Applied Research最新文献

This study reports the synthesis of amorphous and polycrystalline molybdenum disulfide (MoS₂) nanostructures. Amorphous MoS₂ nanoparticles were synthesized by desulfurizing MoS₃ under hydrazine vapor at 360°C. Polycrystalline nanosheets were obtained by annealing at 800°C. For comparison, crystalline MoS₂ nanoparticles were also synthesized via a hydrothermal method. The structural and optical properties were characterized using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), photoluminescence (PL), and UV-visible spectroscopy. UV-visible analysis revealed a decrease in the bandgap energy from approximately 2.4 to 2.0 eV, attributed to the change in synthesis method. Annealing significantly influenced the crystallographic and optical properties of MoS₂. The initial desulfurized MoS₃ sample exhibited an amorphous structure, while both the annealed and hydrothermally prepared samples showed a polycrystalline structure.

The ternary nanostructured 0.39BaTiO₃–0.31SrTiO₃–0.30KNbO₃ mol% (BKS) system was prepared via the mechanical milling technique. The composite powders were ball milled for durations of 0.5, 5, 10, and 20 h to facilitate the synthesis of nanostructured materials. XRD at ambient temperature for these nanostructured materials was precisely examined across varying ball milling durations. The characterization and identification of BKS were carried out using FTIR and HRTEM at a milling time 20 h. HRTEM verified the nanoparticle formation, and the mean size of the particles is estimated to be ~13.07 nm. The dielectric parameters were systematically plotted in relation to temperature at varying frequencies. The sample exhibited a wide and dispersed peak at the temperature-dependent dielectric permittivity ɛ′ (T) and loss tangent, as the temperature increased alongside the measuring frequency, indicative of the typical relaxor ferroelectric behavior. Electrical conduction properties of the synthesized BKS were measured through (AC) electrical conductivity at various temperatures. Moreover, the relaxor ferroelectric characteristics evidenced by a P–E hysteresis loop indicate an energy-recovered storage density (W_rec = 13.40 mJ/cm³) and efficiency of about (η = 79%) at 333 K. These findings propose that the nanostructured BKS sample may serve as an applicable candidate for energy preservation technologies.

Frequent subgraph mining (FSM) is one of the most critical procedures for mining meaningful patterns in large and dynamic graph datasets, common in several applications, such as social networks and biological data analysis. Traditional FSM methods are developed primarily with static graphs in mind and, thus, are inefficient when applied to dynamic data, especially data that updates continuously. This paper provides a novel framework of efficient FSM for dynamic network graphs with the support of a four-phase approach involving preprocessing, map, shuffle, and sort, and reduce phases. The hybrid optimization approach developed is known as Golden Dung Graph Hybridization (GDGH) and is a synchronization of Dung Beetle Optimization Algorithm and Golden Jackal Optimization Algorithm to optimize subgraph selection. For subgraph embedding and isomorphism testing, we further conduct a comparative study of several message-passing neural networks. Furthermore, this study conducts extensive experiments on several datasets that show significant superiority over the existing FSM methods in processing time, memory efficiency, and accuracy to demonstrate the efficacy of the proposed framework.

The material AgInSe₂ (AIS) has garnered much attention for the improvement of the power conversion efficiency in solar cells in recent years. To understand how AIS affects the structure of silicon (Si) solar cells, this study numerically compared Si solar cells to Si/AIS solar cell structures using COMSOL Multiphysics. It was discovered that adding AIS to Si improved the shunt resistance, which increased the open-circuit voltage (V_OC) and marginally increased the short-circuit current density (J_SC). The entire effect caused the efficiency to rise from 10.12% to 11.04% with the final structure having a J_SC, V_OC and fill factor of 18.78 mA/cm², 0.694 V and 0.846 respectively. The results indicate that the AIS layer might be crucial to producing extremely efficient solar cells, by improving its shunt resistance. It was also investigated how heating effects occur within the solar cells. Joule heating was discovered to occur at the locations of the p-n junctions, whereas non-radiative recombination heating was found to happen within the first 5 μm of the solar cell. Studying the heating effects inside the cell is crucial to limiting them and enhancing the cell's operational performance. Based on the results gained from this study, AIS can be suggested as an influential material for achieving higher efficiencies within Si solar cells and may therefore provide an effective strategy and source for the manufacture of high-performance solar cells.

