High-performance Applications Research Articles

MgOMo2C/multiwalled carbon nanotube composite for high-performance supercapacitor applications

Magnesium molybdate (MgMoO4) nanostructure has been successfully prepared and employed as a catalyst for the production of multi-walled carbon nanotubes (CNTs) by the chemical vapor deposition method. The prepared nanostructure materials are characterized through XRD, TEM, FTIR, BET and Raman spectroscopy. The XRD results and TEM images confirm the formation of the plate-like MgMoO4 structure, as well as the existence of Mo2C and CNTs in the type of bundles structure (CNTBs). The high dispersion and stability of Mo nanoparticles in the composite of plate-like MgMoO4 is responsible for the formation of CNTBs with uniform diameters. Thus, the plate-like MgMoO4 nanoparticles and the MgOMo2C@CNTBs hybrid material are assessed as electrodes in a supercapacitor device for the first time. The produced hybrid supercapacitor MgOMo2C@CNTBs has a good specific capacitance (354 F g−1) and strong cycling stability (82 percent capacity retention over 2000 cycles)

Decoupled High-Mobility Graphene on Cu(111)/Sapphire via Chemical Vapor Deposition.

The growth of high-quality graphene on flat and rigid templates, such as metal thin films on insulating wafers, is regarded as a key enabler for technologies based on 2D materials. In this work, the growth of decoupled graphene is introduced via non-reducing low-pressure chemical vapor deposition (LPCVD) on crystalline Cu(111) films deposited on sapphire. The resulting film is atomically flat, with no detectable cracks or ripples, and lies atop of a thin Cu2O layer, as confirmed by microscopy, diffraction, and spectroscopy analyses. Post-growth treatment of the partially decoupled graphene enables full and uniform oxidation of the interface, greatly simplifying subsequent transfer processes, particularly dry-pick up - a task that proves challenging when dealing with graphene directly synthesized on metallic Cu(111). Electrical transport measurements reveal high carrier mobility at room temperature, exceeding 104cm2V-1s-1 on SiO2/Si and 105cm2V-1s-1 upon encapsulation in hexagonal boron nitride (hBN). The demonstrated growth approach yields exceptional material quality, in line with micro-mechanically exfoliated graphene flakes, and thus paves the way toward large-scale production of pristine graphene suitable for high-performance next-generation applications.

Efficient and Near-Zero Thermal Quenching Cr3+-Doped Garnet-Type Phosphor for High-Performance Near-Infrared Light-Emitting Diode Applications.

In recent years, near-infrared (NIR) phosphors have attracted great research interest due to their unique physical properties and broad application prospects. However, obtaining NIR phosphors with both high quantum efficiency and excellent thermal stability remains a great challenge. In this study, novel NIR Ca3Mg2ZrGe3O12:Cr3+ phosphors were successfully prepared using a high-temperature solid-phase method, and the structure and luminescent properties of the material were systematically investigated. Ca3Mg2ZrGe3O12:0.01Cr3+ emits NIR light in the range of 600 to 900 nm with a peak at 758 nm and a half-height width of 89 nm under the excitation of 457 nm blue light. NIR luminescence shows considerable quantum efficiency, and the internal quantum efficiency of the optimized sample is up to 68.7%. Remarkably, the Ca3Mg2ZrGe3O12:0.01Cr3+ phosphor exhibits a near-zero thermal quenching behavior, and the luminescence intensity of the sample at 250 °C maintains 92% of its intensity at room temperature. The mechanism of high thermal stability has been elucidated by calculating the Huang Kun factor and activation energy. Finally, NIR pc-LED devices prepared from Ca3Mg2ZrGe3O12:0.01Cr3+ phosphor with commercial blue LED chips have good performance, proving that this Ca3Mg2ZrGe3O12:0.01Cr3+ NIR phosphor has potential applications in night vision and biomedical imaging.

