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

Mantian Xue (), Charles Mackin, Wei-Hung Weng, Jiadi Zhu, Yiyue Luo, Shao-Xiong Lennon Luo, Ang-Yu Lu, Marek Hempel, Elaine McVay, Jing Kong and Tomás Palacios ()
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Mantian Xue: Massachusetts Institute of Technology
Charles Mackin: IBM Research–Almaden
Wei-Hung Weng: Massachusetts Institute of Technology
Jiadi Zhu: Massachusetts Institute of Technology
Yiyue Luo: Massachusetts Institute of Technology
Shao-Xiong Lennon Luo: Massachusetts Institute of Technology
Ang-Yu Lu: Massachusetts Institute of Technology
Marek Hempel: Massachusetts Institute of Technology
Elaine McVay: Massachusetts Institute of Technology
Jing Kong: Massachusetts Institute of Technology
Tomás Palacios: Massachusetts Institute of Technology

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract Two-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.

Date: 2022
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DOI: 10.1038/s41467-022-32749-4

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