Al2O3-Y3Al5O12 (Al2O3-YAG) eutectic ceramics with the refined and homogeneous microstructure were fabricated rapidly using a novel strategy of combustion synthesis chemical furnace, which combined self-propagation high-temperature heating method with a self-designed rapidly unidirectional solidification equipment. The microstructure, crystallographic orientation relationships and mechanical properties of Al2O3-YAG eutectics were investigated. The results show that the Al2O3-YAG eutectic ceramic features a complex homogeneous 'Chinese script' irregular morphology. The interfacial crystallographic orientation relationships between the Al2O3 and YAG phases are: [111] YAG & Vert;[1100] Al2O3 and (110) YAG & Vert;(1120) Al2O3. Moreover, transition from the irregular eutectic to colony structure was recognized in a cross-section taken 1 mm from the bottom of the Al2O3-YAG ingot. The colony structure consists of the regular lamellar-like or rod-like structure in the central zone and the coarse irregular structure in the boundary region. The relative density, Vickers hardness and fracture toughness of Al2O3-YAG eutectic are measured to be 98.2%, 21.1 GPa and 3.24 MPa m1/2, respectively. The superior fracture toughness is ascribed to the homogeneous and fine eutectic network structure which retards the crack propagation.

Al2O3–Y3Al5O12 (Al2O3–YAG) eutectic ceramic with a three-dimensionally continuous interpenetrating network structure was successfully fabricated via a rapidly heated combustion synthesis chemical furnace. The effects of combustion agent mass on the microstructural evolution and solidification behavior of Al2O3–YAG eutectic ceramics were systematically investigated. The results indicate that the combustion agent mass determines the undercooling degree, melt viscosity and solidification velocity, thereby regulating the eutectic morphology from conventional ”Chinese script” structure to uniform cellular structure. When the combustion agent mass is 180 g, the inter-lamellar spacing of Al2O3–YAG eutectic reaches 1.19 μm, and the growth rate of Al2O3–YAG eutectic is up to 70.62 μm/s. Meanwhile, the Al2O3–YAG eutectic ceramic achieves excellent comprehensive mechanical properties, with a Vickers hardness of 21.58 GPa and a fracture toughness of 3.36 MPa·m1/2. This is attributed to the ultra-fine homogeneous microstructure and multiple synergistic toughening mechanisms, including crack deflection, crack bridging and crack arrest. This work demonstrates that the combustion synthesis rapid heating method is an efficient approach to fabricate high-performance Al2O3–YAG eutectic ceramics, providing a feasible strategy for rapid preparation of fine-structured high-temperature structural ceramics.

Cu-Mn co-doped CeO2 photocatalyst was successfully synthesized by the sol-gel method to assess its capability in degrading tetracycline.XRD and TEM results showed that Cu and Mn were successfully co-doped into CeO2 without forming heterostructure,XPS and photoelectrochemical results revealed that Mn ions doping amplified the generation of photo-induced charge carriers,while Cu ions doping significantly facilitated the interfacial charge transfer process.Notably,the optimized Cu3Mn2CeO2 nanoparticles exhibited the highest TC removal efficiency,achieved a rate of 78.18%and maintained a stable cycling performance.

The practical implementation of Li-S batteries is limited by poor S utilization as a result of the soluble polysulfide shuttle effect combined with slow reaction kinetics. To enhance S utilization, in our present work, a CoFe2O4-based interlayer is introduced between the S cathode and commercial separator, which effectively suppresses active S species diffusion from the cathode region, thanks to the adsorption capability and catalytic activity of CoFe2O4 to lithium polysulfides. Notably, introducing a CoFe2O4-based interlayer, the Li-S battery shows a high initial discharge capacity (905.8 mAh g-1) at 3C with a capacity decay rate of 0.095% per cycle for 500 cycles.

