Explore the vast range of titles available.

The remarkable advantages of heterointerface and defect engineering and their unique electromagnetic characteristics inject infinite vitality into the design of advanced carbon-matrix electromagnetic wave absorbers. However, understanding the interface and dipole effects based on microscopic and macroscopic perspectives, rather than semi-empirical rules, can facilitate the design of heterointerfaces and defects to adjust the impedance matching and electromagnetic wave absorption of the material, which is currently lacking. Herein, CuCo 2 S 4 @Expanded Graphite heterostructure with multiple heterointerfaces and cation defects are reported, and the morphology, interfaces and defects of component are regulated by varying the concentration of metal ions. The results show that the 3D flower-honeycomb morphology, the crystal-crystal/amorphous heterointerfaces and the abundant cation defects can effectively adjust the conductive and polarization losses, achieve the impedance matching balance of carbon materials, and improve the absorption of electromagnetic wave. For the sample CEG-6, the effective absorption of Ku band with RL min of −72.28 dB and effective absorption bandwidth of 4.14 GHz is realized at 1.4 mm, while the filler loading is only 7.0 wt. %. This article reports on the establishment of potential relationship between crystal-crystal/amorphous heterointerfaces, cation defects, and the impedance matching of carbon materials. Heterointerface and defect promote the development of electromagnetic wave absorbers. Here, the authors show the 3D flower-honeycomb CuCo 2 S 4 @Expanded Graphite heterostructure, report the mechanism of crystal-crystal/amorphous heterointerfaces and cation defects on electromagnetic wave absorption.

This paper concerns state and fault simultaneous interval estimations for discrete-time linear systems with unknown but bounded uncertainties. Unlike most existing works on interval estimation, the system considered in this work is subject to actuator and sensor faults, while bounded uncertainties exist in both components. Thus, the method developed in this paper can be used for more general conditions. First, the considered system is reformulated as a descriptor one through the augmented vector method. An important lemma for descriptor systems is given in a more accurate description and proven in a very simple way. Then, by using the reachability analysis technique, a new proportional-integral observer-based interval estimation method is proposed. The H ∞ technique reduces the influences of time-varying actuator faults and uncertainties. To build the zonotope of bounded uncertainties in the residual system, an equivalent description is introduced. Finally, a numerical example and an industrial system are simulated to demonstrate the efficacy and applicability of the developed method.

In recent years, rapid industrial development has led to the emission of diverse gaseous pollutants into the atmosphere. To detect and monitor these pollutants, gas sensors have become a critical technology. Researchers have developed numerous gas-sensitive materials, among which MXenes—a novel class of two-dimensional materials—have garnered significant attention. Owing to their excellent electron transport properties, abundant surface functional groups, and large specific surface area, MXenes find wide applications in catalysis, sensing, electromagnetic shielding, water treatment, and beyond. However, despite these outstanding properties, MXenes’ susceptibility to environmental degradation has hindered their broader development and application as long-term stable gas-sensitive materials. While recent studies have investigated degradation mechanisms and explored various stability enhancement strategies, comprehensive reviews specifically focusing on stability improvements for gas-sensing applications remain scarce. This review first examines the current research on MXene oxidation processes in different environments. Subsequently, it systematically summarizes existing strategies to enhance MXene’s long-term stability and its implementation in gas sensing, including optimization of preparation methods, surface protection and modification, composite construction, and other approaches. Finally, the review concludes by summarizing current progress and outlining future perspectives.

Seawater electrolysis is an ideal technology for obtaining clean energy—green hydrogen. Developing efficient bifunctional catalysts is crucial for hydrogen production through direct seawater electrolysis. Currently, metal substrates loaded with active catalysts are widely employed as electrodes for seawater electrolysis. However, the challenge of metal corrosion cannot be ignored. In this work, the boron-doped diamond (BDD) with excellent corrosion resistance was explored as a substrate for loading active catalysts in seawater electrolysis. A step-by-step electrodeposition method was used to fabricate the FeCoS/Ni/BDD electrode, effectively addressing the poor adhesion of the FeCoS active layer to the BDD substrate. The resulting electrode demonstrated interesting bifunctional catalytic performance, achieving oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) overpotentials of 425 mV and 360 mV, respectively, in alkaline simulated seawater (1 M KOH and 3.5 wt% NaCl) at a current density of 100 mA cm − 2 . Furthermore, by increasing the KOH concentration in the alkaline simulated seawater to 3 M, the OER and HER overpotentials of the electrode significantly decreased to 383 and 300 mV, respectively. This work offers a novel approach for utilizing BDD substrates in the design of corrosion-resistant electrodes for alkaline seawater electrolysis.

Microwave pre-oxidation was compared with conventional pre-oxidation process in 180–280 °C, and the mechanism of microwave pre-oxidation in different temperature was proposed. The crystalline structure (WAXD), chemical structure (FTIR), microstructure (SEM), defect degree (RAMAN) and bulk density were studied. The results showed that the pre-oxidized fibers first undergo crystallization and then undergo a process of amorphous transformation by microwave heating. And at 220 °C, the pre-oxidized fibers under microwave heating show a diffraction peak of 25° (002 diffraction surface), which indicates that a new disordered structure (ladder structure) is generated inside the pre-oxidized fibers. The dehydrogenation reaction runs through the entire pre-oxidation process and is violent at 280 °C. The cyclization reaction is violent at 220 °C and the oxidation reaction mainly occurs at 240 °C.

