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dc.contributor.authorXue, Mantian
dc.contributor.authorMackin, Charles
dc.contributor.authorWeng, Wei-Hung
dc.contributor.authorZhu, Jiadi
dc.contributor.authorLuo, Yiyue
dc.contributor.authorLuo, Shao-Xiong Lennon
dc.contributor.authorLu, Ang-Yu
dc.contributor.authorHempel, Marek
dc.contributor.authorMcVay, Elaine
dc.contributor.authorKong, Jing
dc.contributor.authorPalacios, Tomás
dc.date.accessioned2022-09-19T19:08:05Z
dc.date.available2022-09-19T19:08:05Z
dc.date.issued2022-08-27
dc.identifier.urihttps://hdl.handle.net/1721.1/145510
dc.description.abstract<jats:title>Abstract</jats:title><jats:p>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.</jats:p>en_US
dc.language.isoen
dc.publisherSpringer Science and Business Media LLCen_US
dc.relation.isversionof10.1038/s41467-022-32749-4en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceNatureen_US
dc.titleIntegrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensingen_US
dc.typeArticleen_US
dc.identifier.citationXue, 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).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Chemistryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Soldier Nanotechnologiesen_US
dc.relation.journalNature Communicationsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-09-19T18:56:08Z
dspace.orderedauthorsXue, M; Mackin, C; Weng, W-H; Zhu, J; Luo, Y; Luo, S-XL; Lu, A-Y; Hempel, M; McVay, E; Kong, J; Palacios, Ten_US
dspace.date.submission2022-09-19T18:56:11Z
mit.journal.volume13en_US
mit.journal.issue1en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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