NSF Award
2100930
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. <br/><br/>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.<br/><br/>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.
