Publications

In convergent beam electron diffraction (CBED) on two-dimensional (2D) materials, the intensity distributions within the individual CBED spots map the local atomic arrangements within the probed region. In this study we demonstrate that the average intensities within the CBED spots essentially depend on the scattering parameters by a single atom, thus, offering the possibility of the direct measurement of such parameters. Scattering potential of an individual atom can be approximated by the Gaussian function and its parameters, the standard deviation and the maximal phase shift, can be recovered from the ratio of the intensities in the zero- and higher orders spots in CBED pattern. In order to demonstrate this, we simulated CBED patterns and extracted the atomic scattering parameters from such patterns. In the simulated examples, no weak phase object approximation is applied and the proposed method provides accurate results even for materials with large phase shifts up to 1.5 rad, as for example tungsten. The scattering parameters recovered from the experimental CBED patterns of graphene and twisted graphene-hBN structure show good agreement with the theoretically obtained values.

A mineral assemblage including sulfosalts of the bismuthinite-aikinite (aikinite, bismuthinite, gladite, salzburgite, krupkaite, lindstromite, pekoite, friedrichite, hammarite), pavonite (makovickyite, cupromakovickyite, pavonite, dantopaite, mummeite), cupropavonite (cupromakopavonite, cupropavonite) and lillianite (lillianite s.s.) homologous series, as well as angelaite, berryite, matildite, baksanite, ikunolite, tetradymite, hedleyite and native bismuth is identifed in veinlet ores of the Yugo-Konevo and Porokhovskoe tungsten deposits (Chelyabinsk oblast, South Urals). The Bi minerals occur as individual grains or intergrowths in fuorite-muscovite-quartz veins with hubnerite and scheelite , inclusions in quartz and intersticial aggregates between pyrite, sphalerite and galena. Angelaite, dantopaite, cupromakovickyite and cupromakopavonite are frst found at the territory of Russian Federation and salzburgite, cupropavonite, makovickyite and mummeite are found for the frst time in the South Urals.

No abstract is provided for this article.

No abstract is provided for this article.

Nanomaterials based on two-dimensional (2D) atomic crystals are considered to be very promising for various life-science and medical applications, from drug delivery to tissue modification. One of the most suitable materials for these purposes is graphene oxide (GO), thanks to a well-developed methods of production and water solubility. At the same time, its biological effect is still debated. Here we demonstrate that highly purified and thoroughly washed GO neither inhibited nor stimulated the growth of E.coli, ATCC25922; E.coli NCIMB11943 and S.aureus ATCC25923 at concentrations of up to 1 mg ml−1. Moreover, transmission electron microscopy (TEM) of GO exposed bacteria did not reveal any differences between GO exposed and not exposed populations. In contrast, a suspension of insufficiently purified GO behaved as an antibacterial material due to the presence of soluble acidic impurities, that could be removed by extended purification or neutralisation by alkaline substrates. A standardised protocol is proposed for the generation of clean GO, so it becomes suitable for biological experiments. Our findings emphasise the importance of GO purification status when dealing with biological systems as the true effect of material can be masked by the impact of impurities.

No abstract is provided for this article.

Understanding of the electrochemical properties of graphene, especially the electron transfer kinetics of a redox reaction between the graphene surface and a molecule, in comparison to graphite or other carbon-based materials, is essential for its potential in energy conversion and storage to be realized. Here we use voltammetric determination of the electron transfer rate for three redox mediators, ferricyanide, hexaammineruthenium, and hexachloroiridate (Fe(CN)63–, Ru(NH3)63+, and IrCl62–, respectively), to measure the reactivity of graphene samples prepared by mechanical exfoliation of natural graphite. Electron transfer rates are measured for varied number of graphene layers (1 to ca. 1000 layers) using microscopic droplets. The basal planes of mono- and multilayer graphene, supported on an insulating Si/SiO2 substrate, exhibit significant electron transfer activity and changes in kinetics are observed for all three mediators. No significant trend in kinetics with flake thickness is discernible for each mediator; however, a large variation in kinetics is observed across the basal plane of the same flakes, indicating that local surface conditions affect the electrochemical performance. This is confirmed by in situ graphite exfoliation, which reveals significant deterioration of initially, near-reversible kinetics for Ru(NH3)63+ when comparing the atmosphere-aged and freshly exfoliated graphite surfaces.

Bandstructure engineering using alloying is widely utilised for achieving optimised performance in modern semiconductor devices. While alloying has been studied in monolayer transition metal dichalcogenides, its application in van der Waals heterostructures built from atomically thin layers is largely unexplored. Here, we fabricate heterobilayers made from monolayers of WSe$_2$ (or MoSe$_2$) and Mo$_x$W$_{1-x}$Se$_2$ alloy and observe nontrivial tuning of the resultant bandstructure as a function of concentration $x$. We monitor this evolution by measuring the energy of photoluminescence (PL) of the interlayer exciton (IX) composed of an electron and hole residing in different monolayers. In Mo$_x$W$_{1-x}$Se$_2$/WSe$_2$, we observe a strong IX energy shift of $\approx$100 meV for $x$ varied from 1 to 0.6. However, for $x<0.6$ this shift saturates and the IX PL energy asymptotically approaches that of the indirect bandgap in bilayer WSe$_2$. We theoretically interpret this observation as the strong variation of the conduction band K valley for $x>0.6$, with IX PL arising from the K-K transition, while for $x<0.6$, the bandstructure hybridization becomes prevalent leading to the dominating momentum-indirect K-Q transition. This bandstructure hybridization is accompanied with strong modification of IX PL dynamics and nonlinear exciton properties. Our work provides foundation for bandstructure engineering in van der Waals heterostructures highlighting the importance of hybridization effects and opening a way to devices with accurately tailored electronic properties.

No abstract is provided for this article.

No abstract is provided for this article.

Kohn-Sham Density Functional Theory (KS-DFT) has been traditionally solved by the Self-Consistent Field (SCF) method. Behind the SCF loop is the physics intuition of solving a system of non-interactive single-electron wave functions under an effective potential. In this work, we propose a deep learning approach to KS-DFT. First, in contrast to the conventional SCF loop, we propose to directly minimize the total energy by reparameterizing the orthogonal constraint as a feed-forward computation. We prove that such an approach has the same expressivity as the SCF method, yet reduces the computational complexity from O(N^4) to O(N^3). Second, the numerical integration which involves a summation over the quadrature grids can be amortized to the optimization steps. At each step, stochastic gradient descent (SGD) is performed with a sampled minibatch of the grids. Extensive experiments are carried out to demonstrate the advantage of our approach in terms of efficiency and stability. In addition, we show that our approach enables us to explore more complex neural-based wave functions.

Modification of physical properties of materials and design of materials with on-demand characteristics is at the heart of modern technology. Rare application relies on pure materials--most devices and technologies require careful design of materials properties through alloying, creating heterostructures of composites or controllable introduction of defects. At the same time, such designer materials are notoriously difficult for modelling. Thus, it is very tempting to apply machine learning methods for such systems. Unfortunately, there is only a handful of machine learning-friendly material databases available these days. We develop a platform for easy implementation of machine learning techniques to materials design and populate it with datasets on pristine and defected materials. Here we describe datasets of defects in represented 2D materials such as MoS2, WSe2, hBN, GaSe, InSe, and black phosphorous, calculated using DFT. Our study provides a data-driven physical understanding of complex behaviors of defect properties in 2D materials, holding promise for a guide to the development of efficient machine learning models. In addition, with the increasing enrollment of datasets, our database could provide a platform for designing of materials with predetermined properties.

No abstract is provided for this article.