The development of traditional luminescent materials (such as cadmium-based quantum dots and lead halide perovskites) is intrinsically limited by their reliance on toxic heavy metals (e.g., Cd and Pb), which raises severe environmental and health risks throughout their lifecycles. Therefore, the transition toward eco-friendly alternatives, including cadmium-free quantum dots, lead-free halide perovskites, and rare-earth-doped phosphors, has become a pivotal research imperative. Currently, the design and optimization of such materials rely on inefficient trial-and-error experimental paradigms, which often fail to overcome critical bottlenecks in luminous efficiency, environmental stability, and interfacial compatibility. This review systematically outlines the current landscape and technical challenges of environmental-friendly luminescent materials. It highlights how computational techniques, particularly density functional theory, allow the accurate prediction of optoelectronic properties in core-shell structures and the elucidation of defect-induced non-radiative recombination mechanisms, thus facilitating rational material design and property optimization. In addition to theoretical calculations, data-driven technologies further accelerate material screening by leveraging standardized databases and machine learning models, having already yielded high-stability phosphors and high-efficiency narrowband emitters. Finally, an outlook on the synergy between computational and data-driven approaches to overcome existing research and development barriers is provided. Future efforts must focus on deepening the integration of these technologies to advance the practical deployment of environmental-friendly luminescent materials in display and lighting applications, thereby driving the sustainable transformation of the optoelectronics industry.

Phosphor materials are essential functional components in modern optoelectronics, playing a crucial role in next-generation displays, solid-state lighting, biomedical imaging, and sensing applications. However, the properties of materials are stringently required in multiple dimensions for practical applications. The conventional trial-and-error approach impedes the development of novel high-performance phosphors due to its long cycles and high costs. In recent years, the rapidly advancing machine learning (ML) methodology presents an innovative solution to addressing these bottlenecks. It not only dramatically accelerates the high-throughput virtual screening of candidate materials by establishing complex mappings among “composition-structure-property”, substantially improving efficiency, but also provides novel theoretical insights into structure-property correlations through algorithm-derived importance weights. This review systematically outlines the methodological framework for ML-driven phosphor development, covering key stages including data preparation, feature engineering, model selection, model evaluation and application. It further discusses specific considerations and corresponding strategies for each stage of the process. Subsequently, it reviews recent research progress in applying ML to predict key phosphor properties, encompassing critical metrics such as emission wavelength, full width at half-maximum (FWHM), thermal stability, fluorescence lifetime, centroid shift, and process optimization. Finally, this review addresses existing challenges in current research, such as scarcity of reliable data, complexity of materials properties, and difficulties in parameter quantification. The article outlines future development trends for the deep integration of artificial intelligence (AI) technology in luminescent materials research, aiming to provide a valuable reference for promoting the establishment of an “AI for Science” paradigm in the field of phosphor materials.

Thermal quenching is a key challenge in the application of fluorescent materials in solid state lighting devices. Herein, we report a perovskite phosphor RbZnF₃:Eu³⁺ exhibiting anti-thermal quenching behavior. Under violet excitation, this phosphor yields bright red emission. As the temperature rises, the luminescence intensity first increases up to 175 ℃ (i.e., anti-thermal quenching) and then decreases. When the temperature is above 200 ℃, the luminescence intensity falls below the value at room temperature. Comprehensive characterizations demonstrate that the observed anti-thermal quenching behavior is mainly due to the existence of defect levels. First-principles calculations show that Rb vacancy and F vacancy could be responsible for the observed defect levels. Finally, this study has fabricated a white light-emitting diode (LED) using the RbZnF₃:Eu³⁺ phosphor which verifies its potential application.

High-entropy ceramic (HEC) demonstrates exceptional thermal and mechanical properties, along with outstanding chemical stability, which can be attributed to their high entropy, lattice distortion, sluggish diffusion, and cocktail effects. However, the expansive compositional and structural space associated with HEC renders traditional trial-and-error methods time-consuming, costly and inadequate for investigation of complex systems. Thus, theoretical calculation has become an indispensable tool for addressing these challenges. To outline recent advances in theoretical calculation for HEC, this article focuses on prevalent calculation methods, including first-principles calculations, molecular dynamics, machine learning, and calculation of phase diagrams. Additionally, it discusses research paradigms such as high-throughput computing and performance descriptors, providing a comprehensive overview of their key roles and specific applications in HEC. The article first outlines fundamental characteristics and core effects of HEC, then turns to critically examine theoretical basis of these calculation methods, elaborating on their applications through specific examples in composition design, property prediction, microstructural parsing, and phase stability assessment. Finally, this paper summarizes the major challenges encountered in theoretical calculations in the study of multi-component systems, such as the scarcity of high-quality datasets and the ambiguity of structure-property relationships. It concludes with a forward-looking outlook on the development directions in this field, including data-driven design, cross-scale correlation, and extreme environment simulation.

