EPJ B Highlight - Unzipping graphene nanotubes into nanoribbons
- Published on 30 May 2018
New study shows elegant mathematical solution to understand how the flow of electrons changes when carbon nanotubes turn into zigzag nanoribbons
In a new study published in EPJ B, Basant Lal Sharma from the Indian Institute of Technology Kanpur provides a detailed analysis of how the flow of heat and electrons is affected at the interface between an ‘armchair’ shaped carbon nanotube and a zigzagging nanoribbon made up of a single-layer carbon honeycomb sheet of graphene. Applications of this method can help us understand the propagation of electrons and thermal flow in graphene and similar materials for electromagnetic devices. For example, a partially unzipped carbon nanotube could act as a device with varying electrical resistance depending on the strength of an external magnetic field applied to it. By contrast, these junctions can also act as perfect ‘valley filters’, allowing certain types of electrons through the junction with the maximum possible conductance, while other electrons can't pass through.
EPJ B Highlight - Physicists with green fingers estimate tree spanning rate in random networks
- Published on 22 May 2018
A team in China has just calculated the size of scale-free and small-world networks
Networks are often described as trees with spanning branches. How the tree branches out depends on the logic behind the network’s expansion, such as random expansion. However, some aspects of such randomly expanding networks are invariant; in other words, they display the same characteristics, regardless of the network’s scale. As a result, the entire network has the same shape as one or more of its parts. In a new study published in EPJ B, Fei Ma from Northwest Normal University in Lanzhou, Gansu Province, China, anc colleagues calculate the total number of spanning trees in randomly expanding networks. This method can be applied to modelling scale-free network models, which, as it turns out, are characterised by small-world properties. This means, for instance, that members of the network only exhibit six degrees of separation, like most people in our society.
EPJ B Highlight - Turning graphene into light nanosensors
- Published on 25 April 2018
Tuning the graphene embedded in a photonic crystal by varying the external temperature can transform it into a light-sensitive sensor
Graphene has many properties; it is e.g. an extremely good conductor. But it does not absorb light very well. To remedy this limiting aspect of what is an otherwise amazing material, physicists resort to embedding a sheet of graphene in a flat photonic crystal, which is excellent for controlling the flow of light. The combination endows graphene with substantially enhanced light-absorbing capabilities. In a new study published in EPJ B, Arezou Rashidi and Abdolrahman Namdar from the University of Tabriz, Iran demonstrate that, by altering the temperature in such a hybrid cavity structure, they can tune its capacity for optical absorption. They explain that it is the thermal expansion and thermo-optical effects which give the graphene these optical characteristics. Potential applications include light sensors, ultra-fast lasers, and systems capable of modulating incoming optical beams.
EPJ B Highlight - High-energy ions’ movement affected by silicon crystal periodicity
- Published on 21 March 2018
Thinnest-ever silicon crystal enhances ion channelling performance in particle accelerators
The thinner the silicon crystal, the better. Indeed, thinner crystals provide better ways to manipulate the trajectories of very high-energy ions in particle accelerators. Further applications include materials analysis, semiconductor doping and beam transport in large particle accelerators. All of these rely on our understanding of how positively-charged high-energy particles move through crystals. This process, called ion channelling, is the focus of a new paper by Mallikarjuna Motapothula and Mark Breese working at the National University of Singapore. In a paper published in EPJ B, the authors study how the crystal periodicity affects the motion of ions whose energy belongs to a 1 to 2 MeV range, as they are transmitted through very thin crystals on the order of a few hundred nanometres, and how it impacts their angular distribution.
EPJ B Highlight - Predicting influencers has just been made simpler
- Published on 26 January 2018
Understanding the dynamics of message transmission in networks leads to identification of key individuals spreading news and viruses in epidemics
Social networks, such as Twitter, thrive on key influencers spreading news. Like information, epidemics also spread from key individuals. To identify the most influential actors in such networks, many studies have, until now, focused on ranking the influence of individual nodes. But these methods are not accurate enough to single out influential spreaders because they fail to take into account the spreading dynamics. Now, Byungjoon Min from the Institute of Interdisciplinary Physics and Complex Systems, Balearic Island University, Palma de Mallorca, Spain, has calculated for the first time the expected size of epidemic outbreaks when spreading originates from a single seed. In a study published in EPJ B, Min accurately predicts the influence of spreaders in such networks. Applications include viral marketing, efficient immunisation strategies, and identifying the most influential actors in our society.
