All-inorganic perovskite CsPbX₃ (X = Cl, Br, I) nanocrystals (NCs) have emerged as promising candidates for light-emitting diode (LED) displays due to their outstanding photophysical properties. However, their practical application remains hindered by poor stability and the inherent toxicity of Pb²⁺. In this study, we present a two-step heating method to synthesize CsPb_1−xZn _xBr₃ NCs with enhanced optoelectronic performance and uniform dispersion. The optimized Zn²⁺-doped NCs achieve a photoluminescence quantum yield (PLQY) of 86%, with a reduction in lattice spacing from 0.384 to 0.365 nm, attributed to increased perovskite lattice formation energy and effective surface passivation. To further improve stability, a silica (SiO₂) shell is introduced via surface modification with (3-aminopropyl) triethoxysilane (APTES), forming CsPb_0.7Zn_0.3Br₃@SiO₂ core–shell NCs. At an optimal APTES/B-site metal ion molar ratio of 1.8, the PLQY increases to 96%. The SiO₂ encapsulation significantly enhances environmental stability, with coated NCs retaining 43% of their initial photoluminescence (PL) intensity after immersion in water for 36 h, compared to only 5% for uncoated NCs. Furthermore, after ethanol treatment for 210 min, the coated NCs retain 39% of their initial PL intensity, while the uncoated counterparts retain merely 7%. The enhanced stability and luminescence performance of CsPb_0.7Zn_0.3Br₃@SiO₂ NCs make them highly promising for LED applications. White light-emitting diodes (WLEDs) fabricated using these NCs exhibit a color rendering index (CRI) of 78.2, a correlated color temperature (CCT) of 5470 K, and a luminous efficiency (LE) of 54.2 lm/W, demonstrating significant potential for next-generation display and lighting technologies.

Large-scale underground projects need accurate in-situ stress information, and the acoustic emission (AE) Kaiser effect method currently offers lower costs and streamlined procedures. In this method, the accuracy and speed of Kaiser point identification are important. Thus, this study aims to integrate chaos theory and machine learning for accurately and quickly identifying Kaiser points. An intelligent model of the identification of AE partitioned areas was established by phase space reconstruction (PSR), genetic algorithm (GA), and support vector machine (SVM). Then, the plots of model classification results were made to identify Kaiser points. We refer to this method of identifying Kaiser points as the partitioning plot method based on PSR–GA–SVM (PPPGS). The PSR–GA–SVM model demonstrated outstanding performance, which achieved a 94.37% accuracy rate on the test set, with other evaluation metrics also indicating exceptional performance. The PPPGS identified Kaiser points similar to the tangent-intersection method with greater accuracy. Furthermore, in the feature importance score of the classification model, the fractal dimension extracted by PSR ranked second after accumulated AE count, which confirmed its importance and reliability as a classification feature. The PPPGS was applied to in-situ stress measurement at a phosphate mine in Guizhou Weng’an, China, to validate its practicability, where it demonstrated good performance.

Fe–Cr–Ni austenitic alloys are extensively utilized in the hot-end components of nuclear light water reactors, turbine disks, and gas compressors. However, their low strength at elevated temperatures limits their engineering applications. In this study, a novel precipitation-strengthened alloy system is developed by incorporating Al and Si elements into a FeCrNi equiatomic alloy. The results indicate that the FeCrNiAl_xSi_x (at%, x = 0.1, 0.2) alloys possess heterogeneous precipitation structures that feature a micron-scale σ phase at the grain boundaries and a nanoscale ordered body-centered cube (B2) phase within the grains. An exceptional strength-ductility synergy across a wide temperature range is achieved in FeCrNiAl_0.1Si_0.1 alloys due to grain refinement and precipitation strengthening. Notably, a yield strength of 693.83 MPa, an ultimate tensile strength of 817.55 MPa, and a uniform elongation of 18.27% are attained at 873 K. The dislocation shearing mechanism for B2 phases and the Orowan bypass mechanism for σ phase, coupled with a high density of nano-twins and stacking faults in the matrix, contribute to the excellent mechanical properties at cryogenic and ambient temperatures. Moreover, the emergence of serrated σ phase and micro-twins in the matrix plays a crucial role in the strengthening and toughening mechanisms at intermediate temperatures. This study offers a novel perspective and strategy for the development of precipitation-hardened Fe–Cr–Ni austenitic alloys with exceptional strength–ductility synergy over a broad temperature range.

