Advanced Theory and Simulations最新文献

The paper provides a detailed exploration of piezoelectric transducers in the context of transmission and harvesting ultrasonic energy underwater through integrated theoretical study, finite element analysis, and experimental studies. The piezoelectric transducer is utilized in underwater wireless power transmission (UWPT) research and applications are commonly constructed in the form of a circular wafer. First, this paper demonstrates a way of analyzing maximum potential difference in different piezoelectric materials, such as PZT-based and lithium niobates, in COMSOL Multiphysics software. The purpose of the following analysis is to identify the maximum potential difference obtained in the case where relative permittivity depends on the applied pressure. Second, frequency dependent impedance and efficiency have been determined by driving analytic expressions of the constitutive equations of the electrical equivalent circuit (Thevenin) model. Third, a new type of UWPT process is suggested where a piezoelectric wafer transducer is used to create a connection between the transmitter and the receiver sections in an underwater setting. The ultrasonic transducer of the circular wafer type has been subjected to a finite element analysis (FEA) to assess the stress distribution, electric potential coupling, acoustic pressure fields, sound pressure fields and radiation patterns at varying excitation frequencies (20–80 kHz). Fourth, input and output properties of the proposed model, and electrical equivalent circuit model are simulated in COMSOL software. Lastly, the experimental data proves and validates the simulation and theoretical outcomes. The finding shows that the proposed model is an accurate and comprehensive description of resonance, energy transmission and harvesting in the system. This research improves the performance of Underwater Wireless Power Transfer (UWPT) systems. This is achieved by developing a refined equivalent circuit that precisely models the full process, from ultrasonic wave transmission and piezoelectric reception to final energy harvesting. This study has immersed theoretical implications and practical recommendations for future UWPT studies.

A comprehensive first-principles and machine-learning-assisted study of the oxychalcogenide BaTa₄Te₃O₁₇, highlighting its promise as a multifunctional material for thermoelectric, optoelectronic, and photocatalytic applications. Density functional theory (DFT) calculations show a direct bandgap of 3.3 eV with mixed dispersive and flat valence/conduction states, promoting anisotropic carrier transport. The effective electron and hole masses along the Γ–Γ direction exhibit more balanced carrier masses (m_e^* = 0.682 m₀, m_h^* = 0.714 m₀), corresponding to a reduced mass of 0.348 m₀, a binding energy of 260 meV, and a Bohr radius of 6.48 Å, signifying weaker exciton confinement. Thermoelectric analysis yields a Seebeck coefficient of 1029.23 µV K⁻¹ and a figure of merit ZT ≈ 0.94 at 300 K, improving at higher temperatures. The band-edge positions align well with the hydrogen evolution potential, suggesting photocatalytic suitability. To complement DFT results, supervised regression models (XGBoost and ensemble-stacking) predict E_g ≈ 3.3 eV (R² = 0.55) and ZT ≈ 0.94 with >90% accuracy. This integrated DFT–ML framework demonstrates a cost-effective route for screening and optimizing heteroanionic oxychalcogenides for next-generation energy and electronic applications.

Sodium antiperovskites have emerged as promising cathode materials for next-generation sodium-ion batteries owing to their structural flexibility and high sodium content. In this work, we present a comprehensive first-principles investigation of point defects and sodium diffusion in defect containing Na-rich antiperovskites ($��{\mathrm{Na}}_2�\mathrm{TMSO}�$, where TM = 3d transition metals). The formation energies of Frenkel (Na, S, O, and transition metal) and Schottky ($��{\mathrm{Na}}_2�O�$ and $��{\mathrm{Na}}_2�S�$) defect pairs are systematically evaluated under neutral charge conditions to elucidate the intrinsic defect chemistry and thermodynamic stability. Among all defect types, TM-Frenkel and Na-Frenkel pairs exhibit the lower formation energies, suggesting their predominance under equilibrium conditions. The energy above hull calculation (2–97 eV/atom) suggests that the Na-Frenkel defect containing structures are metastable. The influence of Na-Frenkel defects on sodium migration is further explored through diffusion pathway analysis using climbing image nudged elastic band method, revealing reduced activation barriers and enhanced Na mobility in the defected lattice, especially along (011) direction. Finally, interfacial thermodynamics between these Na-antiperovskite cathodes with Na-Frenkel defect pairs and representative solid electrolytes are examined to assess chemical compatibility and interphase stability. These results provide atomistic insight into defect-mediated ionic transport and interfacial stability, guiding the design of high-performance Na-antiperovskite-based cathodes.

