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 optoelectronic
s 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.
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. Man
y 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 MPS(T&B) (Maximum Protein Similarity Topological and Biological), a new algorithm that leverages both topological and biological information to predict protein-protein interactions. We comprehensively compare MPS(T) and MPS(T&B) with state-of-the-art approaches on reliable PPIs datasets, showing that they have competitive or higher accuracy on biologically validated test sets. Conclusion: MPS(T) and MPS(T&B) are topological only and topological plus sequence-based computational methods that can effectively predict the entire human interactome.
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 th
e 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.
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 stud
y 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.
