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Graphene-hexagonal boron nitride (hBN) scalable heterostructures are pivotal for the development of graphene-based high-tech applications. In this work, we demonstrate the realization of high-quality graphene-hBN heterostructures entirely obtained with scalable approaches. hBN continuous films were grown via ion beam-assisted physical vapor deposition directly on commercially available $SiO_2/Si$ and used as receiving substrates for graphene single-crystal matrixes grown by chemical vapor deposition on copper. The structural, chemical, and electronic properties of the heterostructure were investigated by atomic force microscopy, Raman spectroscopy, and electrical transport measurements. We demonstrate graphene carrier mobilities exceeding $10,000 cm^2/Vs$ in ambient conditions, 30% higher than those directly measured on $SiO_{2}/Si$. We prove the scalability of our approach by measuring more than 100 transfer length method devices over a centimeter scale, which present an average carrier mobility of $7500 \pm 850 cm^{2}/Vs$. The reported high-quality all-scalable heterostructures are of relevance for the development of graphene-based high-performing electronic and optoelectronic applications.

Motivation: Over the past decade, network-based approaches have proven useful in identifying disease modules within the human interactome, often providing insights into key mechanisms and guiding the quest for therapeutic targets. This is all the more important, since experimental investigation of potential gene candidates is an expensive task, thus not always a feasible option. On the other hand, many sources of biological information exist beyond the interactome and an important research direction is the design of effective techniques for their integration. Results: In this work, we introduce the Biological Random Walks (BRW) approach for disease gene prioritization in the human interactome. The proposed framework leverages multiple biological sources within an integrated framework. We perform an extensive, comparative study of BRW's performance against well-established baselines. Availability and implementation: All code is publicly available and can be downloaded at \url{https://github.com/LeoM93/BiologicalRandomWalks}. We used publicly available datasets, details on their retrieval and preprocessing are provided in the supplementary material.

Motivation: Genome-wide association studies (GWAS) have successfully identified thousands of genetic risk loci for complex traits and diseases. Most of these GWAS loci lie in regulatory regions of the genome and the gene through which each GWAS risk locus exerts its effects is not always clear. Many computational methods utilizing biological data sources have been proposed to identify putative casual genes at GWAS loci; however, these methods can be improved upon. Results: We present the Relations-Maximization Method, a dense module searching method to identify putative causal genes at GWAS loci through the generation of candidate sub-networks derived by integrating association signals from GWAS data into the gene co-regulation network. We employ our method in a chronic obstructive pulmonary disease GWAS. We perform an extensive, comparative study of Relations-Maximization Method's performance against well-established baselines.

Copper (Cu) is the electrical conductor of choice in many categories of electrical wiring, with household and building installations being the major market of this metal. This work demonstrates the coating of Cu wires - with diameters relevant for low voltage (LV) applications - with graphene. The chemical vapor deposition (CVD) coating process is rapid, safe, scalable and industrially compatible. Graphene-coated Cu wires display oxidation resistance and increased electrical conductivity (up to 1% immediately after coating and up to 3% after 24 months), allowing for wire diameter reduction and thus significant savings in wire production costs. Combined spectroscopic and diffraction analysis indicate that the conductivity increase is due to a change in Cu crystallinity, induced by the coating process conditions, while electrical testing of aged wires shows that graphene plays a major role in maintaining improved electrical performances over long periods of time. Finally, graphene coating of Cu wires using an ambient pressure roll-to-roll (R2R) CVD reactor is demonstrated. This enables the in-line production of graphene-coated metallic wires as required for industrial scale-up., Comment: 13 pages, 5 figures

Graphene grown via chemical vapour deposition (CVD) on copper foil has emerged as a high-quality, scalable material, that can be easily integrated on technologically relevant platforms to develop promising applications in the fields of optoelectronics and photonics. Most of these applications require low-contaminated high-mobility graphene (i.e., approaching 10 000 $cm^2 V^{-1} s^{-1}$) at room temperature) to reduce device losses and implement compact device design. To date, these mobility values are only obtained when suspending or encapsulating graphene. Here, we demonstrate a rapid, facile, and scalable cleaning process, that yields high-mobility graphene directly on the most common technologically relevant substrate: silicon dioxide on silicon (SiO$_2$/Si). Atomic force microscopy (AFM) and spatially-resolved X-ray photoelectron spectroscopy (XPS) demonstrate that this approach is instrumental to rapidly eliminate most of the polymeric residues which remain on graphene after transfer and fabrication and that have adverse effects on its electrical properties. Raman measurements show a significant reduction of graphene doping and strain. Transport measurements of 50 Hall bars (HBs) yield hole mobility ${\mu}_h$ up to 9000 $cm^2 V^{-1} s^{-1}$ and electron mobility ${\mu}_e$ up to 8000 $cm^2 V^{-1} s^{-1}$, with average values ${\mu}_h$ 7500 $cm^2 V^{-1} s^{-1}$ and ${\mu}_e$ 6300 $cm^2 V^{-1} s^{-1}$. The carrier mobility of ultraclean graphene reach values nearly double of that measured in graphene HBs processed with acetone cleaning, which is the method widely adopted in the field. Notably, these mobility values are obtained over large-scale and without encapsulation, thus paving the way to the adoption of graphene in optoelectronics and photonics., Comment: 20 pages, 11 figures

