Long afterglow materials are widely used, but their luminescence mechanism is difficult to be universally explained. For Sr₂MgSi₂O₇:Eu²⁺, Dy³⁺ silicate long afterglow material with good luminescence-afterglow properties, the molecular models of Sr₂MgSi₂O₇ substrate, Eu doped and (Eu,Dy) co-doped Sr₂MgSi₂O₇ were constructed, and the first-principles calculations were carried out. From the perspective of electronic structure, the electron transition trapping path is revealed to explain the persistent luminescence mechanism of Sr₂MgSi₂O₇:Eu²⁺, Dy³⁺. The results showed that the doping of Eu and Dy ions changed Sr₂MgSi₂O₇ from an indirect band gap semiconductor to a direct band gap semiconductor. The Dy 5d state was mainly located between the Fermi level and the Eu 5d state, and it had energy overlap with the Eu 5d state, which confirmed the rationality of Dy³⁺ as an electron trap. The luminescence process of Sr₂MgSi₂O₇:Eu²⁺, Dy³⁺ was revealed, which is helpful for the subsequent regulation and improvement of optical properties.

With bauxite tailings as the main raw material, adding bauxite clinker and lithium chinaite to prepare the traditional mulite-corundum multiphase ceramics, adding different contents of lepidolite, to explore the influence of the properties of mulite-corundum multiphase ceramics. The phase composition and morphology of ceramics were analyzed by X-ray diffractometer and scanning electron microscope. The effects of lepidolite content and sintering temperature on the mechanical properties of ceramics were studied. The results show that: The addition of lepidolite can reduce the sintering temperature and improve the strength of ceramics. When 10% lepidolite is added and the sintering temperature is 950 ℃, the strength of mulite-corundum multiphase ceramics is the highest, and the ceramics phase composition is corundum, mullite, quartz, hematite, rutile and glass, and the volume density is 1.75 g/cm³, the thermal conductivity is 0.447 W/(m·K), the linear shrinkage is 5.47%, and the compressive strength is 74.87 MPa. It has a wide application prospect in the fields of thermal storage ceramics and refractory materials.

YAG crystal is a typical hard and brittle material with Mohs hardness of 8.5 and insoluble in any acid or base at room temperature, so it is difficult to process. A step-by-step lapping process is proposed in order to explore lapping processing of YAG crystals in this paper. Based on the method of free abrasive lapping, the particle size of boron carbide (B₄C) abrasive was reduced step by step during the lapping process, and abrasive W40, abrasive W28, abrasive W14 and abrasive W7 abrasives were selected for lapping in steps, and the particle size ranges of the four abrasive abrasives were: 40-28 μm, 28-20 μm, 14-10 μm, and 7-5 μm in order. The effect of the lapping pressure, the rotational speed of the lapping disc and pendulum, and the B₄C mass fraction in the lapping solution on the lapping effect in each lapping step was investigated, and the optimal lapping parameters were derived; the depth of subsurface damage after lapping of YAG crystals was measured by cross-section microscopy method to determine the amount of subsequent polishing removal, and the relationship between the depth of subsurface damage h_SSD and the surface roughness R_a after lapping was investigated. The study showed that the best lapping results were obtained for each lapping step when the lapping pressure was 44.54 kPa, the rotational speed of the lapping disc and pendulum was 60 r/min, and the B₄C mass fraction in the lapping solution was 15%. The material removal rates of abrasive W40, abrasive W28, abrasive W14 and abrasive W7 lapping were 83.12, 57.32, 27.54 and 9.53 μm/min, respectively, and the surface roughness after lapping was 0.763, 0.489, 0.264, and 0.142 μm, respectively. The subsurface damage of the step-by-step lapping was 3.041 μm, which were measured by cross-section microscopy method, and needed to be removed by the subsequent polishing; The relationship between subsurface damage depth and surface roughness of YAG crystals after lapping with this lapping parameter is h_SSD=41.46×R_a^4/3. The study can provide guidance for the actual processing and production of YAG crystal components.

The structure of melt is very important for the growth of high-quality large-size functional oxides, which are such materials widely used in military, high-tech manufacturing, and medical treatment. The structures for three typical functional oxide melts, such as oxide alumina, yttrium aluminum garnet, and lithium niobate have been reviewed in this paper. Several coordination units formed by cations and anions exist in each melt, and the coordination number, bond length, spatial structure and the percentage of various coordination units in the melts are obviously different from those in the corresponding crystals. The properties of these structure units are dependent on temperature. The coordination units connected with each other by the nodes. Both the cations and anions in the melts can be acted as nodes to connect the coordination units (polyhedron) in the melts. Phase transition is the kinetic process of melt structure transformation. Therefore, in addition to the melt structure, revealing the melt structure evolution kinetic process will help to understand the growth mechanism of large-sized crystals from a deep level.