The study of friction, wear, and lubrication – traditionally governed by classical physics – is undergoing a transformation with the emergence of quantum tribology, a field where quantum mechanical effects play a pivotal role in surface interactions at the nanoscale. Phenomena such as quantum tunneling, electron–phonon coupling, electron transfer, modifications in atomic orbital interactions, and van der Waals interactions significantly influence tribological behavior, presenting both challenges and opportunities for materials science and engineering. This review explores recent breakthroughs in quantum tribology, including graphene-based lubricants, doped diamond-like carbon coatings, nanoparticle-enhanced coatings, phototribology, structural superlubricity, and self-healing films, which offer promising avenues for reducing energy dissipation and material wear. By leveraging quantum effects, these advancements have the potential to enhance the performance and longevity of tribological systems in industries such as microelectronics, automotive, aerospace, power generation, and nanomanufacturing. Despite these strides, critical hurdles remain, including the need for advanced computational models capable of capturing the intricate quantum mechanisms and experimental techniques capable of capturing and validating quantum-driven tribological phenomena at relevant scales. Addressing these challenges will unlock new frontiers in ultra-low friction technologies, paving the way for more efficient and durable materials working at the atomic and molecular scales.

The limitations of deep learning methods in processing high-resolution inputs can impact the accuracy and efficiency of their results. This study presents a new architectural framework that combines wavelet-based preprocessing with deformable convolutional networks to classify high-resolution histopathological images. Our methodology utilizes multi-resolution wavelet decomposition for efficient feature extraction which maintains diagnostically significant information. This improvement is augmented by deformable convolutions, which improve robustness against geometric transformations of the inputs. Empirical evaluation on the BreaKHis data set shows an image-level accuracy of 96.47% and a patient-level accuracy of 96.55% at 200× magnification. The architecture consistently performs well across different magnification levels, with particular efficiency at higher resolutions where detailed morphological features are essential for accurate diagnosis. Ablation studies support our key architectural contributions, including reduced computational complexity through wavelet-based feature extraction, improved geometric invariance via deformable convolutions, and better classification performance than conventional methods. These findings suggest significant potential for improving diagnostic workflows in clinical settings where pathological expertise may be limited.

Ferroelectric nematic (N_F) liquid crystals (LCs) have garnered substantial interest due to their unique polar ordering, phase transitions, and electro-optic (E-O) properties, with potential applications spanning from low-power reflective displays to advanced photonics and microfluidics. This review explores the multifaceted nature of N_F LCs and comprehensively analyzes their phase behavior, alignment, and surface interactions. Notable findings include the nematic (N)-N_F phase transition's weak first-order nature, surface treatments' role in inducing polar order, and meron-like structures resembling ferromagnetic domains. Furthermore, the review highlights the tunability of N_F LCs under electric fields, offering exciting possibilities for adaptive optics, sensors, and actuators. Despite these advancements, challenges remain in optimizing molecular alignment, controlling defects, and expanding the scalability of N_F LC technologies. By addressing key experimental and theoretical studies, this review aims to present a deeper understanding of N_F liquid crystals' E-O responses, phase transitions, and their potential to revolutionize future LC-based technologies.

This work presents a comprehensive study on the aging behavior of 18650-type lithium-ion batteries, focusing on the uneven intercalation of lithium ions during fast charging processes. It introduces a novel approach using color visual recognition technology to analyze color changes in the graphite anode, indicative of lithiation levels. The study employs X-ray diffraction (XRD) and distribution of relaxation time (DRT) techniques to validate and analyze the observations. The study emphasizes the significance of electrode impedance, the positioning of battery tabs, and electrolyte distribution in influencing the aging dynamics of lithium-ion batteries. Furthermore, the paper presents an innovative impedance Transport-Line Model, specifically developed to capture the evolution of polarization impedance over time. This model offers a deeper understanding of the internal mechanisms driving battery aging, providing valuable insights for the design and optimization of lithium-ion batteries. The research represents a significant contribution to the field, shedding light on the complex aging processes in lithium-ion batteries, particularly under the conditions of fast charging. This could lead to improved battery performance, longevity, and safety, which are critical for the wide range of applications that depend on these energy storage systems.