Preface: XXVI International Conference on Neutrino Physics and Astrophysics
No abstract prepared.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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Committees: XXVI International Conference on Neutrino Physics and Astrophysics
No abstract prepared.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
First detection of high-energy astrophysical neutrinos with IceCube
The IceCube detector at the South Pole is the world’s largest neutrino telescope, instrumenting a cubic kilometre of deep clear ice. Completed in late 2010, the detector has recorded the arrival directions and energies of tens of thousands of neutrinos – mostly those produced when cosmic rays collide with the Earth’s atmosphere. Here, we report on the first observation of high-energy neutrinos from beyond the Earth’s atmosphere, identified using a novel method to strongly suppress atmospheric neutrinos coming downward into the detector from the southern sky, leaving a sample of neutrinos highly likely to be of astrophysical origin.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Conference Photo: XXVI International Conference on Neutrino Physics and Astrophysics
No abstract prepared.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
The effect of spherical nanoparticles on rheological properties of bi-dispersed magnetorheological fluids
In the present investigation, the rheological properties of bi-dispersed magnetorheological (MR) fluid based on Fe{sub 3}O{sub 4} nanosphere and microsphere of iron particles are experimentally investigated. The MR fluid is prepared by substituting nanosphere of 40nm Fe{sub 3}O{sub 4} particles in MR fluids having microsphere iron particles (7-8 μm). Three different weight fractions (0%, 1% and 3%) of nanosphere-microsphere MR fluids are synthesized. In the absence of the magnetic field, substitution of magnetic nanosphere decreases the viscosity lower than without substituted sample at high as well as low shear rate. Upon the application of the magnetic field, the particles align along the direction of the field, which promotes the yield stress. Here too the yield stress value decreases with magnetic nanosphere substitution. This behaviour is explain based on the inter-particle interaction as well as formation of nanosphere cloud around the magnetic microsphere, which effectively reduces the viscosity and works as weak point when chains are formed. Variation of dynamic yield stress with magnetic field is explained using microscopic model. In any event such fluid does not sediment and is not abrasive so it could be useful if not too high yield stress is needed.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Preparation and characterization of composites based on poly(lactic acid) and CaCO{sub 3} nanofiller
In recent years, extensive studies have been conducted on the study of the poly(lactic acid) (PLA) properties, because of its being a typical biobased and biodegradable polymer, with good mechanical properties. However, its toughness and gas barrier properties are not satisfactory and can be improved by the addition of nanofillers, such as calcium carbonate (n-CaCO{sub 3}). The present work PLA composites with nano-sized precipitated calcium carbonate (n-NPCC) were prepared by melt extrusion. Thermal, mechanical and flow properties of the composites were evaluated by using a factorial design.The results showed that the addition of the nanofiller in the PLA matrix didn’t improve thethermal and mechanical properties of the matrix significantly. This behavior is probably due to the presence of the stearic acid that is coating on the n-NPCC particles, resulting in a weak polymer-particle interaction. Beyond this, it was also observed a decrease in MFI of the composites when nanofiller was added and at a higher screw speed.
36 MATERIALS SCIENCE↗
Flux-driven algebraic damping of diocotron modes
Recent experiments with pure electron plasmas in a Malmberg-Penning trap have observed the algebraic damping of m = 1 and m = 2 diocotron modes. Transport due to small field asymmetries produces a low density halo of electrons moving radially outward from the plasma core, and the mode damping begins when the halo reaches the resonant radius R{sub m}, where there is a matching of ω{sub m} = mω{sub E} (R{sub m}) for the mode frequency ω{sub m} and E × B-drift rotation frequency ω{sub E}. The damping rate is proportional to the flux of halo particles through the resonant layer. The damping is related to, but distinct from, spatial Landau damping, in which a linear wave-particle resonance produces exponential damping. This new mechanism of damping is due to transfer of canonical angular momentum from the mode to halo particles, as they are swept around the “cat’s eye” orbits of the resonant wave-particle interaction. This paper provides a simple derivation of the time dependence of the mode amplitudes.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
On the statistical and transport properties of a non-dissipative Fermi-Ulam model
The transport and diffusion properties for the velocity of a Fermi-Ulam model were characterized using the decay rate of the survival probability. The system consists of an ensemble of non-interacting particles confined to move along and experience elastic collisions with two infinitely heavy walls. One is fixed, working as a returning mechanism of the colliding particles, while the other one moves periodically in time. The diffusion equation is solved, and the diffusion coefficient is numerically estimated by means of the averaged square velocity. Our results show remarkably good agreement of the theory and simulation for the chaotic sea below the first elliptic island in the phase space. From the decay rates of the survival probability, we obtained transport properties that can be extended to other nonlinear mappings, as well to billiard problems.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Development of Colle-Salvetti type electron-nucleus correlation functional for MC-DFT
A Colle-Salvetti type electron-nucleus correlation functional for multicomponent density-functional theory is proposed. We demonstrate that our correlation functional quantitatively reproduces the quantum nuclear effects of protons; the mean absolute deviation value is 2.8 millihartrees for the optimized structure of hydrogen-containing molecules. We also show other practical calculations with our new electron-deuteron and electron-triton correlation functionals. Since this functional is derived without any unphysical assumption, the strategy taken in this development will be a promising recipe to make new functionals for the potentials of other particles’ interactions.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Thermodynamic stability and structural properties of cluster crystals formed by amphiphilic dendrimers
