Current Applied Physics最新文献

This study investigates the long-term thermal stability (4 h at 300 °C) of active contacts in high electron mobility transistor (HEMT) devices with and without 10 nm-target thickness chemical vapor deposition (CVD) carbon (C) layers, utilizing in-situ resistance measurements. The thermal stability of the HEMT devices was investigated over the temperature range of 150–300 °C, employing sputtered 30 nm Ti/150 nm Al/30 nm Ti/30 nm TiN (top) with Ohmic metal structures. The results indicate that the HEMT devices with the about 10 nm-thick CVD C layer exhibit superior long-term thermal stability compared to those without the CVD C layer, maintaining Ohmic contact behavior throughout the duration of the test. The increase in contact resistance without C films was 2 %, whereas with the C films, it was only 0.5 % after the 4 h-long thermal stability test at 300 °C.

The spin-orbit correlation in ferromagnet (FM) is an important factor that affects the orbital torque efficiency in the FM. We investigate the spin-orbit correlation in FM alloys, ${\text{Co}}_x{\text{Fe}}_{1-x}$ and ${\text{Ni}}_x{\text{Fe}}_{1-x}$, with varying their composition. We find spots where the spin-orbit correlation is significantly strong near the Fermi surface in ${\text{Co}}_{0.125}{\text{Fe}}_{0.875}$, ${\text{Co}}_{0.25}{\text{Fe}}_{0.75}$, ${\text{Co}}_{0.875}{\text{Fe}}_{0.125}$, and ${\text{Ni}}_{0.5}{\text{Fe}}_{0.5}$, while no such spot appears in ${\text{Co}}_{0.5}{\text{Fe}}_{0.5}$, and ${\text{Ni}}_{0.75}{\text{Fe}}_{0.25}$. These results imply that in the former structures, the orbital polarized current injected into these spots can provide a strong torque to the magnetization of the FM through the orbital torque mechanism. These results also show that even in the same alloy system, the difference in alloy composition can lead to different orbital torque efficiency.

In advancing photovoltaic technology, optimizing the metallization process is crucial for balancing electrical conductivity and optical performance in solar cell fabrication. This process directly impacts the efficiency and quality of solar cells, traditionally measured by the fill factor (FF). Historically, efforts have focused on evolving metal contacts to reduce optical shading and series resistance, which degrade solar cell efficiency. Our study enhances n-type Tunnel Oxide Passivated Contact (n-TOPCon) solar cells by optimizing screen-printing metallization, particularly by examining the effects of squeegee speeds. Employing a mix of experimental and analytical methodologies, we aimed to identify optimal conditions that improve electrical and optical performance, thereby elevating cell efficiency. Our findings indicate that a squeegee speed of 170 mm/s substantially boosts solar cell performance, evidenced by a current density (J_sc) of 38.96 mA/cm², open-circuit voltage (V_oc) of 684.29 mV, fill factor (FF) of 78.77 %, and a power conversion efficiency (PCE) of 21.00 %. Further, dark I–V measurements confirmed a shunt resistance (R_sh) of 6.25 × 10⁶ Ω and a reduced series resistance (R_s) of 6.48 Ω, underscoring the significance of precise metallization in reducing resistive losses and enhancing efficiency. Future research will explore innovative materials and cutting-edge printing techniques beyond squeegee speed adjustments. The potential incorporation of nanomaterials and conducting polymers aims to refine the metallization process further, promising to push the boundaries of efficiency and cost-effectiveness. This progression is essential for advancing n-TOPCon solar cell development, setting new industry standards, and propelling the sustainable energy movement.

Dual-stage scanners can successfully increase the range of atomic force microscope (AFM) scanners in space and/or frequency by designing and constructing two nanopositioners with significant differences in static and dynamic characteristics. In such cases, the positioning signal has to be split to meet the displacement and frequency limitations of each stage. Without closed-loop displacement measurement sensors, linearizing images acquired using signals split in time and frequency can be challenging. A common method, often cumbersome and time consuming, uses inverse model-based feedforward compensation. In this work, we show that image-based linearization can be used to achieve acceptable results for raster scans acquired using dual-stage scanners. In particular, we apply the feedforward and image-based methods to our homemade dual-stage lateral scanner and high-speed AFM (HS-AFM) system. The acquired scans compare well for the two methods, indicating that image-based raster scan linearization can be used in place of inverse model-based feedforward approaches.

Silver telluride, Ag₂Te, was synthesized on various 2D templates of carbon nanotubes (CNT) and transition-metal dichalcogenides (WTe₂ and VTe₂) via hydrothermal synthesis. X-ray diffraction (XRD) and Raman spectroscopy revealed that Ag₂Te was synthesized on the 2D templates under optimal synthesis conditions. Differential scanning calorimetry (DSC) confirmed that the structural phase transition of Ag₂Te underwent endothermic and exothermic reactions at approximately 410 and 425 K, respectively. Among various metallic templates, a large amount of Ag₂Te has been synthesized on strongly acidic CNT. X-ray absorption near-edge structure (XANES) revealed that the chemical state of the metallic support influenced the electronic structure of the as-grown Ag₂Te on the metallic supports. Our findings demonstrated that the hybrid 2D catalysts with an appropriate 2D metallic template can be a promising strategy for achieving efficient catalysis.