Fullerenes and metallofullerenes have unique structure and novel electronic characteristics, which have great application potential in biomedicine, quantum and information fields. However, how to increase the yields of metallofullerenes is a key technical problem that must be solved for practical application. In order to synthesize metallofullerenes with high yields and selectivity, it is necessary to understand the formation mechanism and develop new synthesis methods. Our research focuses on the comprehensive analysis of the formation mechanism of fullerenes and metallofullerenes in order to find a way to break through the bottleneck of their productivity. On the one hand, the formation process of fullerenes was simulated by density functional theory and molecular dynamics, which directed the optimization of the synthesis conditions of fullerenes. On the other hand, a series of metallofullerenes with specific structures and functions were prepared by accurately controlling the inert gas pressure, arc gas composition and raw material composition of metallofullerenes, and the efficient preparation strategy of metallofullerenes was developed. Finally, we also explored the protection methods after the formation of metallofullerenes, and achieved certain results, laying a solid foundation for the industrialization of metallofullerenes in the future.

Most data in material science are multi-fidelity data. From the viewpoint of data producer, there is a system error for any quantum method. For machine learning algorithm, as a data consumer, various methods have been designed to maximize the number of knowledges extracted from the multi-fidelity data. In this paper, a quantitative method of noise addition was used to evaluate the influence of different noise types and intensities on some multi-fidelity data learning methods. And the effective scope of the data correction method was verified via iterative noise reduction. The results show that the ways to exploit the multi-fidelity data are crucial. It is necessary to consider comprehensively both the size and the noise level of the datasets. On a variety of datasets constructed with different noise types and intensities, the "Onion" training method that gradually deletes lower fidelity data is better than the "one by one" training method in the direction of noise reduction due to the synergistic effect of different multi-fidelity data. No matter what kind of noise intensity and training method, linear noise has less impact on the final performance of model. However, the data with sampled noise added, which the final testing results are similar to the real multi-fidelity data, were recommended to be adopted in a future research. Also, the complex noise in data is difficult to be corrected by a small amount of true data, thus being more suitable for the iterative noise reduction processing.

Approximately 80% of chemical products used in daily life involve heterogeneous catalytic reactions. How to shorten the catalyst development cycle and efficiently screen catalysts with the superior performance has become a challenge. Among various types of catalysts, two-dimensional materials have attracted recent attention due to their unique structure and electronic properties. It is thus necessary for two-dimensional materials with some specific reaction requirements to clarify the relationship between catalyst structure and function. Descriptors can correlate the structural properties, electronic properties and performance of catalysts, and the structure-activity relationship of catalysts can be revealed through descriptors. The principle of each descriptors association with catalyst performance and its mechanism were introduced. Structural descriptors have a better performance in predicting the activity of two-dimensional carbon materials. Compared with the structural descriptors, electronic descriptors can match the parameters of catalytic performance. It is possible to develop the binary descriptors that combine the merits of both structural and electronic descriptors. In the era of big data, the development of descriptors is gradually integrated with emerging methods such as machine learning, and some performance prediction models for multi-dimensional parameters need to be developed. With the progress of artificial intelligence technology, big data-driven performance descriptors will facilitate the development of 2D material catalysts.

The model of developing luminescent materials has transferred from the conventional "trial and error" to "experience-guided experiments", and further to a novel paradigm of "theoretical prediction and experimental verification". Efficient theoretical prediction and rapid experimental verification are a key to this transformation. The theoretical prediction methods such as high-throughput calculations and machine learning are becoming more and more mature, and the corresponding experimental methods such as single-particle diagnosis are more efficient, laying a theoretical and experimental foundation for the development of novel luminescent materials. This review briefly summarizes recent research progresses on discovering new rare earth-activated luminescent materials based on single-particle diagnosis and high-throughput computation approaches.

Glass as a material has existed in China for a long time, but the related studies on ancient glass in China started relatively late due to the long-term confusion of name and texture, leading to a lack of research on the weathering and composition of ancient silicate glass. Some previous studies on ancient glass mainly discussed the artistic character and development laws of glass with respect to cultural exchange and chemical analysis from the perspective of dynastic succession. A few work established the related mathematical model and used the intelligent algorithm for qualitative quantification of weathering silicate glass original composition prediction and subclassification method. This paper was to use multiple groups of weathered and unweathered silicate glasses and collect/extract the data on their chemical composition content, ornamentation and color. The relations among the patterns, color, types of glass and surface weathering were analyzed by the Spearman coefficient. The decision tree for a rough classification and neural network to predict the main chemical composition of glass before its weathering was given, and the classification basis of silicate glass was summarized. Besides, the subcategorization at the optimal quantity of categories to conduct subclass classification was established, and a reasonable amount of barium glass and high potassium glass was selected. The results show that the type of glass has an influence on the surface weathering, and there are silicon dioxide, aluminum oxide, lead oxide, barium oxide and phosphorus pentoxide involved in the weathering process. Moreover, the amount of silicon dioxide decreases and lead oxide increases sharply in lead barium glass, while vice versa in high potassium glass after weathering.