In materials science, Machine learning (ML) has emerged as a powerful and efficient method for predicting and optimizing material properties. However, the widespread use of default hyperparameter approaches presents challenges in ensuring model reliability. This study systematically analyses the hyperparameter optimization (HPO) framework, using carbon dots (CDs) as a representative material system. Common HPO strategies, including Grid Search, Random Search, and Bayesian Optimization, are comparatively evaluated using cross-validation. These methods were evaluated to assess their effects on prediction accuracy and computational efficiency. The results reveal that HPO significantly improves model generalization and stabilization under limited data conditions, especially over tree-based algorithms that are sensitive to parameter adjustment. Furthermore, initial screening with default configurations has also proven necessary for the purposeful implementation of HPO. This study demonstrates the critical role of structured HPO in ensuring reproducible, reliable ML models for materials and provides generalizable insights broadly applicable to theoretical materials research.

This study employs an integrated multiscale simulation strategy that combines machine learning potential, molecular dynamics, and density-functional tight-binding methods to investigate the temperature- and size-dependent structural transitions and electronic state evolution in titanium nanoparticles (2.2–4.4 nm). The aim is to provide atomic-level insight into the relationship between structural rearrangements and electronic properties during thermal processes. Analysis of packing patterns reveals distinct structural transformation pathways across different particle sizes. The potential energy and its difference reveal a transition temperature range (400–1450 K) that marks significant changes in atomic thermal vibration, strongly influenced by particle size. Fermi energy and the energy gaps between the highest occupied molecular orbital and the lowest unoccupied molecular orbital illustrate key electronic behavior trends, showing a decrease in the energy gap with increasing temperature and particle size, eventually approaching zero for larger nanoparticles, indicating enhanced metallic character. Mulliken charge distributions further exhibit a characteristic core-shell spatial pattern, while charge density differences emphasize surface electron depletion and internal charge redistribution, which vary systematically with nanoparticle size.

Departing from conventional band theory, topological materials are classified by topological invariants, a fundamental property that reliably ensures the presence of robust boundary states. This intrinsic characteristic renders their surface electrons immune to disorder-induced localization. We delve into the theoretical underpinnings of this topological protection in NaCdBi. Our first-principles calculations reveal Pnma NaCdBi as a potential topological material (TM) exhibiting a distinct Dirac-like crossing point at the surface along the $�\Gamma$-X path. Furthermore, the value of its topological invariant quantity suggests it to be a non-trivial strong topological material. We point out that NaCdBi shows an anisotropic Spin Hall Conductivity (SHC) with the largest component $�{\sigma }_{�z�y�}^x$ reaching approximately −502 $��(�\hslash �/�e�)�$ S/cm. The findings from our work contribute to understanding the emergence of considerable spin Hall effects in this compound. Anisotropic spin Hall conductivity enables directional control of spin currents, offering a pathway to tunable spintronic devices. In anisotropic magneto-optical materials, this spin manipulation can, in turn, modulate light and control polarization, opening new avenues for photonic applications.

The Riga plate produces a Lorentz force to control boundary layers (BL) and improve cooling purposes for effective electromagnetic flow control in nuclear and aeronautical engineering systems. Furthermore, the synergistic interactions of different nanoparticles optimize heat transfer. A Riga surface is a specialized electromagnetically active device that controls the flow regime by modifying the Hartmann number. Riga surfaces have been used in industrial setups for chemical engineering, biomedicine procedures, and environmental engineering. In addition, the boundary layer controlled by the electromagnetic field provided by the rigid surface is used extensively in the production process of extruding polymers, spinning of metals, and fiberglass production. Advancement in thermal capability of the Riga surface by inducing ternary nanoparticles is necessary to fulfill the demand. So, the primary focus is to investigate the upsurge in thermal capability of the Riga surface by adding ternary nanoparticles, considering activation energy and convective heating. Water-based tri-HNF is used because of its improved thermal characteristics, which are anticipated using the Gharesim model viscosity and Hamilton-Crosser thermal conductivity models. The assumptions of the normal heat and mass fluxes are considered for practical purposes. Computational simulations are executed to handle the developed non-linear mathematical model by using the Runge–Kutta procedure in combination with the shooting approach. The wall friction factor, heat, and mass fluxes are optimized by applying the statistical Response Surface Methodology (RSM) technique. The ideal conditions to improve heat and mass transmission are found using sensitivity analysis, and they are subsequently validated by analysis of variance testing. Sensitivity analysis showed that in a thinner boundary layer, the skin friction increases with an augmentation in nanoparticle concentration. A higher Nusselt number indicates improved heat transfer with increased nanoparticle load. The activation energy uplifts the mass transfer rates, but decreases with nanoparticle concentration.