Graphene oxide from coconut shells for high-performance supercapacitor application

Globally, the demand for materials with high ultrafast charge rates yet slow discharge continues to rise which has resulted in extensive research into supercapacitors. Although the current supercapacitors can meet such demands, the high processing cost coupled with the use of toxic chemicals remains a major concern. In this study, porous graphene oxide (GO) with a unique morphology was synthesized by an improved Tour’s method for supercapacitor application using coconut shells as a precursor. The structure of the synthesized material consists of partially graphitized walls of porous GO. The surface morphology, the defects created in the GO, and the specific surface area of the as-synthesized materials were studied. When evaluated as an electrode material for supercapacitors, a specific capacitance of 173 F/g at 0.5 A/g in 0.5 M Na2SO4 and 139 F/g at 0.5 A/g in 2 M KOH was recorded. Moreover, a high capacitance retention of 99.3% was recorded after 1000 cycles, confirming the good cyclic stability of the synthesized electrode material. The good electrochemical performance can be attributed to the electrode material’s intrinsic structural and compositional superiorities. These findings indicate the potential of using coconut shells as a precursor for the synthesis of high-performance graphene-based supercapacitor electrodes with improved cost-effectiveness and reduced environmental impact.

Radiation Behavior of Synthesized LiTi Ferrite Based Microstrip Antenna in X Band

The paper comprehensively explores the development, characteristics, and antenna applications of lithium titanium (LiTi) ferrite. Utilising a solid-state reaction technique, the study examines the substrate’s electrical, magnetic, and structural properties in detail. Additionally, it assesses the far-field radiation patterns of a magnetically-biased LiTi ferrite-based antenna. Key findings point to a noticeable reduction in mutual coupling and radiated power, along with an isotropic redistribution of minor side lobes. Comparative analysis with RT-duroid substrates in X band frequency spectrum highlights the LiTi ferrite-based antenna’s remarkable 62.85 % miniaturization and consistent directivity, along with a superior quality factor. These findings emphasise LiTi ferrite’s potential for compact, high-performance applications in demanding environments. LiTi ferrite’s advantages in miniaturisation and stability position it for specific applications, despite trade-offs in bandwidth, gain, and impedance compared to RT-duroid substrates.

Optimizations of Liquid Phase Deposition Processes for Enhanced Photoelectrocatalytic Activities of Tungsten Oxide Thin Films.

This study focuses on the preparation of tungsten oxide (WO3) as the photoanode for water oxidations by the liquid phase deposition (LPD) technique and its optimizations to improve the photoelectrochemical performance. The alternative precursor large stock solution process was achieved to simplify the LPD process for WO3 thin film preparation. The effect of boric acid in the precursor solutions on the physicochemical properties of the deposited WO3 thin films was investigated. As a result, we found that the optimized concentration of boric acid realized the highest photoelectrochemical performance. Through the optimizations of reaction conditions and surface analyses, we concluded that the preparations of a semiconductor film via the LPD technique had the potential to obtain high-performance photoelectrocatalytic applications.

Short-term air quality prediction based on EMD-transformer-BiLSTM

Actual acquired air quality time series data are highly volatile and nonstationary, and accurately predicting nonlinear time series data containing complex noise is an ongoing challenge. This paper proposes an air quality prediction method based on empirical mode decomposition (EMD), a transformer and a bidirectional long short-term memory neural network (BiLSTM), which is good at addressing the ultrashort-term prediction of nonlinear time-series data and shows good performance for application to the air quality dataset of Patna, India (6:00 am on October 3, 2015–0:00 pm on July 1, 2020). The AQI sequence is first decomposed into intrinsic mode functions (IMFs) via EMD and subsequently predicted separately via the improved transformer algorithm based on BiLSTM, where linear prediction is performed for IMFs with simple trends. Finally, the predicted values of each IMF are integrated using BiLSTM to obtain the predicted AQI values. This paper predicts the AQI in Patna with a time window of 5 h, and the RMSE, MAE and MAPE are as low as 5.6853, 2.8230 and 2.23%, respectively. Moreover, the scalability of the proposed model is validated on air quality datasets from several other cities, and the results prove that the proposed hybrid model has high performance and broad application prospects in real-time air quality prediction.

