相关论文: A Novel Interface Database of Graphene Nanoribbon …
A simple model based on the divide and conquer rule and tight-binding (TB) approximation is employed for studying the role of finite size effect on the electronic properties of elongated graphene nanoribbon (GNR) heterojunctions. In our…
Many objects of nanoscopic dimensions involve fluid-tethered chain interfaces. These systems are of interest for basic science and for several applications, in particular for design of nanodevices for specific purposes. We review recent…
Many of today's most interesting questions involve understanding and interpreting complex relationships within graph-based structures. For instance, in materials science, predicting material properties often relies on analyzing the…
New technologies are necessary for the unprecedented expansion of connectivity and communications in the modern technological society. The specific needs of wireless communication systems in 5G and beyond, as well as devices for the future…
Understanding the role of microscopic attributes in nanocomposites allows for a controlled and, therefore, acceleration in experimental system designs. In this work, we extracted the relevant parameters controlling the graphene oxide…
We present a machine learning method for swiftly identifying nanobubbles in graphene, crucial for understanding electronic transport in graphene-based devices. Nanobubbles cause local strain, impacting graphene's transport properties.…
Successful material selection is critical in designing and manufacturing products for design automation. Designers leverage their knowledge and experience to create high-quality designs by selecting the most appropriate materials through…
Molecular-level understanding of the interactions between the constituents of an atomic structure is essential for designing novel materials in various applications. This need goes beyond the basic knowledge of the number and types of…
Signal processing is crucial for satisfying the high data rate requirements of future sixth-generation (6G) wireless networks. However, the rapid growth of wireless networks has brought about massive data traffic, which hinders the…
Relational databases store much of the world's structured information, and they are essential for driving complex predictive applications. However, deep learning progress on relational data remains limited, as conventional approaches…
The recent introduction of Graph Neural Networks (GNNs) and their growing popularity in the past few years has enabled the application of deep learning algorithms to non-Euclidean, graph-structured data. GNNs have achieved state-of-the-art…
This paper reviews the theoretical work undertaken using density functional theory (DFT) to explore graphene's interactions with its surroundings. We look at the impact of substrates, gate dielectrics and edge effects on the properties of…
High quality graphene nanoribbons (GNRs) grown by on-surface synthesis strategies with atomic precision can be controllably doped by inserting heteroatoms or chemical groups in the molecular precursors. Here, we study the electronic…
Accurately simulating physics is crucial across scientific domains, with applications spanning from robotics to materials science. While traditional mesh-based simulations are precise, they are often computationally expensive and require…
Two-dimensional graphene, carbon nanotubes and graphene nanoribbons represent a novel class of low dimensional materials that could serve as building blocks for future carbon-based nanoelectronics. Although these systems share a similar…
A lot of technological advances depend on next-generation materials, such as graphene, which enables a raft of new applications, for example better electronics. Manufacturing such materials is often difficult; in particular, producing…
Among their amazing properties, graphene and related low-dimensional materials show quantized charge-density fluctuations--known as plasmons--when exposed to photons or electrons of suitable energies. Graphene nanoribbons offer an enhanced…
Information Filtering Networks (IFNs) provide a powerful framework for modeling complex systems through globally sparse yet locally dense and interpretable structures that capture multivariate dependencies. This review offers a…
The mechanical integrity of composite materials depends primarily on the interface strength and the defect density of the reinforcement which is the provider of enhanced strength and stiffness. In the case of graphene/ polymer…
The intercalation of different species in graphene-metal interfaces is widely used to stabilise the artificial phases of different materials. However, formation of the surface alloys upon the guest-metal intercalation is still an open…