Functionalized graphene and carbon nanotubes (CNTs) are widely recognized for their exceptional physical properties, which make them highly suitable for various applications. Although molecular dynamics (MD) simulations are essential for investigating the atomic-level interactions and transport phenomena in functionalized graphene and CNT systems, setting up these simulations remains complex and time-consuming. To streamline this process, we have developed a novel web application that automates the generation of MD simulation setups of functionalized graphene and CNT systems compatible with AMBER force fields and the Gromacs software. Key features include the creation of nanopores, functionalization with hydrogen, hydroxyl, and/or carboxylate groups, and the application of periodic boundary conditions to effectively simulate infinite structures. To facilitate the MD analysis of transport phenomena through nanopores, our web application offers an automated analysis tool that generates and visualizes three-dimensional local flux fields from MD trajectories. Overall, our web applications significantly enhance the accessibility and efficiency of MD simulations of functionalized graphene systems, particularly for nanopore applications. Our web applications are freely available at https://yoo.skku.edu/apps.

Choosing FA⁺/MA⁺ mixed-ion perovskite material as the active layer of the perovskite solar cells hold out the prospect of high efficiency and high stability. In this paper, the FA⁺/MA ⁺ ratio is modified to optimize the performance of FA_xMA_1-xPbI₃-based perovskite solar cells utilizing SCAPS-1D. The results indicate that the PCE of the devices reached a minimum of 21 % when x is greater than or equal to 0.6 in FA_xMA_1-xPbI₃(FTO/NiO_x/FA_xMA_1-xPbI₃/C₆₀/BCP/Al). When x = 0.6, the impact of the bottom interface defects on the solar cell performance is dominant and the thickness of the active layer should be controlled within a range of 0.4 μm–0.8 μm, while the defect density of the layer is expected to be between 10¹³ 1/cm³ and 10¹⁴ 1/cm³. By optimizing the perovskite layer thickness, defect density and changing the type of back electrode material, the efficiency is reaching 25.74 % based on the device(FTO/NiO_x/FA₆MA₄PbI₃/C₆₀/BCP/Au). This study provides theoretical guidance for experimental research on interface modification, defect passivation and manufacturing of high-efficiency FA_xMA_1-xPbI₃-based perovskite solar cells.

Doping is one of the effective strategies to modulate the optoelectronic properties and photocatalytic activity of photocatalysts. In this study, the effect of Fe doping (0–10 %) on morphology, optical and electronic properties, and photocatalytic activity of ZnO nanostructures is studied. The X-ray diffraction analysis shows that >5 % Fe doping, ZnFe₂O₄ is segregated as a secondary phase. The crystalline size decreases from 50.8 nm to 21.4 nm and the micro-strain increases with increasing the Fe concentration. The Fe doping-induced electronic restructuring facilitates visible light absorption through the O 2p → Fe 3d transition and the suppression of charge recombination by efficiently trapping conduction band electrons. Density functional theory (DFT) calculations are employed to unravel the underlying electronic changes induced by Fe doping in ZnO. The formation of shallow donor levels below the conduction band originates from the Fe 3d state. Photoluminescence spectra of pristine and Fe-doped ZnO nanostructures show characteristic emission peaks at approximately 384 nm and 570 nm, indicating the recombination of free excitons and oxygen interstitial defects, respectively. The results of the photocatalytic activity tests confirm that the 1 % Fe-doped ZnO nanostructures can exhibit the highest efficiency compared to the heavily doped ZnO nanostructures. The high efficiency in photocatalytic activity of 1 % Fe-doped ZnO nanostructures is ascribed to the modulated electronic structure and defect density. The adsorption affinity of methylene blue and water molecules to the surfaces of pristine and Fe-doped ZnO is simulated using the Monte-Carlo method. This study emphasizes the importance of controlling the dopant concentration to enhance the photocatalytic activity of various photocatalysts.

The density functional theory (DFT) data-driven approach to generating potential energy surfaces using machine learning has been proven to quickly and accurately predict the molecular and crystal structures of various elements. However, training databases consisting of hundreds of well-known symmetric structures have shown fatal weaknesses in calculating amorphous or nano-scale structures. Ab-initio molecular dynamics (AIMD) simulations create a training set that compensates for these shortcomings, but there are still many rare event structures. Here we introduce a new method to easily enlarge the data diversity and dramatically reduce data points based on the highly defected nano structures for universal machine learned potential. Our potential applies to bulk and nano systems and has been shown to high accuracy and computational efficiency while requiring minimal DFT training data. The developed potential is expected to help observation of structural changes in the Pt-based nano-catalysts that have been difficult to simulate at the DFT-level.

In recent years, electrochemical energy storage devices, including lithium batteries, supercapacitors, and fuel cells, have surged in development. They play indispensable roles across various domains and significantly enhance the quality of life. Electrochemical energy storage is vital to power systems, managing supply and demand dynamics, mitigating challenges such as intermittent energy fluctuations, and fostering the sustainable advancement of clean energy solutions. Among burgeoning research avenues, DNA is a green biological macromolecule with biodegradability and a unique double-helix structure, attracting attention across diverse fields. This review discusses the myriad applications of DNA in electrochemical energy storage devices and offers insights into novel approaches to leveraging DNA for electrochemical applications. Exploring these potential applications of DNA may unlock innovative pathways to enhancing the efficiency, sustainability, and versatility of electrochemical energy storage technologies. As these efforts continue, DNA promises to transform the ongoing quest for robust and eco-friendly energy solutions.