Search Results (3,633)

Organic luminescent radicals with through-space charge-transfer (TSCT) excited states are attractive for optoelectronic applications, yet donor-dependent structure–property relationships remain underexplored. Here we report a new spirofluorene-bridged TSCT radical, PID-FR-TTM, employing 1-phenyl-1H-indole (PID) as the donor. Single-crystal X-ray diffraction confirms a carbon-centered TTM radical and a less bulky, more planar five-membered N-heterocycle in the donor region. PID-FR-TTM shows TSCT-type absorption and an emission at 609 nm with a photoluminescence quantum yield (PLQY) of 23.1% and a 90.1 ns emission lifetime in cyclohexane. Calculations indicate a TSCT-dominated excited state and a pronounced singly occupied molecular orbital–highest occupied molecular orbital (SOMO–HOMO) inversion. Notably, PID-FR-TTM exhibits markedly improved stability, including high decomposition temperatures (≈340 °C), excellent electrochemical stability, and enhanced photostability. These results provide donor-structure insights for designing high-performance TSCT radical emitters. Full article

The assessment of reliability in non-repairable subsystems of mining electronic equipment represents a computationally challenging problem, particularly for complex and highly connected structures. This study presents a systematic comparative analysis of several deterministic approaches for reliability estimation, focusing on their computational efficiency, accuracy, and applicability. The investigated methods include classical boundary techniques (minimal paths and cuts), analytical decomposition based on the Bayes theorem, the logic–probabilistic method (LPM) employing triangle–star transformations, and the algorithmic Structure Convolution Method (SCM), which is based on matrix reduction of the system’s connectivity graph. The reliability problem is formally represented using graph theory, where each element is modeled as a binary variable with independent failures, which is a standard and practically justified assumption for power electronic subsystems operating without common-cause coupling. Numerical experiments were carried out on canonical benchmark topologies—bridge, tree, grid, and random connected graphs—representing different levels of structural complexity. The results demonstrate that the SCM achieves exact reliability values with up to six orders of magnitude acceleration compared to the LPM for systems containing more than 20 elements, while maintaining polynomial computational complexity. Qualitatively, the compared approaches differ in the nature of the output and practical applicability: boundary methods provide fast interval estimates suitable for preliminary screening, whereas decomposition may exhibit a systematic bias for highly connected (non-series–parallel) topologies. In contrast, the SCM consistently preserves exactness while remaining computationally tractable for medium and large sparse-to-moderately dense graphs, making it preferable for repeated recalculations in design and optimization workflows. The methods were implemented in Python 3.7 using NumPy and NetworkX, ensuring transparency and reproducibility. The findings confirm that the SCM is an efficient, scalable, and mathematically rigorous tool for reliability assessment and structural optimization of large-scale non-repairable systems. The presented methodology provides practical guidelines for selecting appropriate reliability evaluation techniques based on system complexity and computational resource constraints. Full article

Cellulose nanofibrils (CNFs), with their high aspect ratio, have been widely used in various resin-based composites. To address the issues of easy agglomeration and poor interfacial compatibility of CNFs in hydrophobic acrylate photosensitive resins, this study adopted γ-methacryloyloxypropyltrimethoxysilane (KH570) for silane modification of CNFs, comparing heating–ultrasonication and heating–stirring methods. Mechanical properties were tested via LCD 3D printer to print splines. FTIR, XRD, and SEM verified successful modification, with the silicon substitution degree of heating–ultrasonication modification reaching 29.35%, significantly higher than heating–stirring (22.76%). Thermal analysis showed the main decomposition temperature increased from 400 °C to 420 °C, while DMA confirmed improved rigidity and glass transition temperature. Mechanical tests revealed a strength–toughness trade-off: the 1 wt% modified CNF composite exhibited a tensile strength of 45.17 MPa (9.41 MPa higher than unmodified CNFs at the same dosage), while a high dosage (3.5 wt%) enhanced toughness but reduced strength. The ultrasound-assisted silanization reaction proposed in this study optimizes the preparation process, achieving dual improvements in modification efficiency and dispersion. In terms of performance regulation, it reveals the quantitative control rules and trade-off characteristics of modified CNF content on the mechanical–thermal properties of the composites, providing a basis for performance customization. This study provides a feasible strategy for CNF modification in photopolymerizable 3D printing composites, expanding nanocellulose’s application in additive manufacturing. Full article

