20182D MaterialsOpen access

Graphene & two-dimensional devices for bioelectronics and neuroprosthetics

Dmitry Kireev, Andreas Offenhäusser

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Abstract

Abstract Graphene bioelectronics is a groundbreaking field which emerged roughly 8 years ago offering important opportunities for developing new kinds of sensors capable of establishing an outstanding interface with soft tissue. Graphene-based transistors, as well as electrode arrays, have emerged as a special group of biosensors with their own peculiarities, advantages and drawbacks. In this review, we show the progress of the field from single device measurements to in vivo neuroprosthetic devices. First, the general architectures of device fabrication and their implementation for extracellular recordings are discussed, along with the basic sensing mechanisms essential for their use as sensors. Then state-of-the-art approaches are introduced with a discussion of advantages and drawbacks in the design/measurement architectures. As a whole, the review highlights the results from the ever-growing discipline of graphene bioelectronics and draws reasonable conclusions for future research directions. The possibility of using other device architectures or other 2D materials such as MoS 2 and MXenes for the same goal are assessed at the end of this review in order to highlight future challenges and directions towards an efficient 2D materials-to-brain interface.

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Abstract Graphene bioelectronics is a groundbreaking field which emerged roughly 8 years ago offering important opportunities for developing new kinds of sensors capable of establishing an outstanding interface with soft tissue. Graphene-based transistors, as well as electrode arrays, have emerged as a special group of biosensors with their own peculiarities, advantages and drawbacks. In this review, we show the progress of the field from single device measurements to in vivo neuroprosthetic devices. First, the general architectures of device fabrication and their implementation for extracellular recordings are discussed, along with the basic sensing mechanisms essential for their use as sensors. Then state-of-the-art approaches are introduced with a discussion of advantages and drawbacks in the design/measurement architectures. As a whole, the review highlights the results from the ever-growing discipline of graphene bioelectronics and draws reasonable conclusions for future research directions. The possibility of using other device architectures or other 2D materials such as MoS 2 and MXenes for the same goal are assessed at the end of this review in order to highlight future challenges and directions towards an efficient 2D materials-to-brain interface.

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Available abstract

Abstract Graphene bioelectronics is a groundbreaking field which emerged roughly 8 years ago offering important opportunities for developing new kinds of sensors capable of establishing an outstanding interface with soft tissue. Graphene-based transistors, as well as electrode arrays, have emerged as a special group of biosensors with their own peculiarities, advantages and drawbacks. In this review, we show the progress of the field from single device measurements to in vivo neuroprosthetic devices. First, the general architectures of device fabrication and their implementation for extracellular recordings are discussed, along with the basic sensing mechanisms essential for their use as sensors. Then state-of-the-art approaches are introduced with a discussion of advantages and drawbacks in the design/measurement architectures. As a whole, the review highlights the results from the ever-growing discipline of graphene bioelectronics and draws reasonable conclusions for future research directions. The possibility of using other device architectures or other 2D materials such as MoS 2 and MXenes for the same goal are assessed at the end of this review in order to highlight future challenges and directions towards an efficient 2D materials-to-brain interface.

Key concepts: Bioelectronics, Neuroprosthetics, Graphene, Nanotechnology, Interface (matter), Biosensor, Computer science, Materials science

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