Fluorine-guided synthesis of copper nickel compounds with 2-methylimidazole and temperature control for battery supercapacitor hybrids.

Manganese oxide quantum dots indorse for control C2C12 inhibition driven by solution process

Tantalum doping triggered electronic reconfiguration of cobalt phosphide for efficient and stable overall seawater splitting.

Two novel cold-start multistage neural solvers for constrained nonlinear equations with extended time horizons.

Solution-processable circularly polarized (CP) organic light-emitting diodes (OLEDs) are unique devices generating CP electroluminescence. However, the efficiency of solution-processed CP OLEDs is still low compared with vacuum-processed CP OLEDs. In this work, highly efficient and narrow-emitting solution-processed CP OLEDs are developed using a novel multi-resonance (MR) thermally activated delayed fluorescence (TADF) emitter operated by an intramolecular TADF sensitized MR-TADF emission mechanism. The CP-type MR-TADF emitter is designed to have a TADF antenna unit and a terminal MR-TADF unit connected through a planar chiral unit. The TADF antenna unit plays the role of initial exciton generator, harvesting both singlet and triplet excitons, and the exciton energy is transferred to the terminal MR-TADF unitby through-space energy transfer. As a result, the external quantum efficiency (EQE) of the chiral MR-TADF emitter is significantly enhanced from 16.2% of the MR-TADF emitter without the TADF antenna unit to 28.0% by solution process. The CP OLEDs showed an electroluminescence dissymmetry factor of 10-3 order, demonstrating that the molecular design is also effective to achieve highly efficient CP emission in OLED. This work establishes an effective strategy for realizing highly efficient narrowband CP electroluminescence via intramolecular TADF-sensitized MR emission.

The present study investigates the magnetohydrodynamic (MHD) flow characteristics of a blood-based ternary nanofluid (Au/Cu/Al2O3-blood) in stenosed arteries, with a focus on symmetry-inspired modeling rooted in the axial symmetry of arterial geometry and the symmetric distribution of external physical fields (magnetic field, thermal radiation). The findings offer significant insights into the realm of hyperthermia therapy and targeted drug delivery within the domain of biomedical engineering. A mathematical model is established under a cylindrical coordinate system (consistent with arterial axial symmetry), integrating key physical effects (thermal radiation, chemical reactions, viscous dissipation, body acceleration) and fractional-order dynamics via Caputo derivatives—while ensuring the symmetry of governing equations in time and space. The numerical solutions for velocity and temperature profiles are obtained using the Laplace transform and Concentrated Matrix-Exponential (CME) method, a technique that preserves symmetric properties during the solution process. The results of the study indicate the following: The Hartmann number, which is increased, has been shown to reduce axial velocity due to the Lorentz force, thereby maintaining radial symmetry. Furthermore, thermal radiation has been demonstrated to raise fluid temperature, a critical factor in heat-based therapies, with the temperature field evolving symmetrically. In addition, it has been observed that ternary nanoparticles outperform single and binary systems in heat and mass transfer via symmetric dispersion. This work contributes to the existing body of knowledge by integrating symmetry principles into the study of fractional dynamics, electromagnetic fields, and body acceleration modeling. It establishes a comprehensive biomedical flow framework. It is imperative that future research explore pulsatile flow under symmetric boundaries and validate the model through experimental means.

Abstract We focus on the expected residual minimization model with conditional value-at-risk constraints (CVaR-ERM Model) for solving the stochastic tensor complementarity problem (STCP). Our previous work (Zhang, Luo, Nguyen, in: J. Nonlinear Convex Anal. 26:61–76, 2025) explored the convergence of global optimal solutions of the approximate problem of the CVaR-ERM model. However, in some practical situations, both the proposed model and its approximate problem exhibit non-convex properties, which significantly raises the probability of obtaining stationary points during the solution process. Therefore, in this paper, we first formulate the approximate problem of the CVaR-ERM model using the smoothing method, the penalty function method, and the sample average approximation method. Subsequently, we prove the convergence of the stationary points of the proposed approximate problem.

