A novel device for controlling the flow of information based on Weyl fermions and a method for manipulating the spatial distribution of Weyl particles

In this work we propose a novel device for controlling the flow of information using Weyl fermions. In more detail, based on a previous work of our group, we show that it is possible to fully control the flow of Weyl fermions on a sequence of different channels, by applying an electric field perpendicular to the direction of motion of the particles on each channel. In this way, we can transmit information, logical bits, depending on the existence or not of a Weyl current on each channel. We also show that the response time of this device is exceptionally low, less than 1 ps, for typical values of the parameters, providing the opportunity to control the flow of information at extremely high rates, of the order of 100 Pbps. This device also offers additional advantages, as low power consumption and robustness against electromagnetic perturbations, and is expected to find important applications in several fields, as telecommunications, signal processing, classical and quantum computing, etc. Finally, we demonstrate that Weyl fermions can be efficiently guided through the proposed device using appropriate magnetic fields

Degenerate solutions to the massless Dirac and Weyl equations and a proposed method for controlling the quantum state of Weyl particles

In a recent work we have shown that all solutions to the Weyl equation and a special class of solutions to the Dirac equation are degenerate, in the sense that they remain unaltered under the influence of a wide variety of different electromagnetic fields. In the present article our previous work is significantly extended, providing a wide class of degenerate solutions to the Dirac equation for massless particles. The electromagnetic fields corresponding to these solutions are calculated, giving also some examples regarding both spatially constant electromagnetic fields and electromagnetic waves. Further, some general forms of solutions to the Weyl equation are presented and the corresponding electromagnetic fields are calculated. Based on these results, a method for fully controlling the quantum state of Weyl particles through appropriate electromagnetic fields is proposed. Finally, the transition from degenerate to non-degenerate solutions as the particles acquire mass is discussed.Comment: Keywords: Dirac equation, Weyl equation, Degenerate solutions, Massless particles, Electromagnetic 4-potentials, Electromagnetic fields, Electromagnetic waves, Nearly degenerate solution

A general method for obtaining degenerate solutions to the Dirac and Weyl equations and a discussion on the experimental detection of degenerate states

In this work we describe a general method for obtaining degenerate solutions to the Dirac equation, corresponding to an infinite number of electromagnetic 4-potentials and fields, which are explicitly calculated. In more detail, using four arbitrary real functions, one can automatically construct a spinor which is solution to the Dirac equation for an infinite number of electromagnetic 4-potentials, defined by those functions. An interesting characteristic of these solutions is that, in the case of Dirac particles with non-zero mass, the degenerate spinors should be localized, both in space and time. Our method is also extended to the cases of massless Dirac and Weyl particles, where the localization of the spinors is no longer required. Finally, we propose two experimental methods for detecting the presence of degenerate states.Comment: In this version of the article we have added a discussion on the experimental detection of degenerate states, proposing two techniques based on electrical and optical measurement

AC and DC conductivity correlation: The coefficient of Barton--Nakajima--Namikawa relation

It has been some time since an empirical relation, which correlates DC with AC conductivity and contains a loosely defined coefficient thought to be of order one, was introduced by Barton, Nakajima and Namikawa. In this work, we derived this relation assuming that the conductive response consists of a superposition of DC conductivity and an AC conductivity term which materialized through a Havriliak--Negami dielectric function. The coefficient was found to depend on the Havriliak--Negami shape parameters as well as on the ratio of two characteristic time scales of ions motion which are related to ionic polarization mechanism and the onset of AC conductivity. The results are discussed in relation to other relevant publications and they also applied to a polymeric material. Both, theoretical predictions and experimental evaluations of the BNN coefficient are in an excellent agreement, while this coefficient shows a gradual reduction as the temperature increases.Comment: 15 pages plain latex2e, 5 eps figures (new figures added). In this revised version the manuscript has been rewritten extensively due to motivational comments and suggestions made by the referee. Accepted for publication by the Journal of Non--Crystalline Solid

Expanding the portfolio of tribo-positive materials: aniline formaldehyde condensates for high charge density triboelectric nanogenerators

The rapid uptake of energy harvesting triboelectric nanogenerators (TENGs) for self-powered electronics requires the development of high-performance tribo-materials capable of providing large power outputs. This work reports on the synthesis and use of aniline formaldehyde resin (AFR) for energy-harvesting applications. The facile, acidic-medium reaction between aniline and formaldehyde produces the aniline-formaldehyde condensate, which upon an in-vacuo high temperature curing step provides smooth AFR films with abundant nitrogen and oxygen surface functional groups which can acquire a tribo-positive charge and thus endow AFR with a significantly higher positive tribo-polarity than the existing state-of-art polyamide-6 (PA6). A TENG comprising of optimized thin-layered AFR against a polytetrafluoroethylene (PTFE) film produced a peak-to-peak voltage of up to ~1,000 V, a current density of ~65 mA m⁻², a transferred charge density of ~200 μC m⁻² and an instantaneous power output (energy pulse) of ~11 W m⁻² (28.1 μJ cycle⁻¹), respectively. The suitability of AFR was further supported through the Kelvin probe force microscopy (KPFM) measurements, which reveal a significantly higher average surface potential value of 1.147 V for AFR as compared to 0.87 V for PA6 and a step-by-step increase of the surface potential with the increase of energy generation cycles. The work not only proposes a novel and scalable mouldable AFR synthesis process but also expands with excellent prospects, the current portfolio of tribo-positive materials for triboelectric energy harvesting applications

The Electrical Conductivity of Ionic Liquids: Numerical and Analytical Machine Learning Approaches

In this paper, we incorporate experimental measurements from high-quality databases to construct a machine learning model that is capable of reproducing and predicting the properties of ionic liquids, such as electrical conductivity. Empirical relations traditionally determine the electrical conductivity with the temperature as the main component, and investigations only focus on specific ionic liquids every time. In addition to this, our proposed method takes into account environmental conditions, such as temperature and pressure, and supports generalization by further considering the liquid atomic weight in the prediction procedure. The electrical conductivity parameter is extracted through both numerical machine learning methods and symbolic regression, which provides an analytical equation with the aid of genetic programming techniques. The suggested platform is capable of providing either a fast, numerical prediction mechanism or an analytical expression, both purely data-driven, that can be generalized and exploited in similar property prediction projects, overcoming expensive experimental procedures and computationally intensive molecular simulations

The Electrical Conductivity of Ionic Liquids: Numerical and Analytical Machine Learning Approaches

In this paper, we incorporate experimental measurements from high-quality databases to construct a machine learning model that is capable of reproducing and predicting the properties of ionic liquids, such as electrical conductivity. Empirical relations traditionally determine the electrical conductivity with the temperature as the main component, and investigations only focus on specific ionic liquids every time. In addition to this, our proposed method takes into account environmental conditions, such as temperature and pressure, and supports generalization by further considering the liquid atomic weight in the prediction procedure. The electrical conductivity parameter is extracted through both numerical machine learning methods and symbolic regression, which provides an analytical equation with the aid of genetic programming techniques. The suggested platform is capable of providing either a fast, numerical prediction mechanism or an analytical expression, both purely data-driven, that can be generalized and exploited in similar property prediction projects, overcoming expensive experimental procedures and computationally intensive molecular simulations