We pursue the goal of finding real-world examples of macromolecular aggregates that form cluster crystals, which have been predicted on the basis of coarse-grained, ultrasoft pair potentials belonging to a particular mathematical class [B. M. Mladek et al., Phys. Rev. Lett. 46, 045701 (2006)]. For this purpose, we examine in detail the phase behavior and structural properties of model amphiphilic dendrimers of the second generation by means of monomer-resolved computer simulations. On augmenting the density of these systems, a fluid comprised of clusters that contain several overlapping and penetrating macromolecules is spontaneously formed. Upon further compression of the system, a transition to multi-occupancy crystals takes place, the thermodynamic stability of which is demonstrated by means of free-energy calculations, and where the FCC is preferred over the BCC-phase. Contrary to predictions for coarse-grained theoretical models in which the particles interact exclusively by effective pair potentials, the internal degrees of freedom of these molecules cause the lattice constant to be density-dependent. Furthermore, the mechanical stability of monodisperse BCC and FCC cluster crystals is restricted to a bounded region in the plane of cluster occupation number versus density. The structural properties of the dendrimers in the dense crystals, including their overall sizes and the distribution of monomers are also thoroughly analyzed.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Hierarchical modeling of molecular energies using a deep neural network
In this work, we introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. Finally, with minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Turbulence-driven anisotropic electron tail generation during magnetic reconnection
Magnetic reconnection (MR) plays an important role in particle transport, energization, and acceleration in space, astrophysical, and laboratory plasmas. In the Madison Symmetric Torus reversed field pinch, discrete MR events release large amounts of energy from the equilibrium magnetic field, a fraction of which is transferred to electrons and ions. Previous experiments revealed an anisotropic electron tail that favors the perpendicular direction and is symmetric in the parallel. New profile measurements of x-ray emission show that the tail distribution is localized near the magnetic axis, consistent modeling of the bremsstrahlung emission. The tail appears first near the magnetic axis and then spreads radially, and the dynamics in the anisotropy and diffusion are discussed. The data presented imply that the electron tail formation likely results from a turbulent wave-particle interaction and provides evidence that high energy electrons are escaping the core-localized region through pitch angle scattering into the parallel direction, followed by stochastic parallel transport to the plasma edge. Finally, new measurements also show a strong correlation between high energy x-ray measurements and tearing mode dynamics, suggesting that the coupling between core and edge tearing modes is essential for energetic electron tail formation.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Collisional enhancement of energetic particle Alfvénic resonance width in tokamaks
The phase-space structure of resonances between fast ions and an Alfvénic mode and the associated modification of density profiles in tokamaks are studied as a function of particle collisions. Guiding-center simulations in a realistic tokamak equilibrium are employed to address the resonance broadening parametric dependencies with respect to changes in the pitch-angle scattering rate. The rate of collisional replenishment, along with resonance strength, given by the combination of eigenmode and resonance structures and equilibrium parameters, determines saturation amplitudes for a given damping rate. As seen from the distribution function flattening, collisions have an effect of broadening the resonances, while the absolute value of δf decreases with increasing collisionality. It is observed that the collisional broadening can be comparable to the collisionless resonance width due to the mode amplitude alone. The resonance broadening coefficients are compared with the existing theory based on analytically expected saturation levels, showing fair agreement. Furthermore the results can be useful in assisting reduced kinetic models, such as quasilinear models, when prescribing the effective resonance phase-space width, i.e., the mode-particle interaction platform, due to collisional or turbulent processes.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Characterization of LiFePO4 samples obtained by pulse combustion under various conditions of synthesis
Lithium iron phosphate (LiFePO4, LFP) is one of the widely used cathode materials for rechargeable lithium ion batteries. LFP batteries are widely used for electric vehicles and backup power due to their important advantages such as low cost, lifetime, efficiency, and reliability. There are still several technical challenges that need to be addressed: The increase of energy density or further reduction of their final cost. This paper concerned with the characterization of carbon coated LiFePO4 nanopowder cathode materials produced under different conditions by pulse combustion for providing energy to the reactor for the synthesis. The reactor was built according to the principles of the thermoacoustic burner on the basis of the Helmholtz resonator. The investigated nanopowders are synthesized by complete and incomplete combustion and calcined at 700 °C. The obtained samples were characterized by X-ray diffraction, Fourier transform infrared, Raman, and Mössbauer spectroscopy. Observed low-Temperature magnetic phase transitions definitively identified the crystal phases. The morphology of samples was controlled by scanning electron microscopy. The aim of this work is to show that it is possible to achieve a desired crystal phase by pulse combustion in a relatively cheap and fast way. The extremely rapid synthesis of almost pure phase material is possible due to the reduction in size of interacting particles and to an enormous number of collisions between them as a result of strong turbulent flow associated with explosive combustion. © 2019 Author(s).