Molecular dynamics simulations of high-entropy boride ceramics (HEBCs) in extreme high-temperature environments are constrained by limited accuracy and temperature stability of empirical force fields. In this work, a high-accuracy deep-learning potential (DP) was proposed and developed for (Hf_0.2Zr_0.2Ta_0.2Ti_0.2Nb_0.2)B₂ systems via first-principles calculations and deep learning method. It is shown that, through expanding datasets via the active learning strategy, the DP model stability under high-temperature conditions (i.e., ~3000 K) could be significantly enhanced. The developed DP achieves high accuracy while maintaining computational efficiency. Validation results from the developed DP manifest that predictions of the volumetric equation of state align well with first-principles calculations, demonstrating the model’s good scalability. The lattice constants and mechanical properties predicted by DP-enabled molecular dynamics simulations show excellent agreements with experimental observations, with relative errors within 2%. Furthermore, the simulations successfully reveal the anisotropic thermal expansion behavior of HEBCs and rectify the anomalous trends reported in previous research. Therefore, this developed DP model provides a reliable tool for atomic-scale simulations of high-entropy boride ceramics under extreme conditions, and holds significant scientific value for advancing the in-depth understanding of their high-temperature service behavior.

Continuous alumina fiber-reinforced silica ceramic matrix composites exhibit excellent properties, such as high-temperature oxidation resistance, high strength and high toughness. As a dual-use material for both military and civilian applications, they hold broad prospects in numerous fields, including aviation, aerospace and energy. However, domestic research currently still remains on its initial stage and is characterized by a primarily qualitative understanding of their mechanical property failure mechanisms. In this study, an improved liquid-phase impregnation method, which integrated the process characteristics of the Sol-Gel method and slurry impregnation method, was adopted to prepare continuous alumina fiber-reinforced silica composites with tunable porosity. Microstructure and composition of the typical composite were comprehensively characterized using different techniques. Mechanical properties of these composites with different densification degrees were tested and analyzed. By integrating porosity data obtained from computed tomography (CT) test with simulation calculation, a relationship model linking mechanical property failure of the composites to porosity and pore size parameters was established. The results indicated that composites prepared via the improved liquid-phase impregnation method had significantly enhanced mechanical properties due to the presence of pore defects and weak interfacial bonding. Notably, as the composite porosity increased from 2.2% to 15.2%, the tensile strength decreased from 24.5 MPa to 17.8 MPa. Further modeling and simulation analysis revealed that, at a pore defect radius of 250 μm, an increase in porosity from 4.5% to 13.5% led to a corresponding reduction in tensile strength from 27.2 MPa to 20.6 MPa, thereby validating rationality of the simulation model. The law that the n-th power of tensile strength shows a negative linear correlation with porosity, and the tensile strength exponent factor n is negatively linearly correlated with the pore defect radius r. These findings provide a research basis for the performance optimization and practical application of continuous alumina fiber-reinforced silica composites.

Continuous fiber-reinforced silicon carbide ceramic matrix composites utilized in hot-section components of high thrust-to-weight ratio aero-engines require protection via thermal/environmental barrier coatings (T/EBCs). To develop novel rare-earth oxide thermal barrier coating materials with low thermal conductivity, compatible thermal expansion coefficients, and excellent high-temperature phase stability, introduction of a high-entropy design concept offers a promising approach and opportunity for composition design and performance optimization. Addressing the challenges of structural modeling and property prediction for complex high-entropy ceramic systems, this study firstly introduces a novel high-entropy ceramic modeling strategy based on the special quasi-random structure (SQS) method. This strategy facilitates rapid prediction of complex ceramic properties while maintaining computational accuracy. Subsequently, crystal structures, elastic properties and thermophysical characteristics of four high-entropy rare-earth oxide materials are predicted and compared by integrating first-principles calculations. This research particularly elucidates regulatory effects and atomic-scale origins of different rare-earth compositions and Hf doping on the material’s low thermal conductivity performance. The research results provide scientific insights and fundamental data for theoretical simulation and material selection design of T/EBCs for aero-engine hot-section components.

