Sensing ion channels in neuronal networks with graphene transistors
Farida Veliev, Dipankar Kalita, Antoine Bourrier, Tiphaine Belloir,, Anne Brian\c{c}on-Marjollet, Mireille Albrieux, Vincent Bouchiat, C\'ecile, Delacour

TL;DR
This paper demonstrates the use of liquid-gated graphene transistors to detect single ion channel activity in hippocampal neurons, leveraging grain boundary sensitivity for high-resolution bioelectronic sensing.
Contribution
It introduces a novel application of CVD graphene grain boundary-enhanced G-FETs for real-time, nanoscale detection of neuronal ion channels.
Findings
Successful detection of single ion channel activity in neurons
Grain boundaries significantly improve graphene transistor sensitivity
Drug dependence and gating effects observed in ion channel signals
Abstract
Graphene, the atomically-thin honeycomb carbon lattice, is a highly conducting 2D material whose exposed electronic structure offers an ideal platform for sensing. Its biocompatible, flexible, and chemically inert nature associated to the lack of dangling bonds, offers novel perspectives for direct interfacing with bioelements. When combined with its exceptional electronic and optical properties, graphene becomes a very promising material for bioelectronics. Among the successful bio-integrations of graphene, the detection of ionic currents through artificial membrane channels and extracellular action potentials in electrogenic cells have paved the road for the high spatial resolution and wide-field imaging of neuronal activity. However, various issues including the low signals amplitude, confinement and stochasticity of neuronal signals associated to the complex architecture and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNeuroscience and Neural Engineering · Graphene research and applications · Advanced Memory and Neural Computing