Transition metal dichalcogenides (TMDCs) have gained significant attention due to their unique nanoscale properties as thin-layered materials. Controlling their structures is essential for understanding their physicochemical characteristics. For instance, the nanoscopic structures of their crystalline phases determine whether TMDCs display semiconductive or metallic properties. Additionally, the number of layers in TMDCs plays a key role in defining their electronic states. Monolayers and bilayers of MoS2 and WSe2, for example, exhibit direct and indirect bandgaps, respectively. In this presentation, I will focus on our recent findings related to the transformation of structures and layer thicknesses through solution-processable molecular treatment methods.We developed a method to convert the semiconductive 1H phase of MoS2 into the 1T' phase.[1] This process involves a UV-Ozone (UVO) treatment that oxidizes the surface of MoS2. While UVO treatment alone does not fully convert MoS2 to the 1T' phase, the transformation is completed when the surface is coated with an organic polymer. The 1T' phase in MoS2 is usually a metastable state, making it challenging to stabilize at room temperature. Our method not only stabilizes the 1T' phase but also allows for the creation of junctions. At the conference, I will explain in detail how we characterized this material.Regarding the shapes of TMDC flakes, we developed a method to transform a monolayer into a bilayer by bending the monolayer through a molecular solution process.[2] Using this procedure, we successfully created thousands of artificial bilayers on a substrate. The method relies on a spontaneous phase separation phenomenon, which generates micro-scale droplets in the solution. These droplets then adhere to and coagulate on the surface of monolayer MoS2. The shear force generated during this coagulation process bends the monolayer, converting it into bilayers. At the conference, I will discuss the details of the mechanisms involved in the bending process.REF:[1] K. Matsuyama, et al., "Phase engineering of 1T’-MoS2 via organic enwrapment", ChemRxiv, 2025, DOI: 10.26434/chemrxiv-2025-r709q[2] S. Yotsuya, et al., "Microdroplet fusion driving the mass formation of twisted bilayer molybdenum disulfide fragments via spontaneous liquid-liquid phase separation", ChemRxiv, 2024, DOI: 10.26434/chemrxiv-2024-cw7wz

When conducting research on the static voltage stability of AC/DC systems with voltage source converter-high voltage direct current (VSC-HVDC) transmission lines, the focus is often given to reactive power control, neglecting the potential from active power support. Based on the minimum modulus eigenvalue, this paper proposes to coordinately control active and reactive power of VSC-HVDC to improve the static voltage stability of AC/DC systems. Firstly, the converter loss is quantified and taken into account to solve the power flow of the AC/DC system. Secondly, the minimum modulus eigenvalue of the system is calculated based on the Jacobian matrix in the power flow solution process to characterize the static voltage stability of the system. Then, taking the minimum modulus eigenvalue of the AC/DC system as the optimization objective, with power flow, node voltage, and converter power as constraints, and with the active and reactive power injections of HVDC as optimization variables, an optimization model is built to determine the optimal adjustment of active and reactive power of VSC-HVDC. Finally, the particle swarm optimization algorithm is utilized to solve the optimization model. Simulations in MATLAB show that compared with only active power control and only reactive power control, the proposed control method can significantly improve the static voltage stability of the system while ensuring its safe operation.

ABSTRACT Deep learning‐based models have recently emerged as alternatives to traditional form‐constrained hysteresis models, including Bouc‐Wen class models, offering significant potential for unified hysteresis modeling to capture complex nonlinearities and diverse response patterns exhibited under stochastic excitations such as ground motions. This paper proposes a unified hysteresis modeling framework based on deep learning, leveraging (1) physics‐encoded deep learning through a custom architecture that emulates the solution process of traditional models, (2) physics‐informed deep learning with an efficient loss function to enforce non‐negative energy dissipation, and (3) data augmentation via resampling hysteresis data to enhance the training dataset. The proposed unified hysteresis model can be trained on a relatively small amount of force–displacement data obtained under seismic excitations and enabling efficient and accurate time history analysis. The proposed model can account for complex stiffness and strength degradations and pinching effects. Tests across various traditional hysteresis models demonstrate that the proposed deep learning‐based unified hysteresis model can effectively reproduce diverse hysteresis behaviors. The proposed model is also validated against experimental hysteresis data from modular yielding links, confirming its capability to accurately represent real‐world hysteresis behavior. The source code and accompanying data can be accessed online for reproducibility at https://github.com/JaehwanJeon/Testing_torch .

