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Exploring nonlinear Rashba effect and spin Hall conductivity in Janus MXenes W2COX (X = S, Se, Te)

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arXiv 2024年

作者： Bordoloi, Arjyama Singh, Sobhit Department of Mechanical Engineering University of Rochester RochesterNY14627 United States Materials Science Program University of Rochester RochesterNY14627 United States

Rashba spin-orbit coupling (RSOC) facilitates spin manipulation without relying on an external magnetic field, opening up exciting possibilities for advanced spintronic devices. In this work, we examine the effects of crystal momentum (k) nonlinearity and anisotropy on the conventional Rashba effect, with a particular focus on their impact on the spin Hall conductivity (SHC) in a newly predicted family of 2D Janus materials, W2COX (X = S, Se, Te). Using first-principles density functional theory calculations, we confirm the dynamical and mechanical stability of the studied 2D materials. Strikingly, this materials family exhibits pronounced nonlinear Rashba spin splitting at the Γ point of Brillouin zone near the Fermi level, which cannot be adequately described by the linear-k Rashba model. Therefore, third-order momentum contributions (k3) must be incorporated into the Rashba Hamiltonian. Our analysis reveals that among the studied systems, W2COS exhibits the highest k3 contribution of −45.9 eV Å3, despite having the lowest linear Rashba constant. A detailed analysis of electronic structure reveals topological nontrivial behaviour in these 2D materials, yielding sizable SHC that is primarily governed by the nonlinear Rashba effect. Notably, these materials also exhibit large spin Hall angle (0.018-2.5), which is comparable to that of in bulk topological insulators like Bi2Se3 and Bi2Te3, and surpassing those in narrow bandgap bulk semiconductors GeTe and SnTe, as well as heavy metals such as Pt. Sizable SHC, large spin Hall angles, and the ability to tune SHC via electric fields without altering the topological properties, rooted in the crystal field splitting, underscore the potential of these materials for spintronic applications. © 2024, CC BY.

关键词： Spin Hall effect

First-principles investigation of elastic, vibrational, and thermodynamic properties of kagome metals CsM3Te5 (M = Ti, Zr, Hf)

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arXiv 2024年

作者： Wei, Yifan Bordoloi, Arjyama Chuang, Chaon-En Singh, Sobhit Department of Mechanical Engineering University of Rochester RochesterNY14627 United States Materials Science Program University of Rochester RochesterNY14627 United States

Kagome metals are a unique class of quantum materials characterized by their distinct atomic lattice arrangement, featuring interlocking triangles and expansive hexagonal voids. These lattice structures impart exotic properties, including superconductivity, interaction-driven topological many-body phenomena, and magnetism, among others. The kagome metal CsM3Te5 (where M = Ti, Zr, or Hf) exhibits both superconductivity and nontrivial topological electronic properties, offering a promising platform for exploring topological superconductivity. This study employs first-principles density functional theory calculations to systematically analyze the elastic, mechanical, vibrational, thermodynamic, and electronic properties of CsM3Te5 (M = Ti, Zr, Hf). Our calculations reveal that the studied compounds - CsTi3Te5, CsZr3Te5, and CsHf3Te5 - are ductile metals with elastic properties akin to the hexagonal Bi and Sb, with average elastic constants, including a bulk modulus of 27 GPa, a shear modulus of 11 GPa, and Young's modulus of 29 GPa. We observe peculiar dispersionless, flat, phonon branches in the vibrational spectra of these metals. Additionally, we thoroughly analyze the symmetries of the zone-center phonon eigenvectors and predict vibrational fingerprints of the Raman- and infrared-active phonon modes. The analysis of thermodynamic properties reveals the Einstein temperature for CsTi3Te5, CsZr3Te5, and CsHf3Te5 to be 66, 54, and 53 K, respectively. Our orbital-decomposed electronic structure calculations reveal significant in-plane steric interactions and multiple Dirac band crossings near the Fermi level. We further investigate the role of spin-orbit coupling effect on the studied properties. This theoretical investigation sheds light on the intriguing quantum behaviour of kagome metals. © 2024, CC BY.

