Moscow Institute of Physics and Technology

About: Moscow Institute of Physics and Technology is a education organization based out in Dolgoprudnyy, Russia. It is known for research contribution in the topics: Laser & Plasma. The organization has 8594 authors who have published 16968 publications receiving 246551 citations. The organization is also known as: MIPT & Moscow Institute of Physics and Technology (State University).

Hallmarks of mechanochemistry: From nanoparticles to technology

Abstract: The aim of this review article on recent developments of mechanochemistry (nowadays established as a part of chemistry) is to provide a comprehensive overview of advances achieved in the field of atomistic processes, phase transformations, simple and multicomponent nanosystems and peculiarities of mechanochemical reactions. Industrial aspects with successful penetration into fields like materials engineering, heterogeneous catalysis and extractive metallurgy are also reviewed. The hallmarks of mechanochemistry include influencing reactivity of solids by the presence of solid-state defects, interphases and relaxation phenomena, enabling processes to take place under non-equilibrium conditions, creating a well-crystallized core of nanoparticles with disordered near-surface shell regions and performing simple dry time-convenient one-step syntheses. Underlying these hallmarks are technological consequences like preparing new nanomaterials with the desired properties or producing these materials in a reproducible way with high yield and under simple and easy operating conditions. The last but not least hallmark is enabling work under environmentally friendly and essentially waste-free conditions (822 references).

Plasma assisted ignition and combustion

Abstract: In recent decades particular interest in applications of nonequilibrium plasma for the problems of plasma-assisted ignition and plasma-assisted combustion has been observed. A great amount of experimental data has been accumulated during this period which provided the grounds for using low temperature plasma of nonequilibrium gas discharges for a number of applications at conditions of high speed flows and also at conditions similar to automotive engines. The paper is aimed at reviewing the data obtained and discusses their treatment. Basic possibilities of low temperature plasma to ignite gas mixtures are evaluated and historical references highlighting pioneering works in the area are presented. The first part of the review discusses plasmas applied to plasma-assisted ignition and combustion. The paper pays special attention to experimental and theoretical analysis of some plasma parameters, such as reduced electric field, electron density and energy branching for different gas discharges. Streamers, pulsed nanosecond discharges, dielectric barrier discharges, radio frequency discharges and atmospheric pressure glow discharges are considered. The second part depicts applications of discharges to reduce the ignition delay time of combustible mixtures, to ignite transonic and supersonic flows, to intensify ignition and to sustain combustion of lean mixtures. The results obtained by different authors are cited, and ways of numerical modelling are discussed. Finally, the paper draws some conclusions on the main achievements and prospects of future investigations in the field.

Optical far-infrared properties of a graphene monolayer and multilayer

Abstract: We analyze the features of the graphene mono- and multilayer reflectance in the far-infrared region as a function of frequency, temperature, and carrier density taking the intraband conductance and the interband electron absorption into account. The dispersion of plasmon mode of the multilayers is calculated using Maxwell's equations with the influence of retardation included. At low temperatures and high electron densities, the reflectance of multilayers as a function of frequency has the sharp downfall and the subsequent deep well due to the threshold of electron interband absorption and plasmon excitations.

A facility to search for hidden particles at the CERN SPS: the SHiP physics case.

Abstract: This paper describes the physics case for a new fixed target facility at CERN SPS. The SHiP (search for hidden particles) experiment is intended to hunt for new physics in the largely unexplored domain of very weakly interacting particles with masses below the Fermi scale, inaccessible to the LHC experiments, and to study tau neutrino physics. The same proton beam setup can be used later to look for decays of tau-leptons with lepton flavour number non-conservation, $\tau \to 3\mu $ and to search for weakly-interacting sub-GeV dark matter candidates. We discuss the evidence for physics beyond the standard model and describe interactions between new particles and four different portals—scalars, vectors, fermions or axion-like particles. We discuss motivations for different models, manifesting themselves via these interactions, and how they can be probed with the SHiP experiment and present several case studies. The prospects to search for relatively light SUSY and composite particles at SHiP are also discussed. We demonstrate that the SHiP experiment has a unique potential to discover new physics and can directly probe a number of solutions of beyond the standard model puzzles, such as neutrino masses, baryon asymmetry of the Universe, dark matter, and inflation.

Deep reinforcement learning for de novo drug design

Abstract: We have devised and implemented a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). On the basis of deep and reinforcement learning (RL) approaches, ReLeaSE integrates two deep neural networks—generative and predictive—that are trained separately but are used jointly to generate novel targeted chemical libraries. ReLeaSE uses simple representation of molecules by their simplified molecular-input line-entry system (SMILES) strings only. Generative models are trained with a stack-augmented memory network to produce chemically feasible SMILES strings, and predictive models are derived to forecast the desired properties of the de novo–generated compounds. In the first phase of the method, generative and predictive models are trained separately with a supervised learning algorithm. In the second phase, both models are trained jointly with the RL approach to bias the generation of new chemical structures toward those with the desired physical and/or biological properties. In the proof-of-concept study, we have used the ReLeaSE method to design chemical libraries with a bias toward structural complexity or toward compounds with maximal, minimal, or specific range of physical properties, such as melting point or hydrophobicity, or toward compounds with inhibitory activity against Janus protein kinase 2. The approach proposed herein can find a general use for generating targeted chemical libraries of novel compounds optimized for either a single desired property or multiple properties.