Structure and magnetic properties of nanostructured Dy/transition-metal multilayered films

We report the results of magnetic and microstructural studies for T/Dy (T=Fe, Co, Ni) compositionally modulated films prepared in a multiple-gun sputtering system. The perpendicular anisotropy and magnetization were measured systematically for X-Å Fe/Y-Å Dy and X-Å Co/Y-Å Dy films. The layer-thickness dependence of the magnetization for Co/Dy and Fe/Dy was interpreted in terms of the antiparallel coupling between transition-metal and Dy magnetic moments. For Co/Dy films the ranges of X and Y required for perpendicular anisotropy were determined. A comparision of the structural and magnetic properties of Ni/Dy, Co/Dy, and Fe/Dy is given and the origin of the perpendicular anisotropy is discussed. Journal of Applied Physics is copyrighted by The American Institute of Physics

Superfluid and magnetic states of an ultracold Bose gas with synthetic three-dimensional spin-orbit coupling in an optical lattice

We study ultracold bosonic atoms with the synthetic three-dimensional spin-orbit (SO) coupling in a cubic optical lattice. In the superfluidity phase, the lowest energy band exhibits one, two or four pairs of degenerate single-particle ground states depending on the SO-coupling strengths, which can give rise to the condensate states with spin-stripes for the weak atomic interactions. In the deep Mott-insulator regime, the effective spin Hamiltonian of the system combines three-dimensional Heisenberg exchange interactions, anisotropy interactions and Dzyaloshinskii-Moriya interactions. Based on Monte Carlo simulations, we numerically demonstrate that the resulting Hamiltonian with an additional Zeeman field has a rich phase diagram with spiral, stripe, vortex crystal, and especially Skyrmion crystal spin-textures in each xy-plane layer. The obtained Skyrmion crystals can be tunable with square and hexagonal symmetries in a columnar manner along the z axis, and moreover are stable against the inter-layer spin-spin interactions in a large parameter region.Comment: 9 pages, 4 figures; title modified, references and discussions added; accepted by PR

Magnetic properties, anisotropy, and microstructure of sputtered rare-earth iron multilayers

A study of compositionally modulated magnetic films of the form Fe/RE, particularly for RE=Nd and Dy, has been performed by vibrating sample magnetometry, ac susceptibility and x-ray diffraction. The relationship between the magnetic properties and the layer thickness was studied systematically for X-Å Fe/Y-Å Dy, as the layer thicknesses X and Y were varied from 1.8 to 20 Å. The ranges of layer thicknesses required for perpendicular anisotropy were determined. The interface and volume anisotropy energies were estimated for X-Å Fe/Y-Å Nd and the differences in the magnetic properties between X-Å Fe/7-Å Dy and X-Å Fe/7-Å Nd are discussed. Journal of Applied Physics is copyrighted by The American Institute of Physics

Geometry Optimization of Self-Similar Transport Network

We optimize geometries of various self-similar transport networks using a three-step strategy based on the entransy theory. Using this optimization method, we obtained optimal relationships of geometric parameters of T-shape networks for fluid flow, heat conduction, convective heat transfer, and other transport phenomena. Some optimization results agree well with the existing theories or experimental data. The optimized transport network structure depends strongly on the optimization objective and the constraints, so that both the maximum heat transfer effect and minimum flow resistance cannot be satisfied at the same time

Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains

Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Several commonly used machine learning (ML) models have been applied to analyze the relationship between Atmospheric Radiation Measurement (ARM) data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains (SGP) ARM site during the summer months (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the random forest model is the best predictor among common classifiers. When convective clouds were detected, it predicts lightning occurrence with an accuracy of 76.9 % and an area under the curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in nowcasting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined for hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables and mid-altitude variables (e.g., equivalent potential temperature and minimum equivalent potential temperature, respectively) have statistically significant contrasts between no-lightning and frequent-lightning hours. For example, the minimum equivalent potential temperature from 700 to 500 hPa is significantly lower during frequent-lightning hours compared with no-lightning hours. Finally, a notable positive relationship between the intracloud (IC) flash fraction and the square root of CAPE (CAPE) was found, suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction.</p

Guiding optical flows by photonic crystal slabs made of dielectric cylinders

We investigate the electromagnetic propagation in two-dimensional photonic crystals, formed by parallel dielectric cylinders embedded a uniform medium. The frequency band structure is computed using the standard plane-wave expansion method, while the propagation and scattering of the electromagnetic waves are calculated by the multiple scattering theory. It is shown that within partial bandgaps, the waves tend to bend away from the forbidden directions. Such a property may render novel applications in manipulating optical flows. In addition, the relevance with the imaging by flat photonic crystal slabs will also be discussed.Comment: 5 pages, 5 figure

The entanglement in one-dimensional random XY spin chain with Dzyaloshinskii-Moriya interaction

The impurities of exchange couplings, external magnetic fields and Dzyaloshinskii--Moriya (DM) interaction considered as Gaussian distribution, the entanglement in one-dimensional random $XY$ spin systems is investigated by the method of solving the different spin-spin correlation functions and the average magnetization per spin. The entanglement dynamics at central locations of ferromagnetic and antiferromagnetic chains have been studied by varying the three impurities and the strength of DM interaction. (i) For ferromagnetic spin chain, the weak DM interaction can improve the amount of entanglement to a large value, and the impurities have the opposite effect on the entanglement below and above critical DM interaction. (ii) For antiferromagnetic spin chain, DM interaction can enhance the entanglement to a steady value. Our results imply that DM interaction strength, the impurity and exchange couplings (or magnetic field) play competing roles in enhancing quantum entanglement.Comment: 12 pages, 3 figure

Impacts of COVID-19 and fiscal stimuli on global emissions and the Paris Agreement

The global economy is facing a serious recession due to COVID-19, with implications for CO2 emissions. Here, using a global adaptive multiregional input–output model and scenarios of lockdown and fiscal counter measures, we show that global emissions from economic sectors will decrease by 3.9 to 5.6% in 5 years (2020 to 2024) compared with a no-pandemic baseline scenario (business as usual for economic growth and carbon intensity decline). Global economic interdependency via supply chains means that blocking one country’s economic activities causes the emissions of other countries to decrease even without lockdown policies. Supply-chain effects contributed 90.1% of emissions decline from power production in 2020 but only 13.6% of transport sector reductions. Simulations of follow-up fiscal stimuli in 41 major countries increase global 5-yr emissions by −6.6 to 23.2 Gt (−4.7 to 16.4%), depending on the strength and structure of incentives. Therefore, smart policy is needed to turn pandemic-related emission declines into firm climate action