Transparent Transfer-Free Ultrasmall Multilayer Graphene Microelectrodes Enable High Quality Recordings in Brain Slices
Nerea de Alvarez de Eulate (TU Delft - EKL Processing)
Christos Pavlou (TU Delft - Bio-Electronics)
Gonzalo León González (TU Delft - BUS/Quantum Delft)
María Camarena Pérez (TU Delft - Sports & Games, TU Delft - Bio-Electronics)
Lukas Holzapfel (Fraunhofer Institute for Reliability and Microintegration IZM)
Zhenyu Gao (Erasmus MC)
Sten Vollebregt (TU Delft - Electronic Components, Technology and Materials)
Vasiliki Giagka (Fraunhofer Institute for Reliability and Microintegration IZM, TU Delft - Bio-Electronics)
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Abstract
Resolving the underlying mechanisms of complex brain functions and associated disorders remains a major challenge in neuroscience, largely due to the difficulty in mapping large-scale neural network dynamics with high spatiotemporal resolution. Multimodal neural platforms that integrate optical and electrical modalities offer a promising approach that surpasses resolution limits. Over the last decade, transparent graphene microelectrodes have been proposed as highly suitable multimodal neural interfaces. However, their fabrication commonly relies on the manual transfer process of pre-grown graphene sheets which introduces reliability and scalability issues. In this study, multilayer graphene microelectrode arrays (MEAs) with electrode sizes as small as 10–50 µm in diameter, are fabricated using a transfer-free process on a transparent substrate for in vitro multimodal platforms. For the first time, the capability of transparent graphene electrodes with a diameter of just 10 µm to reliably capture extracellular spiking activity with high signal-to-noise ratios (up to ∼25 dB) is demonstrated. The recorded signal quality is found to be more limited by the electrode-tissue coupling than the MEA technology itself. Overall, this study shows the potential of transfer-free multilayer graphene MEAs to interface with neural tissue, paving the way to advancing neuroscientific research through the next-generation of multimodal neural interfaces.