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Flexible Graphene Solution‐Gated Field‐Effect Transistors: Efficient Transducers for Micro‐Electrocorticography
Author(s) -
Hébert Clement,
MasvidalCodina Eduard,
SuarezPerez Alejandro,
Calia Andrea Bonaccini,
Piret Gaelle,
GarciaCortadella Ramon,
Illa Xavi,
Del Corro Garcia Elena,
De la Cruz Sanchez Jose M.,
Casals Damia Viana,
PratsAlfonso Elisabet,
Bousquet Jessica,
Godig Philippe,
Yvert Blaise,
Villa Rosa,
SanchezVives Maria V.,
GuimeràBrunet Anton,
Garrido Jose A.
Publication year - 2018
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.201703976
Subject(s) - electrocorticography , graphene , interfacing , materials science , field effect transistor , computer science , local field potential , transducer , nanotechnology , transistor , voltage , neuroscience , electrical engineering , electroencephalography , computer hardware , biology , engineering
Abstract Brain–computer interfaces and neural prostheses based on the detection of electrocorticography (ECoG) signals are rapidly growing fields of research. Several technologies are currently competing to be the first to reach the market; however, none of them fulfill yet all the requirements of the ideal interface with neurons. Thanks to its biocompatibility, low dimensionality, mechanical flexibility, and electronic properties, graphene is one of the most promising material candidates for neural interfacing. After discussing the operation of graphene solution‐gated field‐effect transistors (SGFET) and characterizing their performance in saline solution, it is reported here that this technology is suitable for μ‐ECoG recordings through studies of spontaneous slow‐wave activity, sensory‐evoked responses on the visual and auditory cortices, and synchronous activity in a rat model of epilepsy. An in‐depth comparison of the signal‐to‐noise ratio of graphene SGFETs with that of platinum black electrodes confirms that graphene SGFET technology is approaching the performance of state‐of‐the art neural technologies.