Co-precipitation Synthesis and Characterization Studies of Manganese Oxide Doped with Nickel for High-Performance Energy Storage Supercapacitor Application

The advancement in nanotechnology research has facilitated the development of eco-friendly methods for the synthesis of nanoparticles. In this work, nickel (Ni)-doped manganese oxide was synthesized using the co-precipitation method. The prepared Ni-doped manganese oxide products have been characterized by X-ray powder diffraction, scanning electron microscopy (SEM), transmission electron microscope (TEM), and energy dispersive X-ray spectroscopy (EDS). The preliminary electrochemical characteristics include charge-discharge cycling, which improves the conductivity and capacitance of the high-performance aqueous asymmetrical supercapacitor. The CV analysis of the Ni-doped manganese oxide electrode demonstrated a distinctive pseudocapacitive behavior in 1 M KOH solution. The nickel (Ni) doped electrode has a higher specific capacitance value than the pure manganese oxide electrode, with a value of 2225.07 F×g-1 at a scan rate of 5 mV×s-1. Keywords: Supercapacitor, Co-precipitation method, Nanotechnology, Synthesized, Electrochemical

An Optimization Path for Sb2(S,Se)3 Solar Cells to Achieve an Efficiency Exceeding 20.

Antimony selenosulfide, denoted as Sb2(S,Se)3, has garnered attention as an eco-friendly semiconductor candidate for thin-film photovoltaics due to its light-absorbing properties. The power conversion efficiency (PCE) of Sb2(S,Se)3 solar cells has recently increased to 10.75%, but significant challenges persist, particularly in the areas of open-circuit voltage (Voc) losses and fill factor (FF) losses. This study delves into the theoretical relationship between Voc and FF, revealing that, under conditions of low Voc and FF, internal resistance has a more pronounced effect on FF compared to non-radiative recombination. To address Voc and FF losses effectively, a phased optimization strategy was devised and implemented, paving the way for Sb2(S,Se)3 solar cells with PCEs exceeding 20%. By optimizing internal resistance, the FF loss was reduced from 10.79% to 2.80%, increasing the PCE to 12.57%. Subsequently, modifying the band level at the interface resulted in an 18.75% increase in Voc, pushing the PCE above 15%. Furthermore, minimizing interface recombination reduced Voc loss to 0.45 V and FF loss to 0.96%, enabling the PCE to surpass 20%. Finally, by augmenting the absorber layer thickness to 600 nm, we fully utilized the light absorption potential of Sb2(S,Se)3, achieving an unprecedented PCE of 26.77%. This study pinpoints the key factors affecting Voc and FF losses in Sb2(S,Se)3 solar cells and outlines an optimization pathway that markedly improves device efficiency, providing a valuable reference for further development of high-performance photovoltaic applications.

Suppression of the sausaging effect in (Ba,Na)Fe2As2 round wires by using Ag1−zSnz/Cu double sheath

We report the fabrication of (Ba,Na)Fe2As2 round wires that are processed using the hot isostatic pressing (HIP) technique. We employed double sheaths composed of Cu and Ag1−zSnz alloy because they offer superior mechanical strength compared to pure Ag. The cross-sectional area of the wire core was measured using X-ray computed tomography. The findings of this study demonstrate that utilizing Ag1−zSnz alloy as the inner sheath material helps reduce the degree of the sausaging effect in the wire, resulting in a more uniform cross-sectional area of the wire core. However, transport critical current density (Jc) of the wires with the Ag1−zSnz alloy decreased gradually as the Sn content, z, increased, which may be attributed to the nonoptimized sintering temperatures during the HIP process. These results suggest that when heat-treated at an appropriate temperature during the HIP process, the use of Ag1−zSnz alloy holds great potential for use in high-performance applications.