Material property differences among components of solid oxide fuel cell (SOFC) lead to excessive stresses during cell fabrication and operation, among which functional gradient material electrodes have attracted attention for their ability to reduce residual and thermal stresses in SOFC. But so far, there is rare study on SOFC with functional gradient anode using numerical simulation of thermal stress. In this study, a multi-physics field coupling model of SOFC with complete structure was established by COMSOL Multiphysics 6.0. Based on multi-physics field coupling model and numerical simulation of the residual stresses and thermal stresses in SOFC, four different distribution curves were employed to characterize the component distribution of anode materials. The results show that the tensile stress of anode can be significantly reduced by using functional gradient material during fabrication at different temperatures, especially at room temperature. Compared with non-gradient distribution, the maximum tensile stress of the anode is reduced by 47.69% before reduction and 35.74% after reduction by using quadratic curve distribution. During the operation process, the heat generated by the electrochemical reaction and the convective heat transfer of gas leads to the temperature difference between inlet and outlet, resulting in significant stress concentration at inlet and outlet of the metal frame as well as at contact surface between rib and electrode. Functional gradient materials can significantly reduce the maximum stress on the anode, metal frame and electrolyte, which is particularly obvious when using quadratic curve distribution. Therefore, this research has potential theoretical significance and engineering value for designing and fabricating SOFCs.

Preparation of carbon-carbon composites through the chemical vapor infiltration (CVI) process, utilizing CH₄ and C₂H₅OH as precursors, can effectively improve the deposition rate and produce highly structured pyrolytic carbon. Understanding the reaction mechanism is essential for computational fluid dynamics (CFD) studies. Chemical reaction mechanisms typically involve numerous free radicals and reactions, and manually constructing such mechanisms based on experimental data alone risks omitting critical species and reactions. Hence, in this research, a thorough gas-phase pyrolysis kinetic mechanism for the CH₄+C₂H₅OH+Ar system was developed using the reaction mechanism generator (RMG). This mechanism included 31 core species and 214 core reactions, accurately predicting the evolution of major species' formation and consumption. The simulation results were consistent with experimental observations. Through a detailed analysis of the kinetics and sensitivity of reactants and critical products, reactions influencing the formation and consumption of crucial species were identified. Reaction pathway analysis further clarified relationships among different species, identifying core species within the mechanism. By simplifying the detailed mechanism based on sensitivity and rection pathway analysis at 1373 K and 10 kPa, a gas-phase kinetic mechanism was derived, composed of 18 species and 44 reactions. This streamlined model substantially boosts computational efficiency while retaining key species, providing a more convenient foundation for further CFD studies and applications.

Ceramic stereolithography has a broad prospect in preparation of Al₂O₃-SiO₂ composite ceramics through which the prepared textured Al₂O₃-SiO₂ composite still faces challenges in improving their slurry quality and light intensity distribution. Here, characteristics of a novel ceramic slurry with addition of alumina platelet, equiaxed alumina, and spherical silica were investigated. Comparative analysis on viscosity, sedimentation, curing characteristics, and curing accuracy of the slurry with different solid content was carried out. Simulation algorithm of ultraviolet (UV) light intensity distribution was developed, and theoretical simulation analysis of light intensity distribution in the slurry during light exposure was carried out. Results of the prepared textured porous Al₂O₃-SiO₂ composite with the characteristics of oriented alumina platelet showed that combination of alumina platelet with spherical powders endowed the slurry with low viscosity and shear shinning behavior at high solid content in volume (40%-45%) of which alumina platelet took 50%-60%. Under the same content of the total solid, increasing either amount of alumina platelet or amount of spherical silica could reduce the viscosity, leading to the increase of the slurry total sedimentation. Alumina platelet in the slurry could reduce blocking and scattering of UV light better than equiaxed alumina. Under the same exposure conditions, both decreasing content of the equiaxed alumina and increasing content of the spherical silica could increase curing thickness of the slurry, while increasing content of alumina platelet and spherical silica could adversely increase error in dimension. Numerical simulation results showed that alumina platelet with approximately horizontal distribution showed week effect on UV light blocking and deflection, while that with approximately vertical distribution showed indeed guiding effect on UV light. The variation of the mean UV intensity at the upper boundary of the model was close to that of the curing thickness. Therefore, the established model can provide theoretical support for the experimental value of curing thickness.

