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Multiplexing Detection of Extracellular and Intracellular Toxins in Water by GaN Field Effect Transistors

Objective

Cyanobacterial blooms, also known as harmful algal blooms have been known to produce a variety of harmful toxins including microcystins, saxitoxins, anatoxin-a, and cylindrospermopsins. These blooms have become a major concern when it comes to water safety and management as they can have severe impacts on public health, aquatic ecosystems, and local economies. Current methods for toxin detection include enzyme-linked immunosorbent assay, polymerase chain reaction, high performance liquid chromatography, and liquid chromatography-mass spectrometry. These methods require complex operation, fluorophore labeling, sophisticated and bulk instruments, and skilled personnel. Additionally, these methods take days for analysis, cannot be easily performed in the field, and cannot determine multiple toxins at the same time. Therefore simple and low cost detection technologies that can meet this challenge are critically needed in recognition of the importance of water quality monitoring and environmental protection. The development of novel sensors for detection of toxins in water can revolutionize our understanding of aquatic ecosystem functions governing water quality at the watershed scale. For example, it can be accomplished, in part, by assembling a portfolio of novel sensors for on-site real time multiplexing monitoring of the biotic and abiotic compartments within the water cycle. The pervasive networking of these sensors will enable process monitoring at spatial and temporal scales that are much higher than is currently feasible. Integrating these data streams into simulation models of the water cycle will deepen our understanding of these processes, facilitate the development of real-time indices to forecast events such as algal blooms, and identify strategies that can be followed to avert such events. The outcome of the research will be integrated in the outreach activities include working with industry for validation, field testing in Lake Erie, publicizing the technology through annual "Media Day" at OSU Stone Lab, "Nanobiotechnology Summer Camp" and "Adopting a Class" from local school students. <br/><br/>The objective of this project is to develop GaN (gallium nitride) based immunologically field effect transistors biosensors for detection of extracellular and intracellular cyanotoxins in water and environmental samples. The proposed devices are capable of label-free multiplexing detection rapidly (in minutes) by both immuno-sensing and nucleic acid detection as necessary. These sensor devices can be fabricated in a large array system with each device using distinct antibodies, so that a full profile of contaminants can be achieved by a single low cost test. Due to the fact that these devices can be fabricated at very small sizes and monitored at the individual device level, each sensing array can detect potential toxins at extremely low concentrations. Although this kind of biosensors have been demonstrated for other biosensing applications, their application to cyanotoxin detection and monitoring has yet to be demonstrated. The team choses AlGaN (aluminum gallium nitride)/GaN heterojunction semiconductor structures as the device platform because this material system is chemically inert and stable so a high signal-to-noise ratio can be achieved. The proposed toxin sensors can meet the following requirements: (1) high sensitivity or low detection limit and large dynamic range; (2) high specificity and multiplexing detection; (3) rapid detection and short analysis time; (4) label free; and (5) field-portability and easy-of-use, capable of detection of both extracellular and intracellular toxins, and forming a sensing network for long term environmental monitoring.<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.

Investigators
Wu Lu
Institution
Ohio State University
Start date
2018
End date
2021
Project number
1809570
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