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Mean force kinetic theory applied to self-diffusion in supercritical Lennard-Jones fluids
A new kinetic-theory-based calculation of the self-diffusion coefficient for dense supercritical Lennard-Jones fluids is presented. The mean force kinetic theory, which was recently developed for transport in dense plasmas, is applied for the calculation of diffusion in dense neutral fluids. The calculation only requires the pair distribution function, a quantity that is readily calculable from equilibrium statistical mechanics for many systems, including the Lennard-Jones fluid. The self-diffusion coefficients are compared with calculations from molecular dynamics simulations, and good agreement at high density is demonstrated, even in the vicinity of the solid–fluid coexistence line. A comparison of different kinetic models with molecular dynamics simulations demonstrates that the transport coefficients have important contributions due to particle interaction via a potential of mean force and local correlations, which increase the collision rate. The new calculations compare well to those from free-volume theory and overcome a limitation of this theory that prevents its use in systems that interact via long range monotonic potentials. It is expected that this approach will also apply to other systems, including neutral-plasma and neutral-electrolyte mixtures.
79 ASTRONOMY AND ASTROPHYSICS↗
Bond order predictions using deep neural networks
Machine learning is an extremely powerful tool for the modern theoretical chemist since it provides a method for bypassing costly algorithms for solving the Schrödinger equation. Already, it has proven able to infer molecular and atomic properties such as charges, enthalpies, dipoles, excited state energies, and others. Most of these machine learning algorithms proceed by inferring properties of individual atoms, even breaking down total molecular energy into individual atomic contributions. In this paper, we introduce a modified version of the Hierarchically Interacting Particle Neural Network (HIP-NN) capable of making predictions on the bonds between atoms rather than on the atoms themselves. We train the modified HIP-NN to infer bond orders for a large number of small organic molecules as computed via the Natural Bond Orbital package. We demonstrate that the trained model is extensible to molecules much larger than those in the training set by studying its performance on the COMP6 dataset. This method has applications in cheminformatics and force field parameterization and opens a promising future for machine learning models to predict other quantities that are defined between atoms such as density matrix elements, Hamiltonian parameters, and molecular reactivities.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Simulating sticky particles: A Monte Carlo method to sample a stratification
Many problems in materials science and biology involve particles interacting with strong, short-ranged bonds that can break and form on experimental timescales. Treating such bonds as constraints can significantly speed up sampling their equilibrium distribution, and there are several methods to sample probability distributions subject to fixed constraints. We introduce a Monte Carlo method to handle the case when constraints can break and form. More generally, the method samples a probability distribution on a stratification: a collection of manifolds of different dimensions, where the lower-dimensional manifolds lie on the boundaries of the higher-dimensional manifolds. We show several applications of the method in polymer physics, self-assembly of colloids, and volume calculation in high dimensions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
From predicting to learning dissipation from pair correlations of active liquids
Active systems, which are driven out of equilibrium by local non-conservative forces, can adopt unique behaviors and configurations. Towards designing such materials, an important challenge is to precisely connect the static structure of active systems to the dissipation of energy induced by the local driving. Here, we use tools from liquid-state theories and machine learning to take on these challenges. We first demonstrate analytically for an isotropic active matter system that dissipation and pair correlations are closely related when driving forces behave like an active temperature. We then extend a nonequilibrium mean-field framework for predicting these pair correlations which, unlike most existing approaches, is applicable even for strongly interacting particles and far from equilibrium, to predict dissipation in these systems. Based on this theory, we reveal analytically a robust relation between dissipation and structure which holds even as the system approaches a nonequilibrium phase transition. Finally, we construct a neural network which maps static configurations of particles to their dissipation rate without any prior knowledge of the underlying dynamics. Furthermore, our results open novel perspectives on the interplay between dissipation and organization out-of-equilibrium.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