Enhancement of perovskite solar cells by plasmonic nanoparticles

Synthetic perovskites with photovoltaic properties open a new era in solar photovoltaics. Due to high optical absorption perovskite-based thin-film solar cells are usually considered as fully absorbing solar radiation on condition of ideal blooming. However, is it really so? The analysis of the literature data has shown that the absorbance of all photovoltaic pervoskites has the spectral hole at infrared frequencies where the solar radiation spectrum has a small local peak. This absorption dip results in the decrease of the optical efficiency of thin-film pervoskite solar cells by nearly 3% and close the ways of utilise them at this range for any other applications. In our work we show that to cure this shortage is possible complementing the basic structure by an inexpensive plasmonic array.Comment: 6 pages 6 picture

Schwarz reflections and the Tricorn

We continue our study of the family $\mathcal{S}$ of Schwarz reflection maps with respect to a cardioid and a circle which was started in [LLMM1]. We prove that there is a natural combinatorial bijection between the geometrically finite maps of this family and those of the basilica limb of the Tricorn, which is the connectedness locus of quadratic anti-holomorphic polynomials. We also show that every geometrically finite map in $\mathcal{S}$ arises as a conformal mating of a unique geometrically finite quadratic anti-holomorphic polynomial and a reflection map arising from the ideal triangle group. We then follow up with a combinatorial mating description for the "periodically repelling" maps in $\mathcal{S}$. Finally, we show that the locally connected topological model of the connectedness locus of $\mathcal{S}$ is naturally homeomorphic to such a model of the basilica limb of the Tricorn

A Study of the Mechanism of Action of Zervamicin IIB Peptide Antibiotic by Molecular Dynamics Simulation

We model mechanism of action of a channel-forming peptide antibiotic, zervamicin IIB, by molecular dynamics (MD) simulation. Interaction of this peptide with neutral and negatively charged lipid bilayers is investigated. It is found that charge of membrane surface influences the orientation of zervamicin IIB molecule, that may in turn effect its permeation into the membrane. On this basis we propose modifications to ZrvIIB structure that may increase its affinity towards the prokaryotic cellular membrane. Zervamicin IIB transmembrane channels are modeled as bundles consisting of 4, 5 and 6 individual peptide monomers. Our results suggest that four monomers don’t form a stable water-filled ion channel. Thus the channel with the least number of monomers (and the lowest conductance level by literature data) is a pentamer

Towards all-dielectric metamaterials and nanophotonics

We review a new, rapidly developing field of all-dielectric nanophotonics which allows to control both magnetic and electric response of structured matter by engineering the Mie resonances in high-index dielectric nanoparticles. We discuss optical properties of such dielectric nanoparticles, methods of their fabrication, and also recent advances in all-dielectric metadevices including couple-resonator dielectric waveguides, nanoantennas, and metasurfaces

Expander Decomposition in Dynamic Streams

In this paper we initiate the study of expander decompositions of a graph $G=(V, E)$ in the streaming model of computation. The goal is to find a partitioning $\mathcal{C}$ of vertices $V$ such that the subgraphs of $G$ induced by the clusters $C \in \mathcal{C}$ are good expanders, while the number of intercluster edges is small. Expander decompositions are classically constructed by a recursively applying balanced sparse cuts to the input graph. In this paper we give the first implementation of such a recursive sparsest cut process using small space in the dynamic streaming model. Our main algorithmic tool is a new type of cut sparsifier that we refer to as a power cut sparsifier - it preserves cuts in any given vertex induced subgraph (or, any cluster in a fixed partition of $V$) to within a $(\delta, \epsilon)$-multiplicative/additive error with high probability. The power cut sparsifier uses $\tilde{O}(n/\epsilon\delta)$ space and edges, which we show is asymptotically tight up to polylogarithmic factors in $n$ for constant $\delta$.Comment: 31 pages, 0 figures, to appear in ITCS 202

Expander Decomposition in Dynamic Streams

In this paper we initiate the study of expander decompositions of a graph G = (V, E) in the streaming model of computation. The goal is to find a partitioning ? of vertices V such that the subgraphs of G induced by the clusters C ? ? are good expanders, while the number of intercluster edges is small. Expander decompositions are classically constructed by a recursively applying balanced sparse cuts to the input graph. In this paper we give the first implementation of such a recursive sparsest cut process using small space in the dynamic streaming model. Our main algorithmic tool is a new type of cut sparsifier that we refer to as a power cut sparsifier - it preserves cuts in any given vertex induced subgraph (or, any cluster in a fixed partition of V) to within a (?, ?)-multiplicative/additive error with high probability. The power cut sparsifier uses O?(n/??) space and edges, which we show is asymptotically tight up to polylogarithmic factors in n for constant ?

Long-term hail risk assessment with deep neural networks

Hail risk assessment is necessary to estimate and reduce damage to crops, orchards, and infrastructure. Also, it helps to estimate and reduce consequent losses for businesses and, particularly, insurance companies. But hail forecasting is challenging. Data used for designing models for this purpose are tree-dimensional geospatial time series. Hail is a very local event with respect to the resolution of available datasets. Also, hail events are rare - only 1% of targets in observations are marked as "hail". Models for nowcasting and short-term hail forecasts are improving. Introducing machine learning models to the meteorology field is not new. There are also various climate models reflecting possible scenarios of climate change in the future. But there are no machine learning models for data-driven forecasting of changes in hail frequency for a given area. The first possible approach for the latter task is to ignore spatial and temporal structure and develop a model capable of classifying a given vertical profile of meteorological variables as favorable to hail formation or not. Although such an approach certainly neglects important information, it is very light weighted and easily scalable because it treats observations as independent from each other. The more advanced approach is to design a neural network capable to process geospatial data. Our idea here is to combine convolutional layers responsible for the processing of spatial data with recurrent neural network blocks capable to work with temporal structure. This study compares two approaches and introduces a model suitable for the task of forecasting changes in hail frequency for ongoing decades