The integration of ceramic matrix composites with environmental barrier coatings (CMC-EBC) represents the most promising thermal structural material system in the aerospace field. This paper provides an overview of the advancements in research on the failure mechanisms and numerical models of CMC-EBC. It commences with a concise review of the evolution and primary fabrication techniques of CMC-EBC material system. Subsequently, it summarizes the typical damage modes and failure mechanisms of CMC-EBC under operational conditions, identifying that the interplay between the CMC preform structure, porosity defects, and EBC inner cracks is a critical determinant of the material’s lifespan. However, current mechanistic studies are chiefly focused on the performance evaluation of the coating itself and its susceptibility to environmental factors, disregarding the synergistic effects of the coating and composite architecture during damage progression. This review proceeds with an examination of the history and current status of research on failure simulation and prediction models for CMC-EBC, highlighting issues related to modeling environmental factors and simulating coupled damage evolution. Though much effort has directly developed separate failure models for CMC and EBC, predicting the failure of CMC-EBC components should account for the coupling effects between damage evolution and microstructure. In conclusion, this review offers a perspective on development and service performance prediction methods for CMC-EBC system, which points out that considering the interdependent failure modes of the CMC substrate and EBC is pivotal. Integrated design and analysis of structural and functional aspects are emerging trends in CMC-EBC component research.

The poor oxidation resistance and structural stability of Ti₃C₂(OH)₂ largely limit its wide applications. In this work, the surface adsorption behaviors of oxygen atoms on Ti₃C₂(OH)₂, polyaniline (PANI) and PANI/Ti₃C₂(OH)₂ composite were systematically studied and compared by first-principles calculation method. The simulation results suggest that the existence of -OH functional group can change the active sites on Ti₃C₂ matrix. Thereby the oxidation resistance and the structural stability of Ti₃C₂ matrix can be improved in some extent. Furthermore, after modifying Ti₃C₂(OH)₂ by PANI, the adsorption activity of PANI is much larger. Meanwhile, the adsorption energy of oxygen on the Ti₃C₂(OH)₂ end is significantly decreased, which is caused by the electron transfer from PANI to Ti₃C₂(OH)₂, as confirmed by the Bader charge calculation. Therefore, the oxidation resistance and the structural stability of the PANI modified Ti₃C₂(OH)₂ composite are improved by sacrificing PANI, since oxygen prefers to adsorb and attack PANI firstly. This work provides theoretical guidelines for the improvement of oxidation resistance, structural and chemical stability of MXene.

Among all options of carbon neutrality, conversion of CO₂ into valuable chemicals by electrocatalytic reduction exhibit outstanding performance. However, due to the numerous products and complex pathways of CO₂ electrocatalytic reduction, the exact factors affecting the activity of CO₂ electrocatalytic reduction have not yet been identified. In addition, the CO₂ electrocatalytic reduction process is often accompanied by hydrogen evolution reaction (HER). Therefore, it is still challenging to design a catalyst with high selectivity and high activity for specific product. Herein, this study systematically investigated the potential of 3d transition metal-based single-atom catalysts (SACs) positioned at graphene single vacancies (TM@C_SV), as well as double vacancies (TM@C_DV), for the CO₂ reduction reaction (CO₂RR) using first-principles. The exploration encompassed substrate stability, CO₂ adsorption, and the HER as the main competing reaction. Through the careful screening of 20 catalysts formed by Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu and Zn doped graphene defects, several promising catalysts were identified: Sc@C_SV situated on graphene single vacancies, Sc@C_DV and Ti@C_DV situated on graphene double vacancies. They could not only effectively adsorb CO₂ molecules, but also inhibit HER, the main competing reaction. In assessing their performance in CO₂RR, all exhibited selectivity toward HCOOH. Notably, Sc@C_DV demonstrated the best selectivity, requiring the lowest ΔG (0.96 eV) for efficient CO₂ conversion to HCOOH. Electronic structure analysis revealed that Sc@C_DV outperforms due to its optimal balance between ΔG of hydrogenation and the product desorption achieved through a moderate number of active electrons.

