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.

Due to the characteristics of small samples, high dimensions, and much noise, materials data often produce inconsistent results with those obtained from domain experts when used for machine learning modeling. For the whole process of machine learning, developing machine learning models embedding materials domain knowledge is a solution to this problem. The accuracy of materials data directly affects the reliability of data-driven materials performance prediction. Here, a data accuracy detection method incorporating materials domain knowledge is proposed by focusing on the data preprocessing stage in the machine learning application process. Firstly, a materials domain knowledge database is constructed based on the knowledge from materials experts. Secondly, it is coordinated with the data-driven data accuracy detection method to perform single-dimensional data accuracy detection based on the rule for value of descriptors, multi-dimensional data correlation detection based on the rule for correlation of descriptors, and full-dimensional data reliable detection based on multi-dimensional similar sample identification strategy from both data and domain knowledge perspectives. For the anomalous data identified at each stage, they are corrected by incorporating the materials domain knowledge. Furthermore, domain knowledge is incorporated into the whole process of the data accuracy detection method to ensure high accuracy of the dataset from the initial stage. Finally, experiments on the NASICON-type solid electrolyte activation energy prediction dataset demonstrate that this method can effectively identify anomalous data and make reasonable corrections. Compared with the original dataset, the prediction accuracy of all six machine learning models based on the revised dataset is improved to different degrees, among which R² achieves a 33% improvement on the optimal model.

Density functional theory calculations play an important role in the study of defects in halide perovskites. Although the traditional semi-local functionals (such as PBE) can obtain the band gaps close to the experiments, they fail to accurately describe the positions of the band edges. Utilizing more accurate hybrid functionals combined with the spin-orbit coupling (SOC) effect with full structure relaxation is considered to be necessary for the prediction of defect properties. There are two types of hybrid functionals in the literature, namely the screened HSE and the unscreened PBE0. In this study, taking the orthorhombic phase CsPbI₃ as an example, these methods were compared for the calculation of defect properties. The results show that there is no obvious difference between two methods for bulk properties, but qualitative differences appear for the defect properties. Most of the shallow-level defects predicted in the HSE calculations become deep-level defects in the PBE0 calculations. Meanwhile, there are qualitative differences between the defect transition levels and the Kohn-Sham levels. The origin of above differences lies in the fact that the Hartree-Fock exchange potential has long-range interaction. Therefore, in unscreened hybrid functionals, such as PBE0, it is more difficult to obtain convergent results with a manageable supercell size. In contrast, HSE exhibits a screening effect on the Hartree-Fock exchange potential and can obtain accurate defect energy levels using relatively small supercell sizes. Therefore, all results here demonstrate that the HSE hybrid functional owns a significant advantage in dealing with this problem even though a large Hartree-Fock mixing parameter (about 0.43) is needed.

MAX/MAB phases are a series of non-van der Waals ternary layered ceramic materials with a hexagonal structure, rich in elemental composition and crystal structure, and embody physical properties of both ceramics and metals. They exhibit great potential for applications in extreme environments such as high temperature, strong corrosion, and irradiation. In recent years, two-dimensional (2D) materials derived from the MAX/MAB phase (MXene and MBene) have attracted enormous interest in the fields of materials physics and materials chemistry and become a new 2D van der Waals material after graphene and transition metal dichalcogenides. Therefore, structural modulation of MAX/MAB phase materials is essential for understanding the intrinsic properties of this broad class of layered ceramics and for investigating the functional properties of their derived structures. In this paper, we summarize new developments in MAX/MAB phases in recent years in terms of structural modulation, theoretical calculation, and fundamental application research and provide an outlook on the key challenges and prospects for the future development of these layered materials.

Large-sized crystalline materials are the basic raw materials in semiconductors, lasers, and communications. Preparation of large-scale, high-quality crystalline materials has become a bottleneck restricting the development of related industries. Breaking through the preparation theory and technology of large-sized crystal materials is the key to obtaining high-quality large-sized crystals. Preparation process of crystal materials often undergoes nucleation and growth stages, including multiple processes at spatiotemporal scale: from atom/molecules, through clusters and nuclei, to bulk crystals. To further explore and accurately understand the crystal growth mechanism, we need intensively study the multiscale process,multi-scale in situ characterization techniques, and computational simulation methods. Among them, the latest in situ characterization methods for crystal growth includes optical microscopy, electron microscopy, vibration spectra, synchrotron radiation, neutron technology, and especially, machine learning method. Thus, the multi-scale computational simulation techniques for crystallization is introduced, for example, first principles calculation at atom/molecular scale, molecular dynamics simulation, Monte Carlo simulation, phase field simulation at mesoscopic scale, and finite element simulation at macroscopic scale. A single in situ characterization or simulation technique can only explore crystallization information over a specific time and space scale. To accurately and fully reflect the crystallization process, a combination of multi-scale methods is introduced. It can be speculated that the establishment of in situ characterization technology and computational simulation methods for the actual large-sized crystal growth environment will be the future development trend, which provides an important experimental and theoretical basis for developing crystallization theory and controlling crystal quality. Furthermore, it can be deduced that the combination of in situ characterization technology with machine learning and big data technology will be the trend for large-sized crystal growth.

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.