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Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing

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sword-2022-09-19T18:56:09.original.xml (130 B)
Original SWORD entry document
Author(s)
Xue, Mantian
•
Mackin, Charles
•
Weng, Wei-Hung
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Zhu, Jiadi
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Luo, Yiyue
•
Luo, Shao-Xiong Lennon
•
Lu, Ang-Yu
•
Hempel, Marek
•
McVay, Elaine
•
Kong, Jing
more
Date Issued
August 27, 2022
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Xue, Mantian, Mackin, Charles, Weng, Wei-Hung, Zhu, Jiadi, Luo, Yiyue et al. 2022. "Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing." Nature Communications, 13 (1).
Version
Final published version
Abstract
AbstractTwo-dimensional materials such as graphene have shown great promise as biosensors, but suffer from large device-to-device variation due to non-uniform material synthesis and device fabrication technologies. Here, we develop a robust bioelectronic sensing platform  composed of  more than 200 integrated sensing units, custom-built high-speed readout electronics, and machine learning inference that overcomes these challenges to achieve rapid, portable, and reliable measurements. The platform demonstrates reconfigurable multi-ion electrolyte sensing capability and provides highly sensitive, reversible, and real-time response for potassium, sodium, and calcium ions in complex solutions despite variations in device performance. A calibration method leveraging the sensor redundancy and device-to-device variation is also proposed, while a machine learning model trained with multi-dimensional information collected through the multiplexed sensor array is used to enhance the sensing system’s functionality and accuracy in ion classification.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Chemistry
Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies
Terms of Use
Creative Commons Attribution 4.0 International license
https://creativecommons.org/licenses/by/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/145510
DOI of Published Version
https://doi.org/10.1038/s41467-022-32749-4
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