Ethanol Solvation of Polymer Residues in Graphene Solution-Gated Field Effect Transistors.

The persistence of photoresist residues from microfabrication procedures causes significant obstacles in the technological advancement of graphene-based electronic devices. These residues induce undesired chemical doping effects, diminish carrier mobility, and deteriorate the signal-to-noise ratio, making them critical in certain contexts, including sensing and electrical recording applications. In graphene solution-gated field-effect transistors (gSGFETs), the presence of polymer contaminants makes it difficult to perform precise electrical measurements, introducing response variability and calibration challenges. Given the absence of viable short to midterm alternatives to polymer-intensive microfabrication techniques, a postpatterning treatment involving THF and ethanol solvents was evaluated, with ethanol being the most effective, environmentally sustainable, and safe method for residue removal. Employing a comprehensive analysis with XPS, AFM, and Raman spectroscopy, together with electrical characterization, we investigated the influence of residual polymers on graphene surface properties and transistor functionality. Ethanol treatment exhibited a pronounced enhancement in gSGFET performance, as evidenced by a shift in the charge neutrality point and reduced dispersion. This systematic cleaning methodology holds the potential to improve the reproducibility and precision in the manufacturing of graphene devices. Particularly, by using ethanol for residue removal, we align our methodology with the principles of green chemistry, minimizing environmental impact while advancing diverse graphene technology applications.

Full-bandwidth electrophysiology of seizures and epileptiform activity enabled by flexible graphene microtransistor depth neural probes.

Mapping the entire frequency bandwidth of brain electrophysiological signals is of paramount importance for understanding physiological and pathological states. The ability to record simultaneously DC-shifts, infraslow oscillations (<0.1 Hz), typical local field potentials (0.1-80 Hz) and higher frequencies (80-600 Hz) using the same recording site would particularly benefit preclinical epilepsy research and could provide clinical biomarkers for improved seizure onset zone delineation. However, commonly used metal microelectrode technology suffers from instabilities that hamper the high fidelity of DC-coupled recordings, which are needed to access signals of very low frequency. In this study we used flexible graphene depth neural probes (gDNPs), consisting of a linear array of graphene microtransistors, to concurrently record DC-shifts and high-frequency neuronal activity in awake rodents. We show here that gDNPs can reliably record and map with high spatial resolution seizures, pre-ictal DC-shifts and seizure-associated spreading depolarizations together with higher frequencies through the cortical laminae to the hippocampus in a mouse model of chemically induced seizures. Moreover, we demonstrate the functionality of chronically implanted devices over 10 weeks by recording with high fidelity spontaneous spike-wave discharges and associated infraslow oscillations in a rat model of absence epilepsy. Altogether, our work highlights the suitability of this technology for in vivo electrophysiology research, and in particular epilepsy research, by allowing stable and chronic DC-coupled recordings.

Understanding the bias dependence of low frequency noise in single layer graphene FETs.

This letter investigates the bias-dependent low frequency noise of single layer graphene field-effect transistors. Noise measurements have been conducted with electrolyte-gated graphene transistors covering a wide range of gate and drain bias conditions for different channel lengths. A new analytical model that accounts for the propagation of the local noise sources in the channel to the terminal currents and voltages is proposed in this paper to investigate the noise bias dependence. Carrier number and mobility fluctuations are considered as the main causes of low frequency noise and the way these mechanisms contribute to the bias dependence of the noise is analyzed in this work. Typically, normalized low frequency noise in graphene devices has been usually shown to follow an M-shape dependence versus gate voltage with the minimum near the charge neutrality point (CNP). Our work reveals for the first time the strong correlation between this gate dependence and the residual charge which is relevant in the vicinity of this specific bias point. We discuss how charge inhomogeneity in the graphene channel at higher drain voltages can contribute to low frequency noise; thus, channel regions nearby the source and drain terminals are found to dominate the total noise for gate biases close to the CNP. The excellent agreement between the experimental data and the predictions of the analytical model at all bias conditions confirms that the two fundamental 1/f noise mechanisms, carrier number and mobility fluctuations, must be considered simultaneously to properly understand the low frequency noise in graphene FETs. The proposed analytical compact model can be easily implemented and integrated in circuit simulators, which can be of high importance for graphene based circuits' design.