Amorphous oxide semiconductor-based thin-film transistors (TFTs), particularly those utilizing indium gallium zinc oxide (IGZO), have garnered significant attention for next-generation display backplanes and flexible electronics. However, the precise and reliable modulation of threshold voltage (Vth) remains a persistent challenge, often requiring doping or vacancy engineering approaches that compromise process uniformity and device reliability. In this study, we introduce a scalable and low-temperature strategy for Vth tuning via the incorporation of two-dimensional (2D), single-crystalline silver nanosheets (Ag NSs) within the IGZO channel. These quasi-two-dimensional nanostructures have nanometer-scale thickness and lateral single crystallinity and are assembled using an ultrasonic-driven solution process that allows tunable coverage over large-area substrates. By varying Ag NS coverage up to 6.8%, we achieve a systematic and reproducible positive shift in Vth, with minimal degradation in mobility, on/off ratio, and subthreshold swing. Mechanistic studies using X-ray photoelectron spectroscopy and electrical bias stress testing reveal that the modulation arises from Schottky barrier formation and electrostatic screening at the Ag-IGZO interface rather than from modulation of oxygen vacancy concentrations. Devices incorporating Ag NSs exhibit excellent stability, with minimal hysteresis (ΔVth ≈ 1 V), negligible parameter drift under a ±20 V gate bias stress for 60 min, and long-term retention after 390 days of ambient storage. To validate the circuit-level applicability of this method, we fabricated depletion-load NMOS inverters combining pristine and Ag NS-modified IGZO TFTs, wherein the switching threshold could be finely tuned via the Ag NS coverage. This work demonstrates a wafer-compatible and solution-processable route to deterministic Vth engineering in oxide TFTs, offering a promising platform for future high-performance, flexible, and large-area electronic systems.

This paper introduces a spectral algorithm tailored for solving fractional boundary value problems (BVPs) using the fractional derivatives of modified Chebyshev polynomials. Specifically, it addresses linear and non-linear BVPs and Bratu equations in one dimension via spectral methods. The approach employs basis functions derived from first-kind shifted polynomials that satisfy the homogeneous boundary conditions. The fractional derivatives are formulated to facilitate the solution process. The convergence analysis is studied for the suggested basis expansion; some numerical results are exhibited to verify the applicability and accuracy of the method.

The advent of machine learning has prompted the emergence of innovative methodologies for predicting the hydrodynamics of granular flows. In this study, the physics-informed neural network (PINN) approach was employed to solve the forward and inverse problems of a simple granular flow with smooth particles in the homogeneous cooling state. The three techniques, which are the dimensionless granular temperature as the optimization of the loss functions, the normalized time information as the input layer, and adjusting local weights of sample points based on the physical characteristics, have been shown to significantly contribute to enhancing the precision of classical PINN in predicting the variation of granular temperature over time. The proposed method (developed PINN) has been validated for many different cases and the influence of numbers of sampled date in the solution process was also investigated.

The advancement of novel anti-corrosion coatings is essential for the preservation and maintenance of stone materials in heritage structures. This research investigates the synergistic effects of graphene and polytetrafluoroethylene (PTFE) in enhancing the corrosion resistance of epoxy coatings. Molecular dynamics simulations were utilized to construct models of pure epoxy resin (PR), graphene-reinforced epoxy resin (G/PR), and epoxy resin co-modified with graphene and PTFE (G/PTFE/PR), with the aim of assessing their corrosion resistance and mechanical performance. Findings indicate that the incorporation of graphene and PTFE markedly reduced the porosity within the epoxy resin matrix. Furthermore, the diffusion coefficients of water molecules and epoxy resin molecules in the G/PTFE/PR system decreased by 47% and 52%, respectively. The formation of hydrogen bonds between oxygen atoms in water molecules and hydrogen atoms in epoxy resin molecules was found to impede water molecule diffusion. Mechanical analysis via stress-strain curves revealed that the modified epoxy resin exhibited superior tensile strength. These results offer valuable insights for the development of advanced anti-corrosion coatings applicable to the conservation of historic buildings. The molecular dynamics simulation software LAMMPS was employed to investigate the penetration process of a corrosive solution. To ensure the accuracy of the results, the appropriate empirical force field for polymers, known as PCFF, was utilized.