Stable crystal structure prediction using machine learning-based formation energy and empirical potential function

Crystal structure prediction aims to predict stable and easily experimentally synthesized materials, which accelerates the discovery of new materials. It is worth noting that the stability of materials is the basis for ensuring high performance and reliable application of materials. Among which, the thermodynamic and molecular dynamics stability is especially important. Therefore, this paper proposes a method to predict stable crystal structures using formation energy and Lennard-Jones potential as evaluation indicators. Specifically, we use graph neural network models to predict the formation energy of crystals, and employ empirical formulas to calculate the Lennard-Jones potential. Then, we apply Bayesian optimization algorithms to search for crystal structures with low formation energy and Lennard-Jones potential approaching zero, in order to ensure the thermodynamic stability and dynamics stability of materials. In addition, considering the impact of the bonding situation between atoms in the crystal on the structural stability, this article uses contact map to analyze the atomic bonding situation of each crystal to screen out more stable materials. Finally, the experimental results show that the method we proposed can not only reduce the time for crystal structure prediction, but also ensure the stability of crystal materials.

Impact of reduced graphene oxide on La0.5Ca0.5MnO3 nanocomposite electrode for high-performance energy storage application

Impact of reduced graphene oxide on La0.5Ca0.5MnO3 nanocomposite electrode for high-performance energy storage application

Superior energy storage properties of BiFeO3 doped NaNbO3 antiferroelectric ceramics

NaNbO3 (NN)-based dielectric ceramics for energy storage have garnered significant interest due to their high saturation polarization, low residual polarization, and superior breakdown strength (Eb). However, the low recoverable energy storage density (Wrec) and efficiency (η) significantly limited their practical application. Herein, BiFeO3 (BF) was incorporated into NN to optimize the energy storage performance. The NN-BF ceramics exhibited pronounced antiferroelectric (AFE) relaxor phase, alongside grain size reduction and Eb enhancement, which contributed to a significant increase of Wrec and η. Specially, the optimum Wrec of 4.43 J/cm³ and η of 71.51 % were achieved at the composition of 0.9NN-0.1BF. Besides, stable energy storage performance was maintained over a wide temperature range (20–120 °C). These results highlight the potential of NN-BF relaxor AFE ceramics as promising candidates for high-performance energy storage applications.

Manufacturing of Ni-Co-Fe-Cr-Al-Ti High-Entropy Alloy Using Directed Energy Deposition and Evaluation of Its Microstructure, Tensile Strength, and Microhardness.

High-entropy alloys (HEAs) have drawn significant attention due to their unique design and superior mechanical properties. Comprising 5-35 at% of five or more elements with similar atomic radii, HEAs exhibit high configurational entropy, resulting in single-phase solid solutions rather than intermetallic compounds. Additive manufacturing (AM), particularly direct energy deposition (DED), is effective for producing HEAs due to its rapid cooling rates, which ensure uniform microstructures and minimize defects. These alloys typically form face-centered cubic (FCC) or body-centered cubic (BCC) structures, contributing to their exceptional strength, hardness, and mechanical performance across various temperatures. However, FCC-structured HEAs often have low yield strengths, posing a challenge for structural applications. In this study, a Ni-Co-Fe-Cr-Al-Ti HEA was manufactured using the DED method. This study proposes that the addition of aluminum and titanium creates a γ + γ' phase structure within a multicomponent FCC-HEA matrix, enhancing the thermal stability and coarsening the resistance and strength. The γ' phase with an ordered FCC structure significantly improves the mechanical properties. Analysis confirmed the presence of the γ + γ' structure and demonstrated the alloy's high tensile strength and microhardness. This approach underscores the potential of AM techniques in advancing HEA production for high-performance applications.

