Matrix glass of Li₂O-Al₂O₃-SiO₂ (LAS) glass ceramics without zirconia was prepared by using melting method with Li₂CO₃, Al₂O₃, SiO₂ as the main materials. Then, ZrO₂/LAS glass-ceramic composites with different contents of ZrO₂ were fabricated by using high-energy ball milling, grinding and sintering. Thermal behaviors of the raw materials were examined, while the effects of ZrO₂ and sintering temperature on phase composition, macro/microscopic morphology, thermal expansion, bulk density and hardness of the composites were systematically studied. It is found that phase composition of the ZrO₂/LAS glass-ceramics composites is mainly dependent on sintering temperature, while the addition of ZrO₂ has a strong influence on denseness, thermal expansion and mechanical properties of the composites. The composites with high denseness were achieved as the sintering temperature is 1100 ℃ and the content of ZrO₂ is 30 wt.%, while the low linear shrinkage (3.55×10^-5) was observed at 800 ℃. When the ZrO₂ content was 10–30 wt.%, the average CTEs of the composites at 30–300 ℃were negative and close to zero, which showed the good thermal stability of the composites. The Vickers hardness of the composites increased linearly with the increase of ZrO₂ content, indicating that the addition of ZrO₂ is beneficial to improve the mechanical properties of the composites. The introduction of ZrO₂ into LAS glass could be used as an effective reference for the preparation of glass-ceramic composites with low thermal expansion coefficient and high mechanical properties.

Aiming at the problem of ceramic surface defect detection, deep learning algorithm is one of the hot spots in recent research. By establishing suitable data sets, selecting appropriate network models and algorithms, automatic detection and classification of ceramic surface defects can be realized. Commonly used deep learning surface defect detection algorithms include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Multilayer Perceptron (MLP), etc. Among them, the ceramic defect detection method based on YOLOv5 algorithm is a relatively advanced method in recent years, which has high detection accuracy and real-time performance, can accurately detect and identify various defects on the surface of ceramics and can further improve the performance of the algorithm by optimizing the network structure and loss function. The ceramic defect detection method based on CSS algorithm is to use the image segmentation method to segment ceramic defect samples and perform binary processing on the segmented sample set images to highlight the position and size of the defects. This paper was aimed to review the research progress in deep learning for surface defect detection of ceramics,introduce ceramic defect detection methods based on deep learning algorithms and summarize the process of ceramic surface defect detection algorithms based on YOLOv5 and CSS.

The traditional von Neumann computing architecture has relatively-low information processing speed and high power consumption, making it difficult to meet the computing needs of artificial intelligence (AI). Neuromorphic computing systems, with massively parallel computing capability and low power consumption, have been considered as an ideal option for data storage and AI computing in the future. Memristor, as the fourth basic electronic component besides resistance, capacitance and inductance, is one of the most competitive candidates for neuromorphic computing systems benefiting from the simple structure, continuously adjustable conductivity state, ultra-low power consumption, high switching speed and compatibility with existing CMOS technology. The memristors with applying MXene-based hybrids have attracted significant attention in recent years. Here, we introduce the latest progress in the synthesis of MXene-based hybrids and summarize their potential applications in memristor devices and neuromorphological intelligence. We explore the development trend of memristors constructed by combining MXenes with other functional materials and emphatically discuss the potential mechanism of MXenes-based memristor devices. Finally, the future prospects and directions of MXene-based memristors are briefly described.

MXenes have received extensive attention from scholars due to their unique layered structure, significant electrical conductivity, and excellent mechanical properties. In addition to their pristine forms, they could also be incorporated with other components for attaining hybrids and nanocomposites, accompanying with amplified functionalities. It has been widely used in lithium batteries, supercapacitors, electromagnetic shielding, tumor therapy, biosensors, photocatalysis, and other fields, and has shown great application potential in energy conversion and storage. The purpose of this article is to encyclopaedically overview the latest progress in synthesis and characterization of MXenes, while their potential applications in energy conversation such as water splitting and solar cells, as well as in energy storage such as Li-ion batteries, supercapacitors, and hydrogen energy will be comprehensively elaborated. Development opportunities and challenges are summarized.