In the industrial landscape, the well-established Haber-Bosch method is employed for the catalytic synthesis of ammonia (NH₃) from hydrogen and nitrogen gases, necessitating elevated temperatures (400-600 ℃) and high pressures (150-300 atm, 1 atm= 0.101325 MPa). In response to the imperative to reduce energy consumption and environment impact imposed by this synthetic process, significant research efforts have converged on realizing NH₃ synthesis under ambient conditions. This study delves into the realm of N₂ electrocatalytic reduction to NH₃, using density functional theory (DFT) calculations to explore the feasibility of employing graphene co-doped with a combination of transition metal elements (e.g., Fe, Nb, Mo, W, and Ru) and non-metal elements (e.g., B, P, and S) as catalyst for ammonia synthesis. The findings underscore that Mo and S co-doped graphene (Mo/S graphene) demonstrates an exceptionally low electrode potential of 0.47 V for NH₃ synthesis, with the key rate-controlling step centered around the formation of the intermediate *NNH. Especially, the ammonia synthesis potential is found to be lower than the hydrogen evolution potential (0.51 V), conclusively affirming the selectivity of nitrogen reduction to ammonia. Furthermore, through ab initio molecular dynamics calculations, the study attests to the remarkable thermodynamic stability of the Mo/S co-doped graphene system under room temperature conditions. Notably, electronic structure analysis validates that the ability of electron communication of the transition metal plays a pivotal role in dictating the efficiency of N₂ electrocatalytic reduction. It can be tactically optimized through controlled modulation of the influence of the non-metal element on the coordination environment of the transition metal, thus substantially enhancing catalytic performance.

Nowadays, artificial intelligence (AI) is playing an increasingly important role in human society. Running AI algorithms represented by deep learning places great demands on computational power of hardware. However, with Moore's Law approaching physical limitations, the traditional Von Neumann computing architecture cannot meet the urgent demand for promoting hardware computational power. The brain-inspired neuromorphic computing (NC) employing an integrated processing-memory architecture is expected to provide an important hardware basis for developing novel AI technologies with low energy consumption and high computational power. Under this conception, artificial neurons and synapses, as the core components of NC systems, have become a research hotspot. This paper aims to provide a comprehensive review on the development of oxide neuron devices. Firstly, several mathematical models of neurons are described. Then, recent progress of Hodgkin-Huxley neurons, leaky integrate-and-fire neurons and oscillatory neurons based on oxide electronic devices is introduced in detail. The effects of device structures and working mechanisms on neuronal performance are systematically analyzed. Next, the hardware implementation of spiking neural networks and oscillatory neural networks based on oxide artificial neurons is demonstrated. Finally, the challenges of oxide neuron devices, arrays and networks, as well as prospect for their applications are pointed out.

Heteroepitaxy provides an effective path for the synthesis of diamond wafers. After more than 20 years of development, the diamond nucleation and growth technology on iridium substrates has enabled to prepare crystals with a maximum diameter of 3.5 inches, which opens a door to application diamond as ultimate semiconductor in the future chip industry. However, a series of problems that occur on heterogeneous substrates, such as surface nucleation, bias process window, and diamond epitaxial growth, need to overcome from the perspective of growth thermodynamics. In this study, aiming at the key issue how diamond can achieve epitaxial nucleation and growth in chemical vapor deposition atmosphere, a simulation study was carried out on the nucleation and growth process of diamond at the atomic scale based on the first-principle calculation. The results show that the adsorption of C atoms on the surface of the Ir substrate is more stable than that on the bulk phase, which indicates that diamond nucleation can only occur on the substrate surface. The number of C atoms of sp³ hybridization in the amorphous hydrogenated carbon layer increases firstly and then decreases with the increase of ion kinetic energy under ion bombardment, confirming the existence of the ion kinetic energy or bias voltage window in the high-density nucleation of diamond. The interfacial binding energy is the lowest (about -0.58 eV/C) when diamond is epitaxially grown along the Ir substrate, meaning that the interface binding energy is the decisive thermodynamic factor for the epitaxial growth. In conclusion, this study clarifies the thermodynamic mechanism of single crystal diamond epitaxial growth under the bias-assisted ion bombardment, and points out a great significant guidance for the growth of diamond and other carbon based semiconductors.

