dc.contributor.author | Xue, Mantian | |
dc.contributor.author | Mackin, Charles | |
dc.contributor.author | Weng, Wei-Hung | |
dc.contributor.author | Zhu, Jiadi | |
dc.contributor.author | Luo, Yiyue | |
dc.contributor.author | Luo, Shao-Xiong Lennon | |
dc.contributor.author | Lu, Ang-Yu | |
dc.contributor.author | Hempel, Marek | |
dc.contributor.author | McVay, Elaine | |
dc.contributor.author | Kong, Jing | |
dc.contributor.author | Palacios, Tomás | |
dc.date.accessioned | 2022-09-19T19:08:05Z | |
dc.date.available | 2022-09-19T19:08:05Z | |
dc.date.issued | 2022-08-27 | |
dc.identifier.uri | https://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.iso | en | |
dc.publisher | Springer Science and Business Media LLC | en_US |
dc.relation.isversionof | 10.1038/s41467-022-32749-4 | en_US |
dc.rights | Creative Commons Attribution 4.0 International license | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.source | Nature | en_US |
dc.title | Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing | en_US |
dc.type | Article | en_US |
dc.identifier.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). | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Chemistry | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies | en_US |
dc.relation.journal | Nature Communications | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2022-09-19T18:56:08Z | |
dspace.orderedauthors | Xue, M; Mackin, C; Weng, W-H; Zhu, J; Luo, Y; Luo, S-XL; Lu, A-Y; Hempel, M; McVay, E; Kong, J; Palacios, T | en_US |
dspace.date.submission | 2022-09-19T18:56:11Z | |
mit.journal.volume | 13 | en_US |
mit.journal.issue | 1 | en_US |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | en_US |