In bridge health monitoring, structural responses are strongly coupled with temperature effects and vehicle load effects, making it difficult for conventional fixed thresholds and single data-driven approaches to simultaneously achieve environmental adaptability and quantitative reliability assessment. To address this issue, this study proposes a deep-learning-based dynamic early-warning method for bridge structures, using health-monitoring data from an in-service long-span cable-stayed bridge as the research background. First, a two-month mid-span deflection time series is processed using variational mode decomposition optimized by the Porcupine Optimization Algorithm to separate temperature-induced effects. Subsequently, a hybrid prediction model integrating Informer and SEnet is constructed. Temperature and temperature-induced deflection components are used as input features, and a sliding-window strategy is adopted to achieve high-accuracy prediction of the temperature-induced deflection trend, which serves as the time-varying baseline of the dynamic threshold. On this basis, vehicle load effects are modeled by combining Pareto extreme value theory with finite element analysis and superimposed to establish a two-level dynamic early-warning threshold system that satisfies code requirements. Furthermore, a stochastic finite element Monte Carlo method is introduced to probabilistically model uncertainties associated with material parameters, load effects, and model prediction errors. The threshold failure probability at each time instant is taken as the evaluation metric, enabling quantitative characterization of threshold reliability. The results indicate that under combined multiple working conditions, the proposed method reduces the maximum failure probability of the first-level warning by 32.68% and that of the second-level warning by 93.48%, with more stable and consistent probabilistic responses. In engineering applications, simulation experiments based on stochastic traffic loading show that the warning accuracy is improved by up to 19.27%, while the error rate is reduced by up to 16.16%. The study demonstrates that the proposed method possesses a clear physical and statistical foundation as well as good engineering feasibility and provides a viable pathway for transforming bridge early-warning systems from experience-based schemes toward data-driven and risk-oriented frameworks. Full article

The influence of Zn²⁺, Ca²⁺ and Co²⁺ doping on the thermal, structural, morphological, and magnetic characteristics of CdBi_0.1Fe_1.9O₄ nanoparticles synthetized via the sol–gel technique and calcined at 300, 600, 900 and 1200 °C was investigated. Thermal analysis revealed the initial formation of metallic glyoxylates up to 300 °C, followed by their decomposition into metal oxides and subsequent ferrite formation. X-ray diffraction revealed that the ferrites were poorly crystallized at lower temperatures, whereas at higher calcination temperatures all nanocomposites exhibited well-crystalized ferrites coexisting with the SiO₂ matrix, except for the Co_0.1Cd_0.9Bi_0.1Fe_1.9O₄@SiO₂ nanocomposite, which formed a single, well-defined crystalline phase. Atomic force microscopy images revealed spherical ferrite particles encapsulated within an amorphous layer, with particle size, surface area, and coating thickness influenced by both the type of dopant ion and the calcination temperature. The structural parameters estimated by X-ray diffraction, as well as the magnetic characteristics, were strongly influenced by the dopant type and thermal treatment. These results demonstrate that the structural and magnetic characteristics of CdBi₀.₁Fe₁.₉O₄ ferrites can be effectively tuned through controlled doping and calcination, providing insights for the design of tailored functional applications. Full article

Failure of concrete structures is a multiscale process where macroscale at the structural level and mesoscale at the heterogeneous material level are both involved. A multiscale approach is necessitated in the simulation of concrete failure. Based on an overlapping domain decomposition method, a concurrent multiscale framework for the damage analysis of concrete structures is formulated. The applicability of the proposed framework is illustrated by the multiscale damage analysis of an L-shaped concrete structure. Considering the complexity of a mesoscale model for a global concrete structure, the concrete structure is divided into three parts that require different strategies. Special attention is paid to the part where mesoscale structure needs to be taken. The Concrete Damaged Plasticity (CDP) model is adopted at the mesoscale level. The numerical results indicate that the proposed framework is able to model the damage process in concrete structure where a critical area will be particularly considered. The computational efficiency of the concurrent nonlinear algorithm is also discussed. The proposed multiscale framework can be potentially applied to model structural damage analysis in engineering practice. Full article