A SrTiO3@SiC photocatalytic ceramic membrane with hierarchical pores was fabricated via hydrothermal in-situ growth optimized by ethylene glycol, providing strong interfacial bonding for enhanced durability and photo-catalytic performance. During filtration, the flux initially declined due to tetracycline accumulation but was fully restored after visible-light irradiation. The membrane achieved 96.54 % tetracycline removal with a flux of 8.63 L center dot m-2 center dot h-1and 98.52 % flux recovery through synergistic membrane separation and photocatalytic degradation, where superoxide radicals (center dot O2-) and holes (h+) mineralized pollutants and prevented fouling. This integration of separation and photocatalysis enables effective pollutant mineralization and in-situ membrane self-cleaning, offering a sustainable, chemical-free approach for antibiotic wastewater treatment and advancing environmental remediation technologies.

Silicon carbide ceramic membranes are known as a new generation of membrane products due to their excellent chemical stability and high flux advantages. This article, based on the PatSnap patent analysis, analyzes the global patents of silicon carbide ceramic membranes, preparation methods, devices and equipment, application-related patents, and applicants from perspectives of patent applications, patent layouts, and international patent classification technical categories. It concludes that the technological hotspots in the field of silicon carbide ceramic membranes are concentrated on improving separation efficiency through separation applications, providing a reference for the technical improvement, application, and patent layout of silicon carbide ceramic membranes.

Overcoming the challenges associated with achieving high uniformity and connectivity of pore channels in ceramic membranes, we designed silicon carbide ceramic membrane derived from the recrystallization process based on the Dinger-Funk equation of the closest -packing model with various grain grading. Furthermore, the effects of particle size distribution on the resulting microstructure and pore architecture of the ceramic membrane was also explored. The findings corroborated the critical importance of raw material particle size distribution in controlling pore size distribution and morphology. After sintering at 1900 degrees C, the silicon carbide ceramic membrane, benefiting from ideal particle packing, exhibited a remarkably uniform pore structure. Notably, the most probable pore size constituted over 70 %, while achieving an open porosity of 51.3 % even without the addition of pore -forming agents. The silicon carbide ceramic membrane also demonstrated exceptional hydrophilicity (water contact angle:-0 degrees), impressive water permeation (1210 L m-2 h-1 & sdot;bar- 1), coupled with efficient turbidity removal (-100 %) in carbon black wastewater treatment applications. Additionally, membrane regeneration proved effective using a dilute NaOH solution backwash, achieving a flux recovery efficiency of 98 %. This strategy had directive significance for designing high -performing silicon carbide ceramic membranes.

Machine learning (ML) are currently making significant impact and bringing tremendous opportunities in material science, and has proven to be an effective tool for accelerating the discovery and advancing the development of lithium-ion batteries materials. In this study, we propose a modified version of the crystal convolutional neural network (CGCNN) algorithm, namely mCGCNN. This deep learning model successfully integrates the crystal structure characteristics and the physical/chemical properties of materials, solving the data fusion problem of the current universal models in materials and significantly enhancing the efficiency and accuracy of gravimetric capacity prediction for lithium-ion batteries. In addition, the scale factors alpha and beta are introduced to control the contribution of crystal structure and numerical data of materials to the model, which increases the flexibility and adjustability of the model. A large dataset is extracted from the Materials Project database and analyzed using various ML algorithms, including traditional ML models and deep learning models such as CGCNN and mCGCNN. In comparison to other models, mCGCNN achieves superior performance with MAE of 6.6 mAhg1 and R2 value of 0.965 in test set. This outperforms the original CGCNN model (MAE: 42.5 mAhg � 1, R2: 0.328), XGBoost model (MAE: 23.6 mAhg � 1, R2: 0.842), and Random Forest model (MAE: 21.9 mAhg � 1, R2: 0.844). Furthermore, the mCGCNN model is also applied to a classification task using the same dataset, achieving an AUC of 0.99 and a total accuracy of 95.5 %. The mCGCNN model not only provides a valuable tool for rapid and accurate screening of high-performance batteries using multi-source material data, but also has significant potential for the discovery of other functional materials, making it widely applicable.

