A new Colloquium paper published in EPJ B looks at ion irradiation techniques as a means to control the structure of nanoclusters and nanocrystals embedded in solid materials, such as silica or silicon.
New theoretical model of the effect of triangular defects in graphene provides numerical estimates of the resulting current rectification with potential applications in security screening.
Electronic transport in graphene contributes to its characteristics. Now, a Russian scientist is proposing a new theoretical approach to describe graphene with defects—in the form of artificial triangular holes—resulting in the rectification of the electric current within the material. Specifically, the study provides an analytical and numerical theory of the so-called ratchet effect —which results in a direct current under the action of an oscillating electric field, due to the skew scattering of electronic carriers by coherently oriented defects in the material. These findings are published in EPJ B by Sergei Koniakhin from the Ioffe Physical-Technical Institute and the Academic University - Nanotechnology Research and Education Centre, both affiliated with the Russian Academy of Sciences in St. Petersburg.
A new theoretical model outlines the conditions under which a novel nanostructure, such as the nano-pea pod, can exhibit localised electrons for electronics applications
Periodic chain-like nanostructures are widely used in nanoelectronics. Typically, chain elements include the likes of quantum rings, quantum dots, or quantum graphs. Such a structure enables electrons to move along the chain, in theory, indefinitely. The trouble is that some applications require localised electrons - these are no longer in a continuous energy spectrum but in a discrete energy spectrum, instead. Now, a new study by Russian scientists identifies ways of disturbing the periodicity of a model nanostructure to obtain the desired discrete spectrum with localised electrons. These findings have been published in EPJ B by Dr. Eremin from the Mordovian State University, in Saransk, Russia and colleagues.
A new study relies on a complex systems modelling approach, known as graph theory, to analyze inter-dependent physical or social networks and improve their reliability in the event of failure
Energy production systems are good examples of complex systems. Their infrastructure equipment requires ancillary sub-systems structured like a network - including water for cooling, transport to supply fuel, and ICT systems for control and management. Every step in the network chain is interconnected with a wider network and they are all mutually dependent. A team of UK-based scientists has studied various aspects of inter-network dependencies, not previously explored. The findings have been published in EPJ B by Gaihua Fu from Newcastle University, UK, and colleagues. These findings could have implications for maximising the reliability of such networks when facing natural and man-made hazards.
Another step towards faster computers relies on three coherently coupled quantum dots used as quantum information units, which could ultimately enhance quantum computers’ speed
Quantum computers have yet to materialise. Yet, scientists are making progress in devising suitable means of making such computers faster. One such approach relies on quantum dots—a kind of artificial atom, easily controlled by applying an electric field. A new study demonstrates that changing the coupling of three coherently coupled quantum dots (TQDs) with electrical impulses can help better control them. This has implications, for example, should TQDs be used as quantum information units, which would produce faster quantum computers due to the fact that they would be operated through electrical impulses. These findings have been published in EPJ B by Sahib Babaee Tooski and colleagues affiliated with both the Institute of Molecular Physics at the Polish Academy of Sciences, in Poznan, Poland, the University of Ljubljana and the Jožef Stefan Institute in Slovenia.
More efficient computational methods are urgently needed to capture condensed matter systems in simulations. Electronic structure methods, such as density-functional theory (DFT), usually provide a good compromise between accuracy and efficiency, but they demand much computational power. For this reason, they are only applicable to small systems containing a few hundred atoms at most. Conversely, many interesting phenomena involve much larger systems comprising thousands of atoms or more. Considerable effort has been invested in the development of potentials that enable simulations to run on larger system and for longer times. Typically these potentials are based on physically-motivated functional forms. Therefore, while they perform very well for the specific applications for which they have been designed, they cannot easily be transferred from one system to another. Moreover, their numerical accuracy is restricted by the intrinsic limitations of the imposed functional forms. In this EPJ B Colloquium, Handley and Behler survey several novel types of potentials emerged in recent years, which are not based on physical considerations.
All of the Ti-V alloys could display a relatively high superconducting transition temperature, as it is their unusual physical properties that influence this property, unlike previously thought
Physicists from India have shed new light on a long-unanswered question related to superconductivity in so-called transition metal binary alloys. The team revealed that the local magnetic fluctuations, or spin fluctuations, an intrinsic property of Titanium-Vanadium (Ti-V) alloys, influences superconductivity in a way that is more widespread than previously thought. They found that it is the competition between these local magnetic fluctuations and the interaction between electrons and collective excitations, referred to as phonons, which determine the superconductivity. Dr. Matin, from the Raja Ramanna Center for Advanced Technology, Indore, India, and colleagues published their findings in a study in EPJ B
Analysing the adequation of financial data structure with its expected fractal scaling could help early detection of extreme financial events because these represent a scaling irregularity
Due to their previously discovered fractal nature, financial data patterns are self-similar when scaling up. New research shows that the most extreme events in financial data dynamics—reflected in very large price moves—are incompatible with multi-fractal scaling. These findings have been published in EPJ B by physicist Elena Green from the National University of Ireland, Maynooth, Ireland and colleagues. Understanding the multi-fractal structure of financially sound markets could, ultimately, help in identifying structural signs of impending extreme events.