强大且多功能的可扩展工程系统，允许测试多种农药。研究任务的产品不仅将产生用于农药分析的快速分析工具，还将产生关于适体-小分子靶点结合的前所未有的新知识。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子分开。我们将研究不同照明条件对使用 RGB、LAB 和 HSV 色彩空间的传感器色度输出的影响。机器学习方法还将提供了解实验参数（例如样品 pH 值和电导率、适体长度和 %GC）对适体-靶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，因为传感器制造基于喷墨打印和热压印等低成本技术。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。