4.2 at% Cr addition leads to concurrent strength–ductility improvement of HfMoTaTiZr RHEA by 207.5 MPa and 20.6%.

Abstract. Large language models (LLMs) have achieved remarkable performance in general domains, they still face significant challenges when applied to specialized problems in fields like materials science. In this study, we enhance the performance of LLMs in the specific field of metal-organic frameworks (MOFs) for hydrogen storage by employing a post-pretraining approach to customize the LLM with domain-specific learning. By incorporating a comprehensive dataset comprising more than 2,000 MOF structures, over 7,000 related scientific papers, and a corpus exceeding 210 million tokens of specialized materials and chemical knowledge, we developed a domain-specific LLM for MOFs, referred to as MOFs-LLM. Through supervised fine-tuning, we unlocked the potential of MOFs-LLM in various tasks, including performance prediction, inverse design, mechanistic studies and application prospect analysis, with a specific focus on hydrogen storage material design challenges. In the practical application of reverse design, we utilize MOFs-LLM to mutate numerous ligands and select suitable building blocks, resulting in a structural space encompassing more than 100,000 MOFs. A MOF structure with highly promising hydrogen storage performance was ultimately successfully identified. This work effectively demonstrates the successful application of LLMs in a specific material science domain and provides a methodological pathway that can serve as a valuable reference for future research

Metal-organic frameworks (MOFs) are a new class of nanoporous materials that are widely used in various emerging fields due to their large specific surface area, high porosity and tunable pore size. Its excellent chemical tunability provides a wide material space, in which tens of thousands of MOFs have been synthesized. However, it is impossible to explore such a vast chemical space through trial-and-error methods, making it difficult to achieve custom design of high-performance MOFs for specific applications. Machine learning (ML) is a powerful tool for guiding materials design and preparation by mining the hidden knowledge in data, and can even make prediction of material properties in seconds. This review aims to provide readers with a new perspective on how ML has been changing the research and development paradigm of MOFs. The four main data sources for MOFs and how to select the suitable features (descriptors) are firstly presented to enable the reader to quickly acquire data and carry out machine learning. Moreover, the application of ML in the development of MOFs is highlighted from the perspectives of performance prediction, rational design and intelligent synthesis. Finally, the future challenges and opportunities of combining ML with MOFs from the points of view of data and algorithms are proposed. This review will provide instructive guidance for ML-assisted MOFs research.

The Al–Cr–Mo–Ni system is of technical interest because it is an essential system for the thermodynamic modeling of systems related to the Ni- and NiAl-based superalloys. The knowledge of phase behaviors and thermodynamic properties of this system will be greatly helpful for the development of related alloys. Thermodynamic modeling of the Al–Cr–Mo–Ni system in the previous effort is not satisfactory. In this study, the Cr–Mo–Ni system was re-optimized with more sophisticated binary databases, and a new thermodynamic database of the Al–Cr–Mo–Ni system was established. A satisfactory agreement between calculated results and experimental data was obtained. The thermodynamic database developed in this study is suitable for assisting the design of both Ni- and NiAl-based superalloys.

采用水热法合成纳米钛酸锶(Strontium titanate,SrTiO3)粉体,通过改变乙二醇(Ethylene glycol,EG)的量来控制SrTiO3 的形貌,使用扫描电镜和X射线衍射对样品进行形貌结构分析和物相分析.通过光催化降解亚甲基蓝(Methylene blue,MB)实验,对比研究了煅烧前后、EG加入量对 SrTiO3 粉体光催化性能的影响.结果表明,煅烧后的样品结晶度增大,其光催化效率有明显提升;EG的加入量越少,样品结晶度越高,光催化性能也更好.