关键词： Spin orbit coupling

Solute Clustering in Polycrystals: Unveiling the Interplay of Grain Boundary Junction and Long-Range Solute Attraction Effects

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SSRN

SSRN 2024年

作者： Nenninger, Tara Sansoz, Frederic Department of Mechanical Engineering The University of Vermont BurlingtonVT05405 United States Materials Science Program The University of Vermont BurlingtonVT05405 United States

Spectral analysis of local atomic environments has become a powerful tool for studying solute atom segregation and interactions at grain boundaries in nanocrystalline alloys. When applied to individual grain boundaries, the spectral analysis has shown that solute-solute interaction can be either attractive or repulsive, with long-range relative attraction enhancing the likelihood for solute cluster nucleation. In this article, we combine this analysis with a new grain-boundary structure descriptor based on grain-boundary atom coordination, to investigate the impact of grain-boundary junctions on solute atom segregation in polycrystals. Specifically, we systematically characterize the nucleation tendency of solute clusters at various types of ordinary grain boundaries, triple junctions, and high-order junctions in Ag polycrystals containing either Ni or Cu solute atoms. Our findings demonstrate that the nucleation of solute clusters at grain boundaries is primarily driven by long-range relative solute attraction, rather than short-range solute-solute interactions. This effect is most pronounced near grain-boundary junctions. Our study highlights the multiscale nature of solute segregation at crystalline interfaces and provides new insights into the complex phenomena governing heterogeneous solute segregation in grain-boundary networks. © 2024, The Authors. All rights reserved.

关键词： Nanocrystalline alloys

A Physics-Constrained Deep Learning Treatment of Runaway Electron Dynamics

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arXiv 2024年

作者： McDevitt, Christopher J. Arnaud, Jonathan Tang, Xian-Zhu Nuclear Engineering Program Department of Materials Science and Engineering University of Florida GainesvilleFL32611 United States Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States

An adjoint formulation leveraging a physics-informed neural network (PINN) is employed to advance the density moment of a runaway electron (RE) distribution forward in time. A distinguishing feature of this approach is that once the adjoint problem is solved, its solution can be used to project the RE density forward in time for an arbitrary initial momentum space distribution of REs. Furthermore, by employing a PINN, a parametric solution to the adjoint problem can be learned. Thus, once trained, this adjoint-deep learning framework is able to efficiently project the RE density forward in time across various plasma conditions while still including a fully kinetic description of RE dynamics. As an example application, the temporal evolution of the density of primary electrons is studied, with particular emphasis on evaluating the decay of a RE population when below threshold. Predictions from the adjoint-deep learning framework are found to be in good agreement with a traditional relativistic electron Fokker-Planck solver, for several distinct initial conditions, and across an array of physics parameters. Once trained the PINN thus provides a means of generating RE density time histories with exceptionally low online execution time. © 2024, CC BY.

关键词： Fokker Planck equation

A Physics-Informed Deep Learning Description of Knudsen Layer Reactivity Reduction

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arXiv 2024年

作者： McDevitt, Christopher J. Tang, Xian-Zhu Nuclear Engineering Program Department of Materials Science and Engineering University of Florida GainesvilleFL32611 United States Theoretical Division Los Alamos National Laboratory Los Alamos NM87545 United States

A physics-informed neural network (PINN) is used to evaluate the fast ion distribution in the hot spot of an inertial confinement fusion target. The use of tailored input and output layers to the neural network is shown to enable a PINN to learn the parametric solution to the Vlasov-Fokker-Planck equation in the absence of any synthetic or experimental data. As an explicit demonstration of the approach, the specific problem of Knudsen layer fusion yield reduction is treated. Here, predictions from the Vlasov-Fokker-Planck PINN are used to provide a non-perturbative solution of the fast ion tail in the vicinity of the hot spot thus allowing the spatial profile of the fusion reactivity to be evaluated for a range of collisionalities and hot spot conditions. Excellent agreement is found between the predictions of the Vlasov-Fokker-Planck PINN and results from traditional numerical solvers with respect to both the energy and spatial distribution of fast ions and the fusion reactivity profile demonstrating that the Vlasov-Fokker-Planck PINN provides an accurate and efficient means of determining the impact of Knudsen layer yield reduction across a broad range of plasma conditions. © 2024, CC BY.

关键词： Deep learning

An Efficient Surrogate Model of Secondary Electron Formation and Evolution

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arXiv 2024年

This work extends the adjoint-deep learning framework for runaway electron (RE) evolution developed in Ref. [1] to account for large-angle collisions. By incorporating large-angle collisions the framework allows the avalanche of REs to be captured, an essential component to RE dynamics. This extension is accomplished by using a Rosenbluth-Putvinski approximation to estimate the distribution of secondary electrons generated by large-angle collisions. By evolving both the primary and multiple generations of secondary electrons, the present formulation is able to capture both the detailed temporal evolution of a RE population beginning from an arbitrary initial momentum space distribution, along with providing approximations to the saturated growth and decay rates of the RE population. Predictions of the adjoint-deep learning framework are verified against a traditional RE solver, with good agreement present across a broad range of parameters. © 2024, CC BY.