Investigating the curing kinetics of a fiber prepreg system is beneficial to the controlling of prepreg laminate curing process. In the present work, a dicyandiamide (DICY)-cured carbon fiber/epoxy prepreg system was investigated by non-isothermal differential scanning calorimetry (DSC) at 2, 5, 10, and 20°C/min to attain the glass transition temperature for uncured prepreg and fully cured sample, which were estimated to be 7.6°C and 106.2°C, respectively. The activation energy (Ea) of the prepreg system was evaluated by Kissinger and Ozawa methods, and Friedman method was also employed to reveal the evolution of Ea as a function of curing degree. The kinetic parameters were determined by fitting the average Ea value obtained by Friedman method into Málek methodology, and the two parameters Šesták–Berggren model was found to best describe the curing kinetic of the prepreg system. The preexponential factor was calculated to be 6.0×108 min−1, with the overall reaction order at nearly 2.5. The prediction curves, based on Friedman method and autocatalytic model, were in good agreement with the experimental data.

Owing to high electrical conductivity, layered structure, and abundant surface functional groups, transition metal carbides/nitrides (MXenes) have received enormous interest in the field of gas sensors at room temperature. In this work, we synthesize a heterostructured nanocomposite with indium oxide (In2O3) decorated on titanium carbide (Ti3C2Tx) nanosheets by electrostatic self-assembly and develop it for high-selectivity NO2 gas sensing at room temperature. Self-assembly formation of multiple heterojunctions in the In2O3/Ti3C2Tx composite provide abundant NO2 gas adsorption sites and high electron transfer activity, which is conducive to improving the gas-sensing response of the In2O3/Ti3C2Tx gas sensor. Assisted by rich adsorption sites and hetero interface, the as-fabricated In2O3/Ti3C2Tx gas sensor exhibits the highest response to NO2 among various interference gases. Meanwhile, a detection limit of 0.3 ppm, and response/recovery time (197.62/93.84 s) is displayed at room temperature. Finally, a NO2 sensing mechanism of In2O3/Ti3C2Tx gas sensor is constructed based on PN heterojunction enhancement and molecular adsorption. This work not only expands the gas-sensing application of MXenes, but also demonstrates an avenue for the rational design and construction of NO2-sensing materials.

During electron beam cold hearth melting (EBCHM) of Ti-6wt%Al-4wt%V titanium alloy, aluminum volatilization causes compositional segregation in the ingot, significantly degrading material performance. Traditional methods (e.g., the Langmuir equation) struggle to accurately predict aluminum diffusion and compensation behaviors, while computational fluid dynamics (CFD), although capable of resolving multiphysics fields in the molten pool, suffer from high computational costs and insufficient research on segregation control. To address these issues, this study proposes a CFD-machine learning (backpropagation neural network, CFD-ML(BP)) approach to achieve precise prediction and optimization of aluminum segregation. First, CFD simulations are performed to obtain the molten pool’s temperature field, flow field, and aluminum concentration distribution, with model reliability validated experimentally. Subsequently, a BP neural network is trained using large-scale CFD datasets to establish an aluminum concentration prediction model, capturing the nonlinear relationships between process parameters (e.g., casting speed, temperature) and compositional segregation. Finally, optimization algorithms are applied to determine optimal process parameters, which are validated via CFD multiphysics coupling simulations. The results demonstrate that this method predicts the average aluminum concentration in the ingot with an error of ≤3%, significantly reducing computational costs. It also elucidates the kinetic mechanisms of aluminum volatilization and diffusion, revealing that non-monotonic segregation trends arise from the dynamic balance of volatilization, diffusion, convection, and solidification. Moreover, the most uniform aluminum distribution (average 6.8 wt.%, R2 = 0.002) is achieved in a double-overflow mold at a casting speed of 18 mm/min and a temperature of 2168 K.

The electron beam cold hearth melting (EBCHM) process is one of the key processes for titanium alloy production. However, EBCHM is prone to cause elemental volatilization and segregation during the melting of aluminum-containing titanium alloys such as Ti-6wt%Al-4wt%V. To gain deeper insights into the physical and chemical phenomena occurring during the EBCHM process, this paper establishes melting process models for the Ti-6wt%Al-4wt%V titanium alloy in a crystallizer with multiple overflow inlets. It examines the evolution of melt pool morphology, flow dynamics, heat transfer, and mass transfer during the casting process. The results indicate that the design of multi-overflow inlets can effectively suppress the longitudinal development of impact pits within the melt pool, thereby preventing the formation of solidification defects such as leaks in the melt. Concurrently, the diversion effect of multi-overflow inlets significantly enhances the elemental homogeneity within the melt pool. At a casting speed of 20 mm/min and a casting temperature of 2273 K, compared to a single overflow inlet, the design with three overflow inlets can reduce the depth of thermal impact pits within the crystallizer by 132 mm and decrease the maximum concentration difference in the Al element within the crystallizer by 0.933 wt.%. The aforementioned simulation results provide a theoretical basis for the control of metallurgical and solidification defects in large-scale titanium alloy ingots.

Observer-based actuator fault detection and sensor fault reconstruction for a class of discrete-time nonlinear systems with actuator and sensor faults are investigated in this paper. A descriptor Takagi-Sugeno (T-S) fuzzy model is employed to construct observer-based systems for the purpose of fault detection and sensor fault reconstruction. Two methods for observer design are proposed. In the first method, the observer gains are computed off-line. In the second method, the observer gains are computed on-line at each iteration. The observer designs are formulated using linear matrix inequalities. Sufficient conditions for the existence of the observer-based fault detection and sensor fault reconstruction systems are provided. Comparative simulation study to illustrate the validity of the proposed methods is performed.