Electromagnetic interference (EMI) rectification of grid-tied inverters is crucial for their practical application, and the variable frequency modulation (VFM) technique is a low-cost and simple way for EMI reduction. However, changes in loss and harmonic behaviors make it hard for parameter determination of VFM. In this paper, the parameters required for switching frequency (SF) function are determined for loss optimization of MOSFETs and inductors, while total harmonic distortion (THD) and temperature rise in MOSFETs and inductor core are constrained to guarantee the feasibility of the calculated parameters. Current transient is derived through multidimensional Fourier decomposition (MFD) and characteristics of Bessel function for loss estimation of MOSFET and inductor. Modified Steinmetz equation (MSE) is applied for core loss estimation and AC resistance is considered for copper loss estimation. With the constraints of THD and temperature, the loss optimization problem is solved by the augmented Lagrangian (AL) method. With the assistance of the proposed method, total loss optimization can be realized in feasible regions while the temperature rise in essential components can be restricted to the preset values. Full article

Natural antibacterial food packaging materials endowed with environmental responsiveness are garnering substantial research interest in sustainable food preservation. This study reports the development of a pH-responsive antimicrobial composite film through encapsulation of eugenol—a natural phenolic compound—within zeolitic imidazolate framework-8 (ZIF-8). The engineered eugenol@ZIF-8 system demonstrated pH-dependent release characteristics, with cumulative release reaching 32.2% at pH 6 versus merely 0.61% at pH 7 over 4 h. Subsequent integration of this nanocarrier into a polyvinyl alcohol (PVA)/hydroxypropyltrimethyl ammonium chloride chitosan (HACC) matrix yielded a multifunctional composite film for active food packaging applications. The characterization of film revealed that while eugenol@ZIF-8 incorporation slightly compromised mechanical strength (tensile resistance decreased by 18.7%) and flexibility (elongation at break reduced to 54.3% of control), it significantly enhanced hydrophobicity (water contact angle increased to 92.5°) and thermal stability (decomposition temperature elevated by 34 °C). The composite film demonstrated synergistic antibacterial efficacy through the combined action of Zn²⁺ ions, ZIF-8 nanostructures, and eugenol, achieving 88% inhibition against E. coli. Practical validation through fresh noodle preservation trials confirmed the material’s effectiveness, with the optimized formulation (PVA-HACC-2% eugenol@ZIF-8, PHEZ2) extending shelf life by >5 days compared to conventional packaging. This work establishes a novel strategy for engineering intelligent ZIF-based packaging systems that respond to food spoilage microenvironments, offering significant potential for reducing food loss. Full article

Persistent cloud cover and frequent rainfall in subtropical regions throughout the year significantly limit the applicability of optical remote sensing for tree species identification, thereby constraining dynamic forest monitoring and precise management of forest resources. To address this challenge, this study proposes a tree species identification method that integrates multi-source remote sensing temporal features. By combining multi-temporal optical imagery from Sentinel-2 and dual-polarisation Synthetic Aperture Radar (SAR) data from Sentinel-1, we constructed a comprehensive feature set that incorporates spectral, structural, and phenological attributes, including various vegetation indices, backscatter coefficients, and polarimetric decomposition parameters. Through correlation analysis and assessment of temporal feature variability, five distinct integration strategies (T1-T5) were developed to classify six typical subtropical tree species: Pinus massoniana, Pinus elliottii, Acacia, Eucalyptus grandis, Mangrove, and Other hardwoods, using a random forest classifier. The results indicate that the multi-source feature fusion approach significantly outperforms single-source models, with the T5 strategy achieving the highest overall accuracy (OA) of 95.33% and a Kappa coefficient of 0.94. The red-edge vegetation indices and SAR polarimetric features were identified as major contributors to improving the classification accuracy of hardwood species. This study demonstrates that multi-source remote sensing data fusion can effectively mitigate the spatiotemporal constraints of optical imagery, providing a viable solution and technical framework for high-accuracy remote sensing classification in complex subtropical forest environments. Full article