This article presents a complete classification of Ricci solitons and their associated vector fields in the context of locally rotationally symmetric (LRS) Bianchi type I spacetime, a crucial model in cosmological studies. To systematically address the complexities inherent in the Ricci soliton equations, we adopt the Rif tree technique. The equations defining the Riccisoliton and its vector field are transformed into a reduced involutive form using a computational algorithm, which assists in dividing the integration process into a collection of cases organized in a tree-like structure. Each of these cases is governed by specific constraints on the metric functions, which facilitates the solution process. Definite expressions for the metric functions and the corresponding vector field of the Ricci soliton are obtained by efficiently solving the system of equations characterizing the soliton vector field through the application of these constraints. This powerful approach enables us to derive novel and exact solutions that previous methods have overlooked. Our results demonstrate that this spacetime admits Ricci solitons of shrinking, steady, and expanding natures, characterized by vector fields with up to 11 free parameters. Crucially, we conduct a thorough physical analysis of the resulting models, determining their matter content through the equation of state and testing their physical viability via the standard energy conditions. We find specific families of solutions that correspond to physically significant scenarios, such as a spacetime filled with vacuum energy (a cosmological constant). This work not only provides a comprehensive mathematical classification but also establishes a direct link between these geometric structures and potentially realistic cosmological models.

To improve the efficiency of multi-region multi-unmanned aerial vehicle (UAV) inspection, this paper proposes a composite task planning strategy integrating the K-Means++ genetic algorithm (KMGA) and the multi-neighborhood iterative dynamic programming (MNIDP) method. Firstly, the multi-region multi-UAV inspection problem is modeled as a multiple traveling salesmen problem with neighborhoods (MTSPN). Then, this problem is decomposed into two interrelated subproblems to mitigate the complexity inherent in the solution process: that is, the multiple traveling salesmen problem (MTSP) and multi-neighborhoods path planning (MNPP) problem. Based on this decomposition, the MTSP is solved by the KMGA by converting it into m spatially non-overlapping traveling salesmen problems (TSPs) and then these TSPs are solved to obtain the approximate optimal visiting sequences for the nodes in each TSP in a short time. Subsequently, the MNPP can be efficiently solved by an MNIDP which plans the paths between the corresponding neighborhood of each node based on the node visiting sequences, thus obtaining the approximate optimal path length of the MTSPN. The simulation results demonstrate that the proposed composite strategy exhibits advantages in computational efficiency and optimal path length. Specifically, compared to the baseline algorithm, the average tour length obtained by the KMGA decreased by 23.24%. Meanwhile, the average path lengths computed by MNIDP in three instances were reduced from 8.00% to 11.41% and from 6.46% to 10.08% compared to two baseline algorithms, respectively. It provides an efficient task and path planning solution for multi-region multi-UAV operations in power transmission line inspections, thereby enhancing inspection efficiency.

High-performance liquid chromatography method for simultaneous determination of the degradation products of metformin hydrochloride and vildagliptin.

Draw solution selection and process parameters impact the performance of forward osmosis for nonthermal concentration of liquids

In small multi-functional base stations such as 230MHz power wireless private network LTE, when there is concurrent transient access of a large number of terminals, issues such as packet blocking and loss frequently occur, severely degrading overall system performance. To this end, the total delay during data transmission and queuing in the massive concurrent access of the power wireless private network is modeled, and a carrier allocation optimization method based on the optimized heuristic algorithm - immune algorithm is proposed. First, for the multi-objective problem with high real-time data requirements and packet loss rate requirements in the problem, an operational research model of data delay mechanism is constructed with the total data transmission delay as the objective function; An optimal resource allocation method based on immune algorithm is proposed to optimize the solution process; The minimum existence and convergence of the data delay model were analyzed and proved. The experimental results show that in the case of massive concurrent access, the proposed method enables the base station to maintain more stable performance under carrier limitations and massive concurrent access.

Partial Differential Equations (PDEs) are fundamental tools for modeling dynamic behaviors in physical, chemical, and engineering systems. However, solving nonlinear PDEs poses significant challenges due to the lack of closed-form solutions and the computational limitations of classical numerical approaches. This study introduces the Modified Adomian Decomposition Method (MADM) as an effective semi-analytical technique for solving both linear and nonlinear PDEs, with applications to the Advection, Burgers’, and Sine-Gordon equations. MADM enhances the classical Adomian Decomposition Method by incorporating refined recursive structures and inverse operators, which improve the convergence rate and simplify the solution process. The results demonstrate that MADM provides highly accurate solutions, often matching known exact solutions, and exhibits faster convergence compared to existing methods. Comparative analysis with the Variational Iteration Method (VIM) and the New Iteration Method (NIM) further highlights MADM’s computational efficiency and precision. These findings establish MADM as a robust and reliable tool for addressing complex PDEs across various scientific domains.