Research on Predictive Speed Control Scheme for Surface-Mounted Permanent Magnet Servo Systems

In order to improve the dynamic response and disturbance rejection performance of electric machines, a deadbeat predictive speed control (DPSC) scheme for a permanent magnet synchronous motor (PMSM) is proposed. To begin with, a DPSC controller was proposed with the purpose of achieving precise control for the next control cycle, and the control parameters were determined based on the optimal parameter design method. For better application, performance comparisons were made with a conventional PI control, and the mismatch effects of inertia and torque were analyzed. In order to improve the disturbance rejection performance of the system, an extended sliding mode observer (ESMO) was constructed to compensate for disturbances. Experimental verification with a conventional PI control indicates that the proposed DPSC control can reduce the speed response time from 0.675 s to 0.650 s. When the electric machine operates stably and is applied to a torque disturbance of 0.4 Nm, the speed fluctuation and settling time can be reduced from 9 rpm and 1.7 s to 6 rpm and 0.5 s, respectively. This proposed method effectively enhances the speed control performance of PMSM and can be applied to high-performance electric machine applications.

Design methodology for fused filament fabrication with failure theory: framework, database, design rule, methodology and study of case

PurposeAdditive manufacturing (AM) is growing economically because of its cost-effective design flexibility. However, it faces challenges such as interlaminar weaknesses and reduced strength because of product anisotropy. Therefore, the purpose of this study is to develop a methodology that integrates design for additive manufacturing (AM) principles with fused filament fabrication (FFF) to address these challenges, thereby enhancing product reliability and strength.Design/methodology/approachDeveloped through case analysis and literature review, this methodology focuses on design methodology for AM (DFAM) principles applied to FFF for high mechanical performance applications. A DFAM database is constructed to identify common requirements and establish design rules, validated through a case study.FindingsExisting DFAM approaches often lack failure theory integration, especially in FFF, emphasizing mechanical characterizations over predictive failure analysis in functional parts. This methodology addresses this gap by enhancing product reliability through failure prediction in high-performance FFF applications.Originality/valueWhile some DFAM methods exist for high-performance FFF, they are often specific cases. Existing DFAM methodologies typically apply broadly across AM processes without a specific focus on failure theories in functional parts. This methodology integrates FFF with a failure theory approach to strengthen product reliability in high-performance applications.

Energetic Hypervalent Organoiodine Electrochemistry for Aqueous Zinc Batteries.

Hypervalent organoiodine compounds have been extensively utilized in organic synthesis, yet their electrochemical properties remain unexplored despite their theoretically high redox potential compared with inorganic iodine, which primarily relies on the I-/I0 redox couple in battery applications. Here, the fundamental redox mechanism of hypervalent organoiodine in a ZnCl2 aqueous electrolyte is established for the first time using the simplest iodobenzene (PhI) as a model compound. We validated that the PhI to PhICl2 transition is a single-step and reversible reaction, enabling two-electron transfer of I+/I3+ redox chemistry (1.9 V vs Zn2+/Zn) with high capacity (422 mAh giodine-1, and 262.6 mAh g-1 based on PhI) and high theoretical energy density (801.8 Wh kg-1). It was also elucidated that such organoiodine electrochemistry exhibits rich tunability in terms of the global reactivity of various PhI derivatives, including multiple iodine-substituted isomers and functional substituents. Additionally, the stabilizing anion ligands affect the reversibility and stability of trivalent organoiodine compounds. By limiting side reactions and improving the stability of trivalent organoiodine at low temperatures, the zinc-PhI battery demonstrated the feasibility of I+/I3+ conversion and sustained stable performance over 400 cycles. This work bridges the gap between hypervalent organoiodine chemistry and battery technology, highlighting the potential for future high-performance battery applications.

