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dc.contributor.authorLi, B
dc.contributor.authorTan, H
dc.contributor.authorJenkins, David
dc.contributor.authorSrinivasa Raghavan, V
dc.contributor.authorRosa, BG
dc.contributor.authorGüder, F
dc.contributor.authorPan, Genhua
dc.contributor.authorYeatman, E
dc.contributor.authorSharp, DJ
dc.date.accessioned2020-09-11T11:22:40Z
dc.date.available2020-09-11T11:22:40Z
dc.date.issued2020-10-30
dc.identifier.issn0008-6223
dc.identifier.issn1873-3891
dc.identifier.urihttp://hdl.handle.net/10026.1/16240
dc.description.abstract

Accurate detection of blood biomarkers related to neurodegenerative diseases could provide a shortcut to identifying early stage patients before the onset of symptoms. The specificity, selectivity and operational requirements of the current technologies, however, preclude their use in the primary clinical setting for early detection. Graphene, an emerging 2D nanomaterial, is a promising candidate for biosensing which has the potential to meet the performance requirements and enable cost-effective, portable and rapid diagnosis. In this review, we compare graphene-based immunosensing technologies with conventional enzyme-linked immunosorbent assays and cutting-edge single molecule array techniques for the detection of blood-based neurodegenerative biomarkers. We cover the progress in electrical, electrochemical and optical graphene-based immunosensors and outline the barriers that slow or prevent the adoption of this emerging technology in primary clinical settings. We also highlight the possible solutions to overcome these barriers with an outlook on the future of the promising, graphene immunosensor technology.

dc.format.extent144-162
dc.languageen
dc.language.isoen
dc.publisherElsevier BV
dc.subjectGraphene
dc.subjectClinical detection
dc.subjectNeurodegenerative disease
dc.subjectProtein biomarkers
dc.subjectImmunosensor
dc.titleClinical detection of neurodegenerative blood biomarkers using graphene immunosensor
dc.typejournal-article
dc.typeReview
dc.typeJournal
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000565900900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.volume168
plymouth.publication-statusPublished
plymouth.journalCarbon
dc.identifier.doi10.1016/j.carbon.2020.06.048
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2020-06-15
dc.rights.embargodate2023-4-15
dc.identifier.eissn1873-3891
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1016/j.carbon.2020.06.048
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2020-10-30
rioxxterms.typeJournal Article/Review


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