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Carbon nanotubes in neural interfacing applications

dc.contributor.authorVoge, Christopher Men_US
dc.contributor.authorStegemann, Jan P.en_US
dc.date.accessioned2012-04-06T20:59:03Z
dc.date.available2012-04-06T20:59:03Z
dc.date.issued2011en_US
dc.identifier.citationVoge, Christopher M; Stegemann, Jan P (2011). "Carbon nanotubes in neural interfacing applications." Journal of Neural Engineering, vol. 8, 1, 011001. <http://hdl.handle.net/2027.42/90821>en_US
dc.identifier.urihttp://stacks.iop.org/1741-2552/8/i=1/a=011001en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/90821
dc.description.abstractCarbon nanotubes (CNT) are remarkable materials with a simple and inert molecular structure that gives rise to a range of potentially valuable physical and electronic properties, including high aspect ratio, high mechanical strength and excellent electrical conductivity. This review summarizes recent research on the application of CNT-based materials to study and control cells of the nervous system. It includes the use of CNT as cell culture substrates, to create patterned surfaces and to study cell–matrix interactions. It also summarizes recent investigations of CNT toxicity, particularly as related to neural cells. The application of CNT-based materials to directing the differentiation of progenitor and stem cells toward neural lineages is also discussed. The emphasis is on how CNT surface chemistry and nanotopography can be altered, and how such changes can affect neural cell function. This knowledge can be applied to creating improved neural interfaces and devices, as well as providing new approaches to neural tissue engineering and regeneration.en_US
dc.publisherIOP Publishingen_US
dc.titleCarbon nanotubes in neural interfacing applicationsen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelPhysicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/90821/1/1741-2552_8_1_011001.pdf
dc.identifier.doi10.1088/1741-2552-8-1-011001en_US
dc.identifier.sourceJournal of Neural Engineeringen_US
dc.owningcollnamePhysics, Department of


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