The European Physical Journal B最新文献

We have extensively studied the structural, elastic, mechanical, thermodynamic, electronic, and magnetic properties of Pd-based rare-earth full-Heusler alloys Pd₂YIn (Y = Gd, La) and their quaternary alloys Pd₂Gd_xLa_1−xIn (x = 0.75, 0.5, and 0.25) using the full-potential linearized augmented-plane wave (FP-LAPW) approach in the generalized gradient (GGA) approximation based on the WIEN2k program. Our findings offer a theoretical investigation of the combined Heusler alloys Pd₂Gd_xLa_1−xIn, in which most of their properties studied here have not yet been determined. It is demonstrated that the estimated equilibrium lattice constants and spin magnetic moments and the experimental values of the parent alloys agree well. The electronic structure shows that our compounds have a metallic ferromagnetic nature, except for the Pd₂LaIn alloy, which is not ferromagnetic. Furthermore, the mechanical results indicate that the studied compounds are mechanically stable, anisotropic, and exhibit ductile behavior. Consequently, it is anticipated that Pd₂Gd_xLa_1−x In Heusler alloy will be interesting candidates in spintronic devices and transport applications.

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This work investigates Nd_0.7Ca_0.3MnO₃ (NCMO) samples, which were prepared using solid-state reaction and annealed at different temperatures (700, 800, and 900 °C). Our studies showed variations in impedance (Z), conductivity (σ), dielectric constant (ε), dielectric loss factor, and tan(δ) with frequency. From the analysis of AC conductivity, the semimetal behavior of the material was apparent when the annealing temperature increased. Impedance measurements showed characteristics of a capacitor’s dielectric material. The optical parameters of the NCMO samples were calculated from transmittance and reflectance measurements. The optical study showed that the samples have an indirect band energy gap. The resulting E_g values were 0.89, 0.94, 0.93, and 0.91 eV for the as-prepared sample and samples annealed at 700, 800, and 900 °C, respectively. With these energy gap values, the samples are suitable for photocatalysis applications in the visible light range, thus offering a low-cost alternative.

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The energy gap values make these samples suitable for photocatalysis applications in the visible light range, which is considered a low-cost application.

We investigate the nonlinear dynamics of the deterministic and stochastic Hindmarsh–Rose (HR) neuron models under the influence of an external sinusoidal electric current and magnetic flow effects. In the deterministic regime, in the absence of a magnetic field, we observe multistability between periodic and chaotic attractors. This is accompanied by the emergence of self-similar windows of chaotic dynamics that converge within a broad domain of periodic dynamics in parameter space. Introducing a magnetic flux partially suppresses chaotic dynamics while maintaining multistability. Under stochastic conditions due to the introduction of Gaussian noise with arbitrarily small intensity, D, noise triggers transitions between coexisting states, exhibiting a preference for specific attractors from the deterministic case without returning to any other coexisting metastable states. By increasing D and appropriately adjusting the remaining control parameters of the HR neuron model, it becomes feasible to achieve regimes of noise-induced chaos or noise-induced stabilization, effectively suppressing chaotic dynamics. Furthermore, within this framework, we explore the existence of transient chaotic dynamics.

In this study, we delve into the realm of solving the Schrödinger equation within spherical quantum dots (QDs) characterized by arbitrary potentials, leveraging the capabilities of machine learning methodologies. Our approach involves training neural networks (NNs) through a curated collection of potentials and wave functions (WFs), which were initially computed using the classical finite element method. To gauge the reliability of the estimates produced by these NNs, we introduce accuracy indicators for rigorous assessment. The training procedure relies on the gradient descent method to optimize the networks’ performance. Furthermore, our investigation encompasses scenarios with analytical potentials, broadening the scope of our analysis beyond empirical cases. By integrating analytical potentials into our study, we aim to achieve a comprehensive understanding of the neural network’s effectiveness in handling various potential profiles. This expansion opens avenues for more versatile and insightful quantum mechanical explorations within the realm of nanoscale systems. Among the findings, the QD core exhibited the highest level of accuracy in WF estimation, achieved through the utilization of a spherical potential. Conversely, the estimation performance was least reliable in scenarios involving HLP, with a notable deviation of 16.68%. Transitioning to the core/shell structure, employing the double HLP configuration resulted in the most precise estimation of WFs. This contrasts significantly with the estimation performance for the V-Shaped Potential, where accuracy was comparatively lower with deviation of 4%.

Graphical Abstract

In this work, we solved the Schrödinger equation within spherical quantum dots characterized by arbitrary potentials, leveraging the capabilities of machine learning methodologies. Our approach includes training neural networks through a curated collection of potentials and wave functions, which were initially computed using the classical finite element method. By integrating analytical potentials into our study, we aim to achieve a comprehensive understanding of the neural network’s effectiveness in handling various potential profiles. The estimation performance was least reliable in scenarios involving HLP, with a notable deviation of 16.68%. Transitioning to the core/shell structure, employing the double HLP configuration resulted in the most precise estimation of WFs. This contrasts significantly with the estimation performance for the V-Shaped Potential (VP), where accuracy was comparatively lower with deviation of 4%