Recording extracellular potentials from electrogenic cells (especially neurons) is the hallmark destination of modern bioelectronics. Graphene is a promising material, which possesses features relevant to bioelectronics applications.Graphene-based electrode arrays (GMEAs) and more complicated graphene field effect transistors (GFETs) comprise a new type of bioelectronic device application. Biocompatibility, stability, excellent and unique electronic properties, scalability, and pure two-dimensional structure make graphene the perfect material for bioelectronic applications. The advantages of graphene as part of such devices are numerous: from a general flexibility and biocompatibility to the unique electronic properties of graphene.In this work, the GMEAs and GFETs are fabricated using CVD--grown graphene and a scalable cleanroom-based technology. The devices are fabricated on both rigid and flexible substrates.In order to ensure a wafer-scale fabrication of the devices, a new high throughput graphene transfer technique is established. The technique allows me to use just 4 cm2 of CVD-grown graphene to fabricate a whole 4-inch wafer with 52 chips on it.Rigid GFETs, fabricated on different substrates, with a variety of channel geometries (width/length), reveal a linear relation between the transconductance and the width/length ratio. The area normalized electrolyte-gated transconductance is in the range of 1-2 mS·V-1·□, and does not strongly depend on the substrate. Influence of the ionic strength on the transistor performance is investigated as a part of the work. Double contacts are found to decrease the effective resistance and the transfer length, but do not improve the transconductance. An electrochemical annealing/cleaning effect is investigated and proposed to originate from the out-of-plane gate leakage current. The devices are used as a proof-of-concept for bioelectronic sensors, recording external potentials from ex vivo heart tissue and in vitro cardiomyocyte-like cells (HL-1). Via multichannel measurements we are able to record and analyze both difference in action potentials as well as their spatial propagation through the chip. The recordings show distinguishable action potentials with a signal to noise ratio over 14 from ex vivo tissue and over 6 from the cardiac-like cell line in vitro. Furthermore, I accomplished in vitro recordings of neuronal signals with a distinguishable bursting activity for the first time.Flexible GFETs are fabricated on polyimide substrates and exhibit extremely large transconductance values, up to 11 mS/V, and a mobility over 1750 cm2·V-1·s-1. Furthermore, controllably flexible polyimide-on-steel (PIonS) substrates are able to record ex vivo electrical signals from a primary embryonic rat heart tissue.Rigid GMEAs are used for extensive in vitro studies of a cardiac-like cell line and cortical neuronal networks. They show excellent ability to extracellularly record the action potentials with signal to noise ratios up to 116 for HL-1 cells and up to 100 for the spontaneous bursting-spiking neuronal activity. Complex neuronal bursting activity patterns as well as a variety of HL-1 action potentials are recorded with the GMEAs.Flexible GMEAs show extracellular recordings from ex vivo heart tissue with excellent signal-to-noise ratios up to 80 and from in vitro HL-1 cells with SNR up to 30. The use of flexible polyimide substrates in combination with graphene`s physical and biological stability results in good cell-interface properties and is promising for further applications. Due to the transparency of these devices, the concept can be extended for optogenetic experiments.Furthermore, a new fabrication design and flow has been explored in the thesis, aimed for prospective, more specific in vivo probes and their use as bio-implants.

Dissertation, RWTH Aachen University, 2017; Jülich : Forschungszentrum Jülich GmbH, Zentralbibliothek, Schriften des Forschungszentrums Jülich. Reihe Schlüsseltechnologien / Key Technologies 155, 1 Online-Ressource (ix, 169 Seiten) : Illustrationen, Diagramme (2017). doi:10.18154/RWTH-2017-09706 = Dissertation, RWTH Aachen University, 2017

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