As a fundamental light source and a good window for atmospheric transmission, the mid-infrared 3 μm lasers have led many promising applications. The rare earth doped crystalline materials, such as erbium doped crystals, are some of the most important routes for generation of the lasers. However, they have an intrinsic shortcoming of self-termination because of their short lifetime of ⁴I_11/2 and longer lifetime of ⁴I_13/2. To eliminate this effect, a high concentration doping method is usually adopted to change the energy transfer process to decrease ⁴I_13/2 lifetime. The efficiency and output power of Er³⁺-doped crystals were thus limited due to their degraded thermal properties. Trivalent erbium ions are easily clustering in fluoride crystals. Distances among the ions are short and therefore energy transfer processes could be significantly improved in the crystals even doping with low concentrations. Low doping concentrations could also alleviate the thermal effect in laser operations, which enable the erbium doped fluorides to be promising candidates for high power and high efficiency mid-infrared lasers. However, connection of spectral properties and erbium clusters is unknown. Here, the first principles calculation is utilized to model the erbium ion clusters in CaF₂, SrF₂ and PbF₂ crystals, concerning the absorption and photoluminescence properties. The results reveal that spectral properties and structures of the erbium clusters, evolve gradually with matrix crystals. Relationship between spectral properties and optical erbium clusters is determined qualitatively, which could be used to design new erbium doped mid-infrared lasers.

Zr₂SB, Hf₂SB, Zr₂SeB, Hf₂SeB, and Hf₂TeB are all recently discovered S-group elements containing MAX-phase borides, which attract much attention since the MAX phase borides are significantly unlike the typical MAX phases. Here, the phase stability, mechanical properties and thermal properties of MAX phase borides (M = Zr, Hf, A = S, Se, Te) were studied by using first principles and "linear optimization method", bond stiffness model and quasi-simple harmonic approximation. The results of the theoretical analysis were consistent with the currently available experimental results. Only M₂AB was found to be stable after thermodynamic and intrinsic stability analysis. The shorter M−A bond and M−B bond lengths cause bond stiffness of Hf lineage higher than that of Zr, which also leads to the higher hardness of Hf lineage compound than that of Zr. the A site element goes from S to Se and to Te, the bond lengths of M−B and M−A are gradually increased, which lead to decrease in the elastic modulus. Moreover, the bulk modulus of these compounds is determined by their average chemical bond stiffness. Importantly, the high k_min/k_max (stiffness ratio of the weakest and the strongest bonds) shows that these MAX phases are inherently brittle, different from conventional MAX phase. Including the contribution of lattice vibration (phonon) and electron excitation, the isobaric heat capacity and heat expansion coefficient of M₂AB increase rapidly with increasing the temperature below 300 K and then the rise rate gradually decreases, similar to other MAX phases. Lower bond stiffness results in an overall higher TEC of MAX phase borides in the Zr lineage than in the Hf lineage. The TEC values of these compounds in the 300−1300 K interval are consistent with most of the MAX and MAB phases.

Spent hydrogenation catalysts are an important source of regeneration catalysts due to their large waste volume and high particle integrity. Most of the existing recovery technologies focusing on recovering valuable metals limit studies on the recovery of carrier particles. This study addresses the key challenge of ineffective classification of rod-shaped spent catalyst particles via traditional sieving due to their length-to-diameter ratios exceeding standard specifications. A fluidized bed classification process is innovatively proposed, and a coupled computational fluid dynamics (CFD) and discrete element method (DEM) simulation combined with response surface methodology (RSM) is employed to systematically elucidate the intrinsic mechanisms and optimization principles of fluidized bed classification. The results demonstrate that a fluidized bed enables efficient classification of particles with varying aspect ratios via gas-solid fluidization. Gas velocity is identified as the dominant factor influencing classification efficiency, followed by feed flow, whereas inlet height exhibits a negligible impact. A critical feed flow threshold exists under specific gas velocity and inlet height; exceeding this threshold leads to a decline in classification efficiency. By establishing a Box-Behnken design (BBD) model, optimal conditions are identified as a gas velocity of 10.45 m/s, a feed flow of 7.50 t/h, and an inlet height of 3.50 m, achieving 100% classification efficiency. This study clarifies the multi-physics coupling mechanism in fluidized bed classification and provides theoretical guidance for pre-classification processes of carrier particles during spent hydrogenation catalyst recycling.