Background:Typically, proteins perform key biological functions by interacting with each other. As a consequence, predicting which protein pairs interact is a fundamental problem. Experimental methods are slow, expensive, and may be error prone.Many computational methods have been proposed to identify candidate interacting pairs. When accurate, they can serve as an inexpensive, preliminary filtering stage, to be followed by downstream experimental validation. Among such methods, sequence-based ones are very promising.Results:We present, a new algorithm that leverages both topological and biological information to predict protein-protein interactions. We comprehensively compare our Framework with state-of-the-art approaches on reliable PPIs datasets, showing that they have competitive or higher accuracy on biologically validated test sets.Conclusion:We shown that topological plus sequence-based computational methods can effectively predict the entire human interactome compared with methods that leverage only one source of biological information.

We report compact, scalable, high-performance, waveguide integrated graphene-based photodetectors (GPDs) for telecom and datacom applications, not affected by dark current. To exploit the photothermoelectric (PTE) effect, our devices rely on a graphene-polymer-graphene stack with static top split gates. The polymeric dielectric, poly(vinyl alcohol) (PVA), allows us to preserve graphene quality and to generate a controllable p-n junction. Both graphene layers are fabricated using aligned single-crystal graphene arrays grown by chemical vapor deposition. The use of PVA yields a low charge inhomogeneity 8 x 10$^{10}$ $cm^{-2}$ at the charge neutrality point, and a large Seebeck coefficient 140 ${\mu}$V K$^{-1}$, enhancing the PTE effect. Our devices are the fastest GPDs operating with zero dark current, showing a flat frequency response up to 67 GHz without roll-off. This performance is achieved on a passive, low-cost, photonic platform, and does not rely on nanoscale plasmonic structures. This, combined with scalability and ease of integration, makes our GPDs a promising building block for next-generation optical communication devices., Comment: 15 pages, 11 figures. Published under ACS AuthorChoice license

This work proposes a unified framework to leverage biological information in network propagation-based gene prioritization algorithms. Preliminary results on breast cancer data show significant improvements over state-of-the-art baselines, such as the prioritization of genes that are not identified as potential candidates by interactome-based algorithms, but that appear to be involved in/or potentially related to breast cancer, according to a functional analysis based on recent literature.

The combination of the exciting properties of graphene with those of monolayer tungsten disulfide (WS2) makes this heterostack of great interest for electronic, optoelectronic and spintronic applications. The scalable synthesis of graphene/WS2 heterostructures on technologically attractive substrates like SiO2 would greatly facilitate the implementation of novel two-dimensional (2D) devices. In this work, we report the direct growth of monolayer WS2 via chemical vapor deposition (CVD) on single-crystal graphene arrays on SiO2. Remarkably, spectroscopic and microscopic characterization reveals that WS2 grows only on top of the graphene crystals so that the vertical heterostack is selectively obtained in a bottom-up fashion. Spectroscopic characterization indicates that, after WS2 synthesis, graphene undergoes compressive strain and hole doping. Tailored experiments show that such hole doping is caused by the modification of the SiO2 stoichiometry at the graphene/SiO2 interface during the WS2 growth. Electrical transport measurements reveal that the heterostructure behaves like an electron-blocking layer at large positive gate voltage, which makes it a suitable candidate for the development of unipolar optoelectronic components., Comment: 30 pages, main text and supplementary information

Graphene nanoribbons (GNRs) are a novel and intriguing class of materials in the field of nanoelectronics, since their properties, solely defined by their width and edge type, are controllable with high precision directly from synthesis. Here we study the correlation between the GNR structure and the corresponding device electrical properties. We investigated a series of field effect devices consisting of a film of armchair GNRs with different structures (namely width and/or length) as the transistor channel, contacted with narrowly spaced graphene sheets as the source-drain electrodes. By analyzing several tens of junctions for each individual GNR type, we observe that the values of the output current display a width-dependent behavior, indicating electronic bandgaps in good agreement with the predicted theoretical values. These results provide insights into the link between the ribbon structure and the device properties, which are fundamental for the development of GNR-based electronics., Comment: Published in Carbon (2019)