Recording infraslow brain signals (<0.1 Hz) with microelectrodes is severely hampered by current microelectrode materials, primarily due to limitations resulting from voltage drift and high electrode impedance. Hence, most recording systems include high-pass filters that solve saturation issues but come hand in hand with loss of physiological and pathological information. In this work, we use flexible epicortical and intracortical arrays of graphene solution-gated field-effect transistors (gSGFETs) to map cortical spreading depression in rats and demonstrate that gSGFETs are able to record, with high fidelity, infraslow signals together with signals in the typical local field potential bandwidth. The wide recording bandwidth results from the direct field-effect coupling of the active transistor, in contrast to standard passive electrodes, as well as from the electrochemical inertness of graphene. Taking advantage of such functionality, we envision broad applications of gSGFET technology for monitoring infraslow brain activity both in research and in the clinic.

One of the critical factors determining the performance of neural interfaces is the electrode material used to establish electrical communication with the neural tissue, which needs to meet strict electrical, electrochemical, mechanical, biological and microfabrication compatibility requirements. This work presents a nanoporous graphene-based thin-film technology and its engineering to form flexible neural interfaces. The developed technology allows the fabrication of small microelectrodes (25 µm diameter) while achieving low impedance (∼25 kΩ) and high charge injection (3–5 mC cm⁻²). In vivo brain recording performance assessed in rodents reveals high-fidelity recordings (signal-to-noise ratio >10 dB for local field potentials), while stimulation performance assessed with an intrafascicular implant demonstrates low current thresholds (0.8) for activating subsets of axons within the rat sciatic nerve innervating tibialis anterior and plantar interosseous muscles. Furthermore, the tissue biocompatibility of the devices was validated by chronic epicortical (12 week) and intraneural (8 week) implantation. This work describes a graphene-based thin-film microelectrode technology and demonstrates its potential for high-precision and high-resolution neural interfacing.

The demand for real-time monitoring of cell functions and cell conditions has dramatically increased with the emergence of organ-on-a-chip (OOC) systems. However, the incorporation of co-cultures and microfluidic channels in OOC systems increases their biological complexity and therefore makes the analysis and monitoring of analytical parameters inside the device more difficult. In this work, we present an approach to integrate multiple sensors in an extremely thin, porous and delicate membrane inside a liver-on-a-chip device. Specifically, three electrochemical dissolved oxygen (DO) sensors were inkjet-printed along the microfluidic channel allowing local online monitoring of oxygen concentrations. This approach demonstrates the existence of an oxygen gradient up to 17.5% for rat hepatocytes and 32.5% for human hepatocytes along the bottom channel. Such gradients are considered crucial for the appearance of zonation of the liver. Inkjet printing (IJP) was the selected technology as it allows drop on demand material deposition compatible with delicate substrates, as used in this study, which cannot withstand temperatures higher than 130 °C. For the deposition of uniform gold and silver conductive inks on the porous membrane, a primer layer using SU-8 dielectric material was used to seal the porosity of the membrane at defined areas, with the aim of building a uniform sensor device. As a proof-of-concept, experiments with cell cultures of primary human and rat hepatocytes were performed, and oxygen consumption rate was stimulated with carbonyl-cyanide-4-(trifluoromethoxy)phenylhydrazone (FCCP), accelerating the basal respiration of 0.23 ± 0.07 nmol s⁻¹/10⁶ cells up to 5.95 ± 0.67 nmol s⁻¹/10⁶ cells s for rat cells and the basal respiration of 0.17 ± 0.10 nmol s⁻¹/10⁶ cells by up to 10.62 ± 1.15 nmol s⁻¹/10⁶ cells for human cells, with higher oxygen consumption of the cells seeded at the outflow zone. These results demonstrate that the approach of printing sensors inside an OOC has tremendous potential because IJP is a feasible technique for the integration of different sensors for evaluating metabolic activity of cells, and overcomes one of the major challenges still remaining on how to tap the full potential of OOC systems.

Brain–computer interfaces and neural prostheses based on the detection of electrocorticography (ECoG) signals are rapidly growing fields of research. Several technologies are currently competing to be the first to reach the market; however, none of them fulfill yet all the requirements of the ideal interface with neurons. Thanks to its biocompatibility, low dimensionality, mechanical flexibility, and electronic properties, graphene is one of the most promising material candidates for neural interfacing. After discussing the operation of graphene solution‐gated field‐effect transistors (SGFET) and characterizing their performance in saline solution, it is reported here that this technology is suitable for μ‐ECoG recordings through studies of spontaneous slow‐wave activity, sensory‐evoked responses on the visual and auditory cortices, and synchronous activity in a rat model of epilepsy. An in‐depth comparison of the signal‐to‐noise ratio of graphene SGFETs with that of platinum black electrodes confirms that graphene SGFET technology is approaching the performance of state‐of‐the art neural technologies.

Cortical slow oscillations (SO) of neural activity spontaneously emerge and propagate during deep sleep and anesthesia and are also expressed in isolated brain slices and cortical slabs. We lack full understanding of how SO integrate the different structural levels underlying local excitability of cell assemblies and their mutual interaction. Here, we focus on ongoing slow waves (SWs) in cortical slices reconstructed from a 16-electrode array designed to probe the neuronal activity at multiple spatial scales. In spite of the variable propagation patterns observed, we reproducibly found a smooth strip of loci leading the SW fronts, overlapping cortical layers 4 and 5, along which Up states were the longest and displayed the highest firing rate. Propagation modes were uncorrelated in time, signaling a memoryless generation of SWs. All these features could be modeled by a multimodular large-scale network of spiking neurons with a specific balance between local and intermodular connectivity. Modules work as relaxation oscillators with a weakly stable Down state and a peak of local excitability to model layers 4 and 5. These conditions allow for both optimal sensitivity to the network structure and richness of propagation modes, both of which are potential substrates for dynamic flexibility in more general contexts.