The novel In-based MAX phase Zr₃InC₂ has recently attracted considerable attention for its excellent physical properties, yet investigations into its behaviour under high pressure remain limited. This study systematically investigates effects of pressure on crystal structure, mechanical properties, electronic structure, and thermodynamic behavior of the novel MAX phase Zr₃InC₂ using first-principles calculations based on density functional theory (DFT). Comparative analysis with Zr₃AlC₂ reveals how substituting Al with In at the A-site influences structural and physical properties, as well as responses under high-pressure conditions. Calculated lattice parameters for both Zr₃InC₂ and Zr₃AlC₂ show good agreement with previous experimental reports. Results indicate pronounced anisotropic compression, with significantly higher compressibility along the c-axis than the a-axis. Elastic constants and phonon dispersion curves confirm mechanical and dynamic stability of Zr₃InC₂ up to 50 GPa. Poisson’s ratio analysis suggests brittle behavior at ambient pressure with ductility first appearing at 40 GPa. Discrepancy between the Poisson’s ratio and the Cauchy pressure at 50 GPa suggests that Zr₃InC₂ may be near the critical region of a brittle-to-ductile transition under high pressure. Compared with Zr₃AlC₂, Zr₃InC₂ exhibits greater sensitivity in mechanical properties under high pressure. Electronic structure calculations reveal its metallic nature. Thermodynamic analysis shows a relatively low thermal expansion coefficient at ambient pressure, while increased pressure leads to a significant rise in Debye temperature and minimum thermal conductivity. These findings highlight tunability of Zr₃InC₂’s thermodynamic properties under pressure, offering theoretical support for potential high-temperature applications.

As an anode for lithium-ion batteries, silicon material has the advantage of high energy density. However, the volume effect during charge-discharge cycles causes instability in the active coating's surfaces and diffusion stress induced by internal polarization, leading to inevitable structural degradation and capacity fading. Inspired by functionally gradient materials, this study proposed a five-layer composite gradient silicon electrode. Experiments and multi-scale electro-chemo-mechanical coupled model demonstrate that the designed symmetric and linear gradient silicon electrodes effectively mitigate mechanochemical coupled degradation, showing superior cycling and rate performance compared to traditional uniform electrode. Specifically, the symmetric gradient electrode retains a specific capacity of 2065 mAh·g^-1 after 100 cycles at 0.2C (1C=2.65 mA·cm^-2) rate, with a capacity retention rate of 81%, while that of uniform electrode is 51%. The linear gradient electrode exhibits an average discharge capacity 1.5 times that of the uniform electrode at 1C rate. Moreover, both types of gradient electrodes demonstrate smaller impedance variations before and after cycling compared to the uniform electrode. These composite gradient electrodes are implemented through an innovative multi-layer coating process, and improved structural stability and electrochemical performance without material modifications, providing a reference for designing and fabricating high-performance silicon electrodes.

Lithium niobate crystal, combining its piezoelectric, nonlinear, electro-optical, and photorefractive properties, along with its stable physicochemical characteristics, has great potential for applications in integrated optics. However, designing thermal field for large-size lithium niobate crystal growth presents considerable challenges, considering the crucible shape being an important factor that significantly influences the crystal growth in which the diameter and height are compulsively restricted to the factors such as load capacity and crystal diameters. In this study, 4-inch congruent lithium niobate crystals were grown by using crucibles with two types of bottom shapes. The impacts of crucible bottom shape on the axial temperature gradient within the crystal and the melt near the crystal-melt interface, and the temperature distribution within the melt below the crystal-melt interface, were analyzed by numerical simulation. The impact of the crucible bottom shape on crystal growth was analyzed in contrast to crystal growth results. It is found that changes in the crucible bottom shape lead to variations in the temperature difference along the crucible sidewall and the temperature gradient within the melt, thereby altering the strength of natural convection in the melt. Compared to crucible with slipped bottom corner, the axial temperature gradient near the crystal-melt interface within the crystal and melt is large when using the crucible with curved bottom corner, and the axial temperature gradient within the melt below the crystal-melt interface is also large, and the natural convection is strong. Therefore, this study helps to solve the problems such as the unwanted crystal growth ridge spreading and the overgrowth of cellular interface.