为了摸清贵州省某高品位钙质磷矿矿石性质,为后续生产工艺提供理论指导,采用偏光显微镜、X射线衍射仪、扫描电镜等分析手段对该矿石的化学组成、矿物组成、各组分赋存状态、主要矿物的嵌布特征、嵌布粒度特征和单体解离度等进行了工艺矿物学研究.研究结果表明:该工业矿物为胶磷矿,脉石矿物主要为碳酸盐矿物、石英、玉髓、长石-黏土类矿物,以及少量铁碳质矿物.胶磷矿与碳酸盐矿物的嵌布粒度较大,较易解离,在磨矿细度为-0.074 mm占 60%时,胶磷矿与碳酸盐矿物单体解离大于 85%.石英-长石-黏土类矿物和铁碳质矿物的嵌布粒度较小,并且部分以浸染状嵌布于胶磷矿内部,解离分选难度较大,需要在磨矿细度足够细(<0.04 mm)时,该部分脉石才可解离出,此时精矿P2O5 的理论品位可达到36.25%.

Membrane fouling is a critical challenge for current ceramic membranes, which suffer from low flux and insufficient removal. Development of self-cleaning catalytic ceramic membranes is promising to address this challenge. Herein, we design heterogeneous silicon carbide ceramic membranes featuring a novel structure of g-C3N4-decorated beta-SiC nano-wire catalytic functional layer, which enables enhanced anti-fouling self-cleaning performance. At chemical harsh (alkaline or especially acidic) conditions, the nano-wire membrane exhibits catalysis-enhanced removal performance for organic contaminants. Unlike conventional particle-packing membrane structure, such a nano-wire network membrane structure has not only high porosity (56.1%), but exceptional water permeance (110 L.m(-2).h(-1).bar(-1)) and removal (100%) of organic substance under simulated sunlight, outperforming state-of-the-art organic membranes and ceramic membranes. Superoxide radical (center dot O-2(-)) was experimentally confirmed to be major reactive species responsible for self-cleaning function. We also propose a catalytic mechanism model with radical formation pathway, enabled by the as-formed g-C3N4@beta-SiC heterojunction structure with reduced electron-hole recombination. This work would provide new insights into not only rational design of next-generation ceramic membranes with self-cleaning function but also more applications of efficient treatment of refractory wastewaters containing degradable organic substances by using such membranes.

根据Furnas模型理论,研究了两种粒径颗粒的级配对SiC陶瓷膜孔径大小及其纯水通量的影响.研究结果表明,细颗粒含量≤40％时,随着细颗粒含量的增加,粗细颗粒之间形成的烧结颈增多,粗颗粒与粗颗粒之间的间隙减小,使得粗颗粒堆积形成的陶瓷膜骨架更加密实,SiC陶瓷膜的孔径减小;经1900℃烧结后,当细粗颗粒含量比例为4:6时SiC陶瓷膜的孔径较小,为0.699μm,能有效分离乳化油油水.

高校作为人才培养的主要阵地,在大数据时代下,其教学模式面临着改革和创新,高校教师角色转换也成为一种趋势.在简述大数据时代特征以及传统高校教师角色的基础上,重新分析大数据时代下的高校教师的新角色,并总结了高校教师角色转换的途径.

Abstract The silicon carbide (SiC) powder is covered with polycarbosilane (PCS), N-Methyl pyrrolidone (NMP) is a solvent, polyethersulfone (PES) is a bonding aid, using phase-phase and solid-phase sintering process combined with a step-by-step molding to prepare a symmetrical structure of silicon carbide hollow fibers, and determine the membrane’s perforation and purification and purity of the water distribution and the circumference of the membrane. The results show that the SiC membrane tube is a finger-hole structure layer-spongy structure layer-finger-hole structure structure, the optimal sintering temperature is at 1200 °C, the size of the prepared ceramic membrane aperture is concentrated in about 0.9 μm, and the pure water flux is 2.832m3 / (m2⋅h).