{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/150232"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/150232","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Graphene-based Biochemical Sensing Array: Materials, System Design and Data Processing","abstract":"Graphene and other two-dimensional materials have garnered significant attention as potential biochemical and chemical sensors due to their unique physical and electrical properties. However, their use has been limited by significant device-to-device variation resulting from non-uniform synthesis and fabrication processes. To overcome this challenge, we have developed a bioelectronic sensing platform comprising thousands of integrated sensing units, custom-designed high-speed readout electronics, and machine-learning-based inference. This platform has demonstrated reconfigurable sensing capability in both the liquid and gas phases, with highly sensitive, reversible, and real-time responses to potassium, sodium, and calcium ions in complexed solutions. Additionally, using a biomimetic \"dual-monolayer\" construct, we have observed nature-like specific interactions with the CXCL12 ligand and HIV-coat glycoprotein in 100% human serum. Furthermore, the platform is capable of providing highly distinguishable fingerprints of relevant biomarkers in breath. Machine learning models trained on multi-dimensional data collected by the multiplexed sensor array is used to enhance the sensing system’s functionality. In summary, our bioelectronic sensing platform represents an end-to-end, versatile, robust, and high-performing solution for the detection of biochemical species, with potential applications in health monitoring and disease diagnosis.","abstract_html":"Graphene and other two-dimensional materials have garnered significant attention as potential biochemical and chemical sensors due to their unique physical and electrical properties. However, their use has been limited by significant device-to-device variation resulting from non-uniform synthesis and fabrication processes. To overcome this challenge, we have developed a bioelectronic sensing platform comprising thousands of integrated sensing units, custom-designed high-speed readout electronics, and machine-learning-based inference. This platform has demonstrated reconfigurable sensing capability in both the liquid and gas phases, with highly sensitive, reversible, and real-time responses to potassium, sodium, and calcium ions in complexed solutions. Additionally, using a biomimetic "dual-monolayer" construct, we have observed nature-like specific interactions with the CXCL12 ligand and HIV-coat glycoprotein in 100% human serum. Furthermore, the platform is capable of providing highly distinguishable fingerprints of relevant biomarkers in breath. Machine learning models trained on multi-dimensional data collected by the multiplexed sensor array is used to enhance the sensing system’s functionality. In summary, our bioelectronic sensing platform represents an end-to-end, versatile, robust, and high-performing solution for the detection of biochemical species, with potential applications in health monitoring and disease diagnosis.","abstract_has_math":false,"creators":["Xue, Mantian"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Palacios, Tomás"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-02","date_published":"2023-02","updated_at":"2026-07-22T22:21:12Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/150232","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Palacios, Tomás"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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However, their use has been limited by significant device-to-device variation resulting from non-uniform synthesis and fabrication processes. To overcome this challenge, we have developed a bioelectronic sensing platform comprising thousands of integrated sensing units, custom-designed high-speed readout electronics, and machine-learning-based inference. This platform has demonstrated reconfigurable sensing capability in both the liquid and gas phases, with highly sensitive, reversible, and real-time responses to potassium, sodium, and calcium ions in complexed solutions. Additionally, using a biomimetic \"dual-monolayer\" construct, we have observed nature-like specific interactions with the CXCL12 ligand and HIV-coat glycoprotein in 100% human serum. Furthermore, the platform is capable of providing highly distinguishable fingerprints of relevant biomarkers in breath. Machine learning models trained on multi-dimensional data collected by the multiplexed sensor array is used to enhance the sensing system’s functionality. In summary, our bioelectronic sensing platform represents an end-to-end, versatile, robust, and high-performing solution for the detection of biochemical species, with potential applications in health monitoring and disease diagnosis."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Graphene-based Biochemical Sensing Array: Materials, System Design and Data Processing"]}]}],"canonical_facts":{"dc:contributor.advisor":["Palacios, Tomás"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Xue, Mantian"],"dc:date.accessioned":["2023-03-31T14:41:18Z"],"dc:date.available":["2023-03-31T14:41:18Z"],"dc:date.issued":["2023-02"],"dc:description.abstract":["Graphene and other two-dimensional materials have garnered significant attention as potential biochemical and chemical sensors due to their unique physical and electrical properties. However, their use has been limited by significant device-to-device variation resulting from non-uniform synthesis and fabrication processes. To overcome this challenge, we have developed a bioelectronic sensing platform comprising thousands of integrated sensing units, custom-designed high-speed readout electronics, and machine-learning-based inference. This platform has demonstrated reconfigurable sensing capability in both the liquid and gas phases, with highly sensitive, reversible, and real-time responses to potassium, sodium, and calcium ions in complexed solutions. Additionally, using a biomimetic \"dual-monolayer\" construct, we have observed nature-like specific interactions with the CXCL12 ligand and HIV-coat glycoprotein in 100% human serum. Furthermore, the platform is capable of providing highly distinguishable fingerprints of relevant biomarkers in breath. Machine learning models trained on multi-dimensional data collected by the multiplexed sensor array is used to enhance the sensing system’s functionality. In summary, our bioelectronic sensing platform represents an end-to-end, versatile, robust, and high-performing solution for the detection of biochemical species, with potential applications in health monitoring and disease diagnosis."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/150232"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Graphene-based Biochemical Sensing Array: Materials, System Design and Data Processing"],"dc:type":["Thesis"],"thesis:degree_name":["Doctoral","Doctor of Philosophy"]},"updated_at":"2026-07-22T22:21:12Z"}