EPJ B Highlight - Better mastery of heat flow leads to next-generation thermal cloaks
- Published on 22 November 2017
Chinese physicists manipulate the transfer of thermal energy as a means of reducing heat waste, using thermal camouflage tactics
Ever heard of the invisibility cloak? It manipulates how light travels along the cloak to conceal an object placed behind it. Similarly, the thermal cloak is designed to hide heated objects from infrared detectors without distorting the temperature outside the cloak. Materials for such cloaks would need to offer zero thermal conductivity to help camouflage the heat. Now, Liujun Xu and colleagues from Fudan University, Shanghai, China, have explored a new mechanism for designing such materials. These findings published in EPJ B could have implications for manipulating the transfer of thermal energy as a way to ultimately reduce heat waste from fossil fuels and help mitigate energy crises.
EPJ B Highlight - ID microstructure of stock useful in financial crisis
- Published on 22 November 2017
New study of the trading interactions that determine the stock price using AI algorithms reveals unexpected microstructure for stock evolution, useful for financial crash modeling
Every day, thousands of orders for selling or buying stocks are registered and processed within milliseconds. Electronic stock exchanges, such as NASDAQ, use what is referred to as microscopic modelling of the order flow - reflecting the dynamics of order bookings - to facilitate trading. The study of such market microstructures is a relatively new research field focusing on the trading interactions that determine the stock price. Now, a German team from the University of Duisburg-Essen has analysed the statistical regularities and irregularities in the recent order flow of 96 different NASDAQ stocks. Since prices are strongly correlated during financial crises, they evolve in a way that is similar to what happens to nerve signals during epileptic seizures. The findings of the Duisburg-Essen group, published in EPJ B, contribute to modelling price evolution, and could ultimately be used to evaluate the impact of financial crises.
EPJ B Highlight - Greater government responsiveness is paramount to maintaining stable societies
- Published on 20 November 2017
Complex systems models reveal that socio-political instabilities are so predictable that the need to reduce the time lag in political decision-making is blatantly obvious
The Brexit is the perfect example of a time-delayed event. It will happen, if at all, only several years after the referendum vote. Dynamical systems with time delays, like societies making political decisions, have attracted considerable attention from physicists specialised in complex systems. In this new study published in EPJ B, Claudius Gros from Goethe University Frankfurt, Germany has shown that over time, the stability of our democracies can only be preserved by finding ways to reduce the time span governments and other political actors typically need to respond to the wishes of citizens, particularly when confronted with external shocks. That’s because citizens’ opinions are now forming much more quickly than ever before, relative to the time lags that policy decision making involves. This means that drastic changes in modes of governance may be required in order to keep democratic societies stable.
EPJ B Highlight: Improved model of energy highway along protein strands
- Published on 28 August 2017
Lessons from self-trapped electrons in crystal lattice offer better predictive power for transport model
Ever heard of polarons? They are a kind of quasi-particle resulting from electrons self-trapping in a vibrating crystal lattice. Polarons can be harnessed to transport energy under certain conditions related to the relative vibrations of the electrons and the lattice itself. The theory explaining how polarons carry energy in crystals can be applied to long molecules called polypeptides—which can fold into proteins. In a new study published in EPJ B, Jingxi Luo and Bernard Piette from Durham University, UK, present a new mathematical model describing how polarons can be displaced in a directed way with minimum energy loss in linear peptide chains—which were used as a proxy for the study of proteins. The model therefore accounts for the energy transport mechanism explaining how energy generated inside a biological cell moves along transmembrane proteins towards the cell's exterior.
EPJ B Highlight: A new method provides better insights into real-world network evolution
- Published on 28 August 2017
Chinese scientists show how the network structure affects the accuracy of methods predicting the future evolution of a network, like those used to predict protein interactions
Nature and society are full of so-called real-world complex systems, such as protein interactions. Theoretical models, called complex networks, describe them and consist of nodes representing any basic element of that network, and links describing interactions or reactions between two nodes. In the case of protein-interaction studies, reconstruction of complex networks is key as the data available is often inaccurate and our knowledge of the exact nature of these interactions is limited. For reconstruction of networks, link predict -- the likelihood of the existence of a link between two nodes -- matters. Now, Chinese scientists have looked at the influence of the network structure to shed some light on the robustness of the latest methods used to predict the behaviour of such complex networks. Jin-Xuan Yang and Xiao-Dong Zhang from Shanghai Jiao Tong University in China have just published their work in EPJ B, providing a good reference for the choice of a suitable algorithm for link prediction depending on the chosen network structure. In this paper, the authors use two parameters of networks—the common neighbours index and the so-called Gini coefficient index—to reveal the relation between the structure of a network and the accuracy of methods used to predict future links.