Lattice thermal conductivity of 2D nanomaterials: a simple semi-empirical approach.

Extracting reliable information on certain physical properties of materials, such as thermal transport, can be computationally very demanding. Aiming to overcome such difficulties in the particular case of lattice thermal conductivity (LTC) of 2D nanomaterials, we propose a simple, fast, and accurate semi-empirical approach for LTC calculation. The approach is based on parameterized thermochemical equations and Arrhenius-like fitting procedures, thus avoiding molecular dynamics or ab initio protocols, which frequently require computationally expensive simulations. As a proof of concept, we obtain the LTC of some prototypical physical systems, such as graphene (and other 2D carbon allotropes), hexagonal boron nitride (hBN), silicene, germanene, binary, and ternary BNC lattices and two examples of the fullerene network family. Our obtained values are in good agreement with other theoretical and experimental estimations, nonetheless, being derived in a rather straightforward way, at a fraction of the usual computational cost.

Electronic and optical properties of B x C y N z hybrid α-graphynes.

Hybrid two-dimensional (2D) materials composed of carbon, boron, and nitrogen constitute a hot topic of research, as their flexible composition allows for tunable properties. However, while graphene-like hybrid lattices have been well characterized, systematic investigations are lacking for various 2D materials. Hence, in the present contribution, we employ first-principles calculations to investigate the structural, electronic and optical properties of what we call B x C y N z hybrid α-graphynes. We considered eleven structures with stoichiometry BC2N and varied atomic arrangements. We calculated the formation energy for each arrangement, and determined that it is low (high) when the number of boron-carbon and nitrogen-carbon bonds is low (high). We found that the formation energy of many our structures compared favorably with a previous literature proposal. Regarding the electronic properties, we found that the investigated structures are semiconducting, with band gaps ranging from 0.02 to 2.00 eV. Moreover, we determined that most of the B x C y N z hybrid α-graphynes proposed here strongly absorb infrared light, and so could potentially find applications in optoelectronic devices such as heat sensors and infrared filters.

B x C y N z hybrid graphenylene: stability and electronic properties.

Interest in hybrid monolayers with arrangements that differ from that of the honeycomb lattice has been growing. However, systematic investigations on the properties of these structures are still lacking. In this work, we combined density functional theory (DFT) and molecular dynamics (MD) simulations to study the stability and electronic properties of nanosheets composed of B, C, and N atoms arranged in the pattern of the carbon allotrope graphenylene. We considered twenty structures with varied atomic arrangements and stoichiometries, which we call B x C y N z hybrid graphenylenes. We calculated the formation energy for each arrangement, and found that it decreases as the number of B-C and N-C bonds decreases. We also found that the structure with minimum energy has stoichiometry B2CN and an atomic arrangement with BN and C stripes connected along the zigzag direction. Regarding the electronic properties, we found that all investigated structures are semiconductors, with band gaps ranging from 0.14 to 1.65 eV. Finally, we found that the optimized hybrid lattices presented pores of varied sizes and shapes. This diversity in pore geometry suggests that these structures might be particularly suited for molecular sieve applications.

Inferring Lévy walks from curved trajectories: A rescaling method.

An important problem in the study of anomalous diffusion and transport concerns the proper analysis of trajectory data. The analysis and inference of Lévy walk patterns from empirical or simulated trajectories of particles in two and three-dimensional spaces (2D and 3D) is much more difficult than in 1D because path curvature is nonexistent in 1D but quite common in higher dimensions. Recently, a new method for detecting Lévy walks, which considers 1D projections of 2D or 3D trajectory data, has been proposed by Humphries et al. The key new idea is to exploit the fact that the 1D projection of a high-dimensional Lévy walk is itself a Lévy walk. Here, we ask whether or not this projection method is powerful enough to cleanly distinguish 2D Lévy walk with added curvature from a simple Markovian correlated random walk. We study the especially challenging case in which both 2D walks have exactly identical probability density functions (pdf) of step sizes as well as of turning angles between successive steps. Our approach extends the original projection method by introducing a rescaling of the projected data. Upon projection and coarse-graining, the renormalized pdf for the travel distances between successive turnings is seen to possess a fat tail when there is an underlying Lévy process. We exploit this effect to infer a Lévy walk process in the original high-dimensional curved trajectory. In contrast, no fat tail appears when a (Markovian) correlated random walk is analyzed in this way. We show that this procedure works extremely well in clearly identifying a Lévy walk even when there is noise from curvature. The present protocol may be useful in realistic contexts involving ongoing debates on the presence (or not) of Lévy walks related to animal movement on land (2D) and in air and oceans (3D).