N₂O emissions exacerbate the greenhouse effect, urgently demanding advances in abatement technologies. Catalytic decomposition of N₂O over cobalt-based oxides with alkali metal promoters remains challenging because these catalysts are used in pelletized form, limiting their activity to a narrow outer-shell region due to internal diffusion limitations. However, research efforts continue to focus on enhancing Co–alkali metal contact on unsupported powder samples under inert conditions, even though, under industrial conditions, catalysts are exposed to inhibitory components of waste gases and N₂O, and the powder form is unsuitable for practical application. This study aims at testing N₂O decomposition over catalysts with a Co₃O₄-Cs active phase supported on a ceramic foam. For this purpose, we characterized these catalysts by H₂ temperature-programmed reduction, H₂O and NO temperature-programmed desorption, atomic absorption spectroscopy, and X-ray diffraction and assessed their catalytic performance under an inert-gas atmosphere and with O₂, water vapor, and NO to simulate industrial conditions. Using a pseudo-homogeneous, one-dimensional model of an ideal plug flow reactor in an isothermal regime, the simulation calculations for a full-scale catalytic reactor for N₂O abatement in waste gas from HNO₃ production were performed. The Cs₂CO₃ precursor significantly enhanced catalyst reducibility and electron transferability, increasing N₂O decomposition efficiency in inert gas, but its high hygroscopicity decreased resistance to water vapor and NO, overriding its advantages under industrial conditions. Conversely, glycerol-assisted impregnation enhanced catalyst performance regardless of Cs precursor. These foam-supported catalysts offered several other advantages, including lower pressure drop and lower active phase loading with matching catalytic activity. Based on our findings, depositing Cs₂CO₃ on ceramic foam through glycerol-assisted impregnation may facilitate catalytic N₂O decomposition at the industrial level and, therefore, promote environmental sustainability by reducing N₂O emissions. Full article

To overcome the typical limitations of conventional polyurethanes, including insufficient thermal stability, mechanical strength, and recyclability, this study presents a high-performance and reprocessable poly(urethane–urea) nanocomposite reinforced with Ti₃C₂T_x MXene (MX-AHPU). The formation of strong hydrogen bonds between the urea groups of the polymer and the oxygen-functionalized MXene surface was confirmed by FTIR, XRD, and XPS, which also verified the complete reaction of –NCO groups. MXene incorporation substantially improved thermal stability, as evidenced by TGA showing a higher onset decomposition temperature and increased char residue. DSC analysis indicated a raised glass transition temperature, reflecting restricted chain mobility. The composite demonstrated remarkable mechanical enhancement, with tensile strength increasing by 70% to 26.7 MPa and toughness rising by 28% to 311.8 MJ·m⁻³, while maintaining exceptional elongation (>3600%). Dynamic mechanical analysis revealed a lower activation energy for stress relaxation (26.6 kJ/mol for MX-AHPU, 30.9 kJ/mol for neat AHPU), indicating enhanced molecular mobility and energy dissipation. Importantly, the material exhibited excellent recyclability, retaining most of its mechanical performance after three reprocessing cycles due to the reversible nature of the interfacial hydrogen bonds. This work provides an effective strategy for designing sustainable, high-performance polyurethane–urea composites suitable for demanding applications such as flexible electronics and advanced coatings. Full article

Molybdenum (Mo) finds extensive applications in the steel industry, and the recycling of secondary molybdenum resources is crucial for the green development of the molybdenum sector. Meanwhile, the large-scale stockpiling of copper slag, a bulk industrial solid waste, poses severe environmental and resource-related challenges. Addressing the common issues of the refractory nature of waste molybdenum disilicide (MoSi₂) and the underutilization of iron resources in copper slag, this study proposes a synergistic smelting approach using copper slag and waste MoSi₂, aiming to realize the coordinated treatment of these two solid wastes and the simultaneous, efficient recovery of valuable metals (Mo and Fe). Under non-isothermal conditions, this work elucidates the phase evolution of copper slag and the decomposition–reduction behavior of MoSi₂; clarifies the dual role of coke as the primary reductant at the initial reaction stage and as a maintainer of a reducing atmosphere during smelting; and systematically investigates the effects of smelting temperature, slag basicity, and coke dosage on metal recovery. The results demonstrate that, under optimized process conditions, the recovery efficiencies of molybdenum and iron can reach 98.97% and 98.46%, respectively. This study provides a new strategy for the enrichment and extraction of metallic elements from waste MoSi₂ and copper slag. Full article

