2026-04-13T02:50:35Zhttps://recercat.cat/oai/request
oai:recercat.cat:2072/4558622024-11-18T01:36:00Zcom_2072_98col_2072_378192
RECERCAT author Kostarelos, Kostas author Vincent, Melissa author Hébert, Clément author Garrido, Jose 2017 Altres ajuts: the ICN2 team is supported by the CERCA Programme/Generalitat de Catalunya. C.H. received funding from the P-SPHERE COFUND - Horizon 2020 Marie Sklodowska-Curie Actions.Neural interfaces are becoming a powerful toolkit for clinical interventions requiring stimulation and/or recording of the electrical activity of the nervous system. Active implantable devices offer a promising approach for the treatment of various diseases affecting the central or peripheral nervous systems by electrically stimulating different neuronal structures. All currently used neural interface devices are designed to perform a single function: either record activity or electrically stimulate tissue. Because of their electrical and electrochemical performance and their suitability for integration into flexible devices, graphene-based materials constitute a versatile platform that could help address many of the current challenges in neural interface design. Here, how graphene and other 2D materials possess an array of properties that can enable enhanced functional capabilities for neural interfaces is illustrated. It is emphasized that the technological challenges are similar for all alternative types of materials used in the engineering of neural interface devices, each offering a unique set of advantages and limitations. Graphene and 2D materials can indeed play a commanding role in the efforts toward wider clinical adoption of bioelectronics and electroceuticals. open access Aquest material està protegit per drets d'autor i/o drets afins. Podeu utilitzar aquest material en funció del que permet la legislació de drets d'autor i drets afins d'aplicació al vostre cas. Per a d'altres usos heu d'obtenir permís del(s) titular(s) de drets. https://rightsstatements.org/vocab/InC/1.0/ Graphene in the design and engineering of next-generation neural interfaces Article