A DMI Guide to Magnets Micro-World

Journal of Experimental and Theoretical Physics - Dzyaloshinskii–Moriya interaction, DMI in short, represents an antisymmetric type of magnetic interactions that favor orthogonal orientation...     

Reductive Amination of Carbonyl Compounds over a Ni2P/SiO2 Catalyst in a Flow Mode

Kinetics and Catalysis - The catalytic properties of a nickel phosphide catalyst supported on silica gel in the reductive amination of carbonyl compounds were studied in a flow reactor using...     

A Novel Biaryl Derivative from Hydrangea chinensis

Chemistry of Natural Compounds - A novel bibenzyl derivative, hydrangchinenin (1), was isolated from the stems of Hydrangea chinensis Maxim. (Hydrangeaceae). Its structure was determined on the...     

Investigation of Relationships between Grain Structure and Inhomogeneous Deformation by Means of Laboratory-Based Multimodal X-Ray Tomography: Strain Accuracy Analysis

Experimental Mechanics - The internal strain distribution developing during plastic deformation is important for understanding the mechanical properties of polycrystalline materials. Such...     

Secondary Metabolites of Litchi chinensis

Chemistry of Natural Compounds -     

Preparation and Properties of Sr3B2O6:Dy3+, Eu3+ White Phosphors Using the High-Temperature Solid-State Method

Journal of Applied Spectroscopy - Sr3B2O6:Dy3+, Eu3+ single-matrix white-light-emitting materials are prepared using the high-temperature solid-state method. The microstructure, emission spectrum,...     

A Neural Network-Based Policy Iteration Algorithm with Global $$H^2$$ -Superlinear Convergence for Stochastic Games on Domains

In this work, we propose a class of numerical schemes for solving semilinear Hamilton–Jacobi–Bellman–Isaacs (HJBI) boundary value problems which arise naturally from exit time problems of diffusion processes with controlled drift. We exploit policy iteration to reduce the semilinear problem into a sequence of linear Dirichlet problems, which are subsequently approximated by a multilayer feedforward neural network ansatz. We establish that the numerical solutions converge globally in the \(H^2\)-norm and further demonstrate that this convergence is superlinear, by interpreting the algorithm as an inexact Newton iteration for the HJBI equation. Moreover, we construct the optimal feedback controls from the numerical value functions and deduce convergence. The numerical schemes and convergence results are then extended to oblique derivative boundary conditions. Numerical experiments on the stochastic Zermelo navigation problem are presented to illustrate the theoretical results and to demonstrate the effectiveness of the method.

     

Robustness of the Spin-Navigator Based Method for the Manipulation of the Beam Polarization Axis in a Spin-Transparent Storage Ring

Physics of Atomic Nuclei - We have investigated the robustness of the spin-navigator based method for manipulating the beam polarization to bend magnet installation errors. Toward that end,...     

Highly Proton-Conductive Zinc Metal-Organic Framework Based On Nickel(II) Porphyrinylphosphonate

The design of new solid-state proton-conducting materials is a great challenge for chemistry and materials science. Herein, a new anionic porphyrinylphosphonate-based MOF ( IPCE-1Ni ), which involves dimethylammonium (DMA) cations for charge compensation, is reported. As a result of its unique structure, IPCE-1Ni exhibits one of the highest value of the proton conductivity among reported proton-conducting MOF materials based on porphyrins (1.55×10⁻³ S cm⁻¹ at 75 °C and 80 % relative humidity).