Effect of electron-donating and -withdrawing substitutions in naphthoquinone sensitizers: The structure engineering of dyes for DSSCs in Quantum Chemical Study

Dye-sensitized solar cells (DSSCs) are cost-effective photovoltaic devices that convert solar energy into electricity using a dye sensitizer, TiO2 photoanode, electrolyte, and counter electrode. This study investigates the impact of substituents on the performance of naphthoquinone-based dye sensitizers in DSSCs. We analyzed various naphthoquinone derivatives’ electronic structures and light absorption properties using DFT and TD-DFT. Our results demonstrate that electron-donating groups enhance DSSC performance by improving light absorption and electron injection. Specifically, naphthoquinone derivatives with methoxy (Dye-2) and methyl (Dye-3) groups showed superior properties. TD-DFT analysis revealed high molar extinction coefficients over a broad spectrum, making these dyes efficient at capturing sunlight. Additionally, these dyes effectively interact with TiO2, which is crucial for photostability and photovoltaic performance. In conclusion, naphthoquinone derivatives with electron-donating groups significantly improve DSSC performance, with Dye-2 and Dye-3 being strong candidates for high-performance applications.

Knowledge Distillation for Enhanced Age and Gender Prediction Accuracy

In recent years, the ability to accurately predict age and gender from facial images has gained significant traction across various fields such as personalized marketing, human–computer interaction, and security surveillance. However, the high computational cost of the current models limits their practicality for real-time applications on resource-constrained devices. This study addressed this challenge by leveraging knowledge distillation to develop lightweight age and gender prediction models that maintain a high accuracy. We propose a knowledge distillation method using teacher bounds for the efficient learning of small models for age and gender. This method allows the student model to selectively receive the teacher model’s knowledge, preventing it from unconditionally learning from the teacher in challenging age/gender prediction tasks involving factors like illusions and makeup. Our experiments used MobileNetV3 and EfficientFormer as the student models and Vision Outlooker (VOLO)-D1 as the teacher model, resulting in substantial efficiency improvements. MobileNetV3-Small, one of the student models we experimented with, achieved a 94.27% reduction in parameters and a 99.17% reduction in Giga Floating Point Operations per Second (GFLOPs). Furthermore, the distilled MobileNetV3-Small model improved gender prediction accuracy from 88.11% to 90.78%. Our findings confirm that knowledge distillation can effectively enhance model performance across diverse demographic groups while ensuring efficiency for deployment on embedded devices. This research advances the development of practical, high-performance AI applications in resource-limited environments.

Mentor: A Memory-Efficient Sparse-dense Matrix Multiplication Accelerator Based on Column-Wise Product

Sparse-dense matrix multiplication (SpMM) is the performance bottleneck of many high-performance and deep-learning applications, making it attractive to design specialized SpMM hardware accelerators. Unfortunately, existing hardware solutions do not take full advantage of data reuse opportunities of the input and output matrices or suffer from irregular memory access patterns. Their strategies increase the off-chip memory traffic and bandwidth pressure, leaving much room for improvement. We present Mentor , a new approach to designing SpMM accelerators. Our key insight is that column-wise dataflow, while rarely exploited in prior works, can address these issues in SpMM computations. Mentor is a software-hardware co-design approach for leveraging column-wise dataflow to improve data reuse and regular memory accesses of SpMM. On the software level, Mentor incorporates a novel streaming construction scheme to preprocess the input matrix for enabling a streaming access pattern. On the hardware level, it employs a fully pipelined design to unlock the potential of column-wise dataflow further. The design of Mentor is underpinned by a carefully designed analytical model to find the trade-off between performance and hardware resources. We have implemented an FPGA prototype of Mentor . Experimental results show that Mentor achieves speedup by geomean 2.05 × (up to 3.98 ×), reduces the memory traffic by geomean 2.92 × (up to 4.93 ×), and improves bandwidth utilization by geomean 1.38 × (up to 2.89 ×), compared to the state-of-art hardware solutions.