关键词： Electrons

Exchange-correlation potential built on the derivative discontinuity of electron density

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arXiv 2024年

作者： Huang, Chen Department of Scientific Computing Materials Science and Engineering Program National High Magnetic Field Laboratory Florida State University TallahasseeFL32306 United States

Electronic structures are fully determined by the exchange-correlation (XC) potential. In this work, we develop a new method to construct reliable XC potentials by properly mixing the exact exchange and the local density approximation potentials in real space. The spatially dependent mixing parameter is derived based on the derivative discontinuity of electron density and is first-principle. We derived the equations for solving the mixing parameter and proposed an approximation to simplify these equations. Based on this approximation, this new method gives reasonable predictions for the ionization energies, fundamental gaps, and singlet-triplet energy differences for various molecular systems. The impact of the approximation on the constructed XC potentials is examined, and it is found that the quality of the XC potentials can be further improved by removing the approximation. This work demonstrates that the derivative discontinuity of electron density is a promising constraint for constructing high-quality XC potentials. © 2024, CC BY.

关键词： Local density approximation

Lost-Core Injection Molding of Fluidic Elastomer Actuators for the Fabrication of a Modular Eel-Inspired Soft Robot

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Lost-Core Injection Molding of Fluidic Elastomer Actuators f...

IEEE International Conference on Soft Robotics (RoboSoft)

作者： Jacobo Cervera-Torralba Yuxiang Kang Eesa M. Khan Iman Adibnazari Michael T. Tolley Department of Mechanical and Aerospace Engineering University of California San Diego (UCSD) La Jolla CA USA Materials Science and Engineering Program University of California San Diego (UCSD) La Jolla CA USA

Biologically inspired soft anguilliform swimming robots show great promise in underwater exploration. Their soft bodies promise to reduce the chance of harming humans or wildlife with which they come into contact, and also to reduce their chance of becoming stuck in complex environments. Furthermore, the efficiency of anguilliform swimming may enable long duration operation. However, the design and fabrication of soft anguilliform swimming robots remain challenging. Here we present a design concept for a modular soft anguilliform robot. To address the challenge of consistently fabricating modules for this design, we also present a new fabrication method that combines injection molding and lost-core molding for the consistent fabrication of bi-directional fluidic elastomer actuator modules. We evaluate the consistency of the fabrication method through visual inspection, weight measurements, bending angle measurements and the measurement of force output of the actuator. This work represents a step towards an autonomous eel-inspired soft robot, as well as a new fabrication approach that may enable a number of other new soft robotic systems.

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Discrete fracture network application to rock slope engineering

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Journal of Industrial Safety

Journal of Industrial Safety 2025年第1期2卷 47-64页

作者： Elvis Karikari Mensah Reginald Hammah Hassan Basahel Hani Mitri Department of Mining and Materials Engineering McGill University Montreal Quebec H3A 0E8 Canada Rocscience Ltd. 54 Saint Patrick Street Toronto Ontario M5T 1V1 Canada Geological Hazards Program Saudi Geological Survey Jeddah 21514 Saudi Arabia

The stability of rock slopes is a critical concern in rock engineering applications due to the potential safety hazards and economic repercussions associated with slope failures. Currently, slope stability analysis relies on various analytical and numerical modeling tools leading to a factor of safety. Parameters such as joint strength are assessed through probabilistic analysis. Despite the established effectiveness of these methods, they often lack the capability to provide a volumetric estimation of the failure zone, primarily because the joint frequency parameter is not considered. By incorporating this parameter, it becomes feasible to construct a three-dimensional discrete fracture network (DFN), which offers a more comprehensive representation of the rock slope structure. This study aims to validate the use of DFN in civil engineering applications using two road-cut case studies from Saudi Arabia. Both case studies are analyzed with the conventional methods and DFN approaches, allowing for a comparative assessment of results. DFN models optimize the utilization of statistical data related to discontinuity persistence and spacing, enabling the construction of both deterministic and stochastic fracture networks. The effects of joint spacing and persistence on the factor of safety and volume of failure are examined. Finally, the advantages and limitations of the DFN on rock slope stability are highlighted and areas for improvement and optimization are discussed.

关键词： Discrete fracture network Rock slope engineering Numerical modeling Rock mechanics Mathematical modeling Limit equilibrium Kinematic analysis