Flip-flow screens offer unique advantages in grading fine-grained materials. To address inaccuracies caused by sensor vibrations in traditional contact measurement methods, we constructed a non-invasive measurement system based on electrical and optical signals. A trajectory tracking algorithm for the screen-body was developed to visually measure the kinematics. Employing the principle of laser reflection for distance measurement, optical techniques were performed to capture the kinematic information of the screen-plate. Additionally, by using Wi-Fi and Bluetooth transmission of electrical signals, tracer particle tracking technology was implemented to electrically measure the kinematic information of mineral particles. Consequently, intelligent fusion and perception of the kinematic information for the screen-body, screen-plate, and particles in the screening system have been achieved.

Doping small amounts at the A-site or B-site of SmCrO₃ ceramics is a promising approach for modifying their microstructure, as well as their magnetic and dielectric properties. In this study, polycrystalline ceramics of Sm_1−xNi_xCrO₃ (x = 0, 0.05, and 0.20) and SmCr_1−yNi_yO₃ (y = 0.05 and 0.20) were synthesized via a conventional solid-state reaction. X-ray diffraction validated that all the doped ceramics maintained an orthorhombic crystalline structure consistent with the Pbnm space group. Furthermore, X-ray photoelectron spectroscopy demonstrated the presence of Ni²⁺ ions in the doped specimens. Notably, doping resulted in significant enhancement of low-temperature magnetic properties, particularly in samples doped at the A-site, such as Sm_0.80Ni_0.20CrO₃. Compared with the pristine sample, the maximum magnetization of Sm_0.80Ni_0.20CrO₃ increased by approximately 60.9% and 93.5% in the zero-field cooling and field-cooling modes, respectively, in an external magnetic field of 100 Oe. Furthermore, the dielectric constants of the Ni-doped ceramics initially exceeded that of the pristine sample as the temperature increased. At equivalent doping ratios, A-site doping demonstrated superior performance over B-site doping, including higher magnetization, lower dielectric loss, and enhanced electrical quality factors.

Digital light processing (DLP) is a crucial additive manufacturing (AM) technique for producing high-precision ceramic components. This study aims to optimize the formulation of Si₃N₄ slurry to enhance both its performance and manufacturability in the DLP process, and investigate key factors such as particle size distribution, photopolymer resin monomer ratios, and dispersant types to improve the slurry’s rheological properties. Through these optimizations, a photosensitive Si₃N₄ slurry with 50vol% solid content was developed, exhibiting excellent stability, and low viscosity (2.48 Pa·s at a shear rate of 12.8 s⁻¹). The effects of gas-pressure sintering on the material’s phase composition, microstructure, and mechanical properties were further explored, revealing that this technique significantly increases the flexural strength of the green sample from (109 ± 10.24) to (618 ± 42.15) MPa. The sintered ceramics exhibited high hardness ((16.59 ± 0.05) GPa) and improved fracture toughness ((4.45 ± 0.03) MPa·m^1/2). Crack trajectory analysis revealed that crack deflection, crack bridging, and the pull-out of rod-like β-Si₃N₄ grains, are the main toughening mechanisms, which could effectively mitigate crack propagation. Among these mechanisms, crack deflection and bridging were particularly influential, significantly enhancing the fracture toughness of the Si₃N₄ matrix. Overall, this research highlights how monomer formulation and gas-pressure sintering strengthen the performance of Si₃N₄ slurry in the DLP three-dimensional printing technique. This work is expected to provide new insights for fabricating complex Si₃N₄ ceramic components with superior mechanical properties.

Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ modification of hard rocks. This study proposes an intelligent approach for predicting rock strength and cuttability. A database comprising 132 data sets is established, containing cutting parameters (such as cutting depth and pick angle), cutting responses (such as specific energy and instantaneous cutting rate), and rock mechanical parameters collected from conical pick-cutting experiments. These parameters serve as input features for predicting the uniaxial compressive strength and tensile strength of rocks using regression fitting and machine learning methodologies. In addition, rock cuttability is classified using a combination of the analytic hierarchy process and fuzzy comprehensive evaluation method, and subsequently identified through machine learning approaches. Various models are compared to determine the optimal predictive and classification models. The results indicate that the optimal model for uniaxial compressive strength and tensile strength prediction is the genetic algorithm–optimized backpropagation neural network model, and the optimal model for rock cuttability classification is the radial basis neural network model.

Advanced processes for peroxymonosulfate (PMS)-based oxidation are efficient in eliminating toxic and refractory organic pollutantsfrom sewage. The activation of electron-withdrawing HSO₅ ⁻ releases reactive species, including sulfate radical (·SO₄ ⁻), hydroxyl radical (·OH), superoxide radical (·O₂ ⁻), and singlet oxygen (¹O₂), which can induce the degradation of organic contaminants. In this work, we synthesized a variety of M-OMS-2 nanorods (M = Co, Ni, Cu, Fe) by doping Co²⁺, Ni²⁺, Cu²⁺, or Fe³⁺ into manganese oxide octahedral molecular sieve (OMS-2) to efficiently remove sulfamethoxazole (SMX) via PMS activation. The catalytic performance of M-OMS-2 in SMX elimination via PMS activation was assessed. The nanorods obtained in decreasing order of SMX removal rate were Cu-OMS-2 (96.40%), Co-OMS-2 (88.00%), Ni-OMS-2 (87.20%), Fe-OMS-2 (35.00%), and OMS-2 (33.50%). Then, the kinetics and structure–activity relationship of the M-OMS-2 nanorods during the elimination of SMX were investigated. The feasible mechanism underlying SMX degradation by the Cu-OMS-2/PMS system was further investigated with a quenching experiment, high-resolution mass spectroscopy, and electron paramagnetic resonance. Results showed that SMX degradation efficiency was enhanced in seawater and tap water, demonstrating the potential application of Cu-OMS-2/PMS system in sewage treatment.

The mechanical behavior of cemented gangue backfill materials (CGBMs) is closely related to particle size distribution (PSD) of aggregates and properties of cementitious materials. Consequently, the true triaxial compression tests, CT scanning, SEM, and EDS tests were conducted on cemented gangue backfill samples (CGBSs) with various carbon nanotube concentrations (P _CNT) that satisfied fractal theory for the PSD of aggregates. The mechanical properties, energy dissipations, and failure mechanisms of the CGBSs under true triaxial compression were systematically analyzed. The results indicate that appropriate carbon nanotubes (CNTs) effectively enhance the mechanical properties and energy dissipations of CGBSs through micropore filling and microcrack bridging, and the optimal effect appears at P _CNT of 0.08wt%. Taking PSD fractal dimension (D) of 2.500 as an example, compared to that of CGBS without CNT, the peak strength (σ _p), axial peak strain (ε _1,p), elastic strain energy (U _e), and dissipated energy (U _d) increased by 12.76%, 29.60%, 19.05%, and 90.39%, respectively. However, excessive CNTs can reduce the mechanical properties of CGBSs due to CNT agglomeration, manifesting a decrease in σ _p, ε _1,p, and the volumetric strain increment (Δε _v) when P _CNT increases from 0.08wt% to 0.12wt%. Moreover, the addition of CNTs improved the integrity of CGBS after macroscopic failure, and crack extension in CGBSs appeared in two modes: detour and pass through the aggregates. The σ _p and U _d firstly increase and then decrease with increasing D, and porosity shows the opposite trend. The ε _1,p and Δε _v are negatively correlated with D, and CGBS with D = 2.150 has the maximum deformation parameters (ε _1,p = 0.05079, Δε _v = 0.01990) due to the frictional slip effect caused by coarse aggregates. With increasing D, the failure modes of CGBSs are sequentially manifested as oblique shear failure, “Y-shaped” shear failure, and conjugate shear failure.