Traditional burn severity assessment methods have predominantly leveraged optical remote sensing data, yet such methods often overlook critical vegetation structural information inherent to post-fire ecosystems. Synthetic Aperture Radar (SAR) data offer structural information but are hindered by non-uniform scattering in fire-affected areas, limiting the utility of conventional decomposition techniques. Here, we introduced a metric that quantifies scattering non-uniformity by jointly considering canopy burn and ground condition non-uniformity. From this metric, we derived quantitative polarimetric features that enhance SAR-based severity estimation and demonstrated the potential to assess burn severity, with an R of 0.77 and a RMSE of 0.58. Initially, six decomposition features were extracted with the covariance matrix and then 14 feature groups were formed through metric and combination. Subsequently, sensitivity analyses were conducted for the first nine feature groups with the Composite Burn Index (CBI) values. Following this, the 14 feature groups were employed as inputs and the CBI values as outputs for random forest learning at a 7:3 training ratio to assess burn severity and generate burn severity maps. This study used the Jinyun Mountain fire in Chongqing as the primary case and eight fires in the United States as supplemental data to discuss the general applicability of the quantitative polarimetric features in assessing burn severity. Notably, the developed methodology showcased superior results within all wildfires, offering a new outlook for future burn severity assessments utilizing vegetation structure information. Full article

Infrared small target detection (IRSTD) task is vital in practical applications. It is still a challenge when the target size is very small and the local signal-to-noise ratio is particularly low. This paper proposed an Infrared Tall Patch-Matrix (ITPM) model, which takes a novel perspective to construct a lower-rank patch matrix structure to improve the detection performance of low-contrast small targets. Specifically, we use a sliding split window to reconstruct the original image into a suitable low-rank structure called Tall Patch-Matrix, which can increase the detection rate of low-contrast small targets and suppress most noise. Second, the High Local Variance Low-Rank and Sparse Decomposition (ITPM-HiLV-LRSD) method is used to perform low-rank and sparse decomposition of the Infrared Tall Patch-Matrix, and a Thin Singular Value Decomposition (Thin SVD) optimization strategy is proposed to further reduce the computational complexity. Given the absence of open literature datasets for detecting infrared targets in low-contrast small scenarios, we created a Low-contrast Small Target Detection Dataset (LSTDD) comprising 600 infrared target detection images with varied sky backgrounds. This dataset simulates low-contrast small targets across different signal-to-noise ratios. To demonstrate the generalizability of our method, we also conducted experiments on a representative low-contrast subset of real-world images from the SIRST dataset. Compared with six state-of-the-art methods, our proposed method excels in detecting low-contrast small targets with superior performance. Full article

Accurate assessment of source-load complementarity and system regulation capacity is critical for secure dispatch and planning in high-penetration renewable power systems. Addressing limitations of existing methods—which rely heavily on static metrics, struggle to capture temporal and tail dependence characteristics, and provide insufficient support for dispatch decisions—this paper proposes a multi-level integrated evaluation framework. First, from a source—load matching perspective, we develop a novel complementarity metric, integrating real-time rate of change, temporal consistency, and tail dependency. An improved adaptive noise-complete set empirical mode decomposition combined with a hybrid Copula model is employed to isolate noise and to precisely quantify dynamic dependency structures. Second, we introduce the Minkowski measure and construct a net load fluctuation domain accounting for extreme fluctuations and coupling relationships. Subsequently, combining the Analytic Hierarchy Process (AHP) with probabilistic convolution enables multi-level comparative quantification of resource capacity and fluctuation domain requirements under varying confidence levels. Simulation results demonstrate that the proposed framework not only provides a more robust assessment of source-load complementarity but also quantitatively outputs the adequacy and risk level of system regulation capacity. This delivers hierarchical, actionable decision support for dispatch planning, significantly enhancing the engineering applicability of evaluation outcomes. Full article