Xuesong Leng

The corrosion behavior of 316L stainless steel joints brazed with three commercial nickel-based filler metals (Ni-7Cr-2.8B-4.5Si, Ni-14.3Cr-3.5B, and Ni-18.5Cr-9.8Si) was comparatively investigated. The B-containing joints form dispersed CrB-type borides, while the Si-only joint develops continuous band-like Ni₃Si intermetallics along the seam center. Electrochemical measurements show that the Ni-Cr-B joint exhibits the highest polarization resistance, whereas the Ni-Cr-Si joint shows the most compact passive film. However, cross-sectional corrosion morphology reveals that the Ni-Cr-Si joint suffers the deepest localized attack due to the continuous Ni₃Si phase acting as preferential corrosion channels. In contrast, the Ni-Cr-B joint exhibits shallow and uniform corrosion with minimal strength degradation (0.45% loss), while the Ni-Cr-B-Si joint shows the most severe reduction (47% loss) due to preferential attack along boride boundaries, which is facilitated by pre-existing solidification pores in the non-isothermal solidification zone. These findings demonstrate that corrosion resistance is governed by the distribution and continuity of second phases, providing guidance for filler metal selection in corrosive environments.

Superconducting materials, exhibiting zero resistance and perfect diamagnetism, play a crucial role in electromagnetic applications. The critical transition temperature (T_c) is a key parameter in determining the practical utility of superconducting materials. However, experimental observations and theoretical calculations face challenges such as lengthy trial-and-error cycles and the lack of unified theoretical frameworks. The data-driven scientific paradigm has established machine learning (ML) as a powerful tool in superconducting materials research, enabling data correlation analysis, candidate identification, and T_c prediction. This review provides an overview of recent advancements in applying ML to superconducting materials research, focusing on T_c prediction, exploring potential superconducting candidates, and applying advanced algorithms. Additionally, it highlights experimental and theoretical validations of machine learning outcomes. The features of superconducting materials and their data representation methods are introduced, encompassing both experimental and computationally generated data sets. The review further summarizes inverse design strategies driven by machine learning, demonstrating their potential for discovering new high-T_c superconductors. Finally, challenges such as data scarcity, limited model generalizability, and insufficient cross-scale prediction capabilities are discussed, alongside proposed future directions to advance machine learning-driven superconducting materials research.

Chromium Nitride (CrN) coatings have widespread utilization across numerous industrial applications, primarily attributed to their excellent properties. Among the different methods for CrN coating synthesis, direct current magnetron sputtering (DCMS) has been the dominant technique applied. Nonetheless, with the expanded applications of CrN coatings, the need for enhanced mechanical performance is concurrently escalating. High-power impulse magnetron sputtering (HiPIMS), an innovative coating deposition approach developed over the past three decades, is gaining recognition for its capability of yielding coatings with superior mechanical attributes, thereby drawing significant research interest. Considering that the mechanical performance of a coating is fundamentally governed by its microstructural properties, a comprehensive review of CrN coatings fabricated through both techniques is presented. This review of recent literature aims to embark on an insightful comparison between DCMS and HiPIMS, followed by an examination of the microstructure of CrN coatings fabricated via both techniques. Furthermore, the exploration of the underlying factors contributing to the disparities in mechanical properties observed in CrN coatings is revealed. An assessment of the advantages and potential shortcomings of HiPIMS is discussed, offering insight into CrN coating fabrication.

Copper-based diamond composites have been the focus of many investigations for higher thermal conductivity applications. However, the natural non-wetting behavior between diamond particles and copper matrix makes it difficult to fabricate copper-based diamond composites with high thermal conductivity. Thus, to promote wettability between copper and diamond particles, the copper/diamond interface must be modified by coating alloying elements on the diamond surface or by adding active alloying elements with carbon in the copper matrix. In this paper, we review the research progress on copper-based diamond composites, including theoretical models for calculating the thermal conductivity and the effect of process parameters on the thermal conductivity of copper-based diamond composites. The factors that affect interfacial thermal conductivity are emphatically analyzed in this review. Finally, the current problems of copper-based diamond composites and future research trends are recommended.

Supercritical carbon dioxide (S-CO₂) has the advantages of amphoteric liquid and gas, which possesses many unique characteristics, such as good compressibility, high density, high solubility, good fluidity and low viscosity. The Brayton cycle with S-CO₂ is considered to have many promising applications, especially for power conversion industries. However, the corrosion and degradation of structural materials hinder the development and application of the Brayton cycle with S-CO₂. Nickel-based alloys have the best corrosion resistance in S-CO₂ environments compared to austenitic stainless steels and ferritic/martensitic steels. Thus, the present article mainly reviews the corrosion behaviour of nickel-based alloys in S-CO₂ under high temperature and pressure. The effect of alloying elements and environment parameters on the corrosion behaviour of different nickel-based alloys are systematically summarized. The conclusion and outlook are given at the end.

Atomic oxygen in the low Earth orbit (LEO) environment is highly oxidizing. Due to the high flight speed of spacecraft, the relative kinetic energy of high-flux atomic oxygen bombarding the spacecraft surface can reach up to about 5 eV. Therefore, atomic oxygen is one of the most dangerous space environment factors in LEO, which seriously affects the safe operation and service life of spacecraft in orbit. In order to meet the requirements for the high reliability and long lifetime of spacecraft, effective protection measures must be taken on their sensitive surfaces. Siloxane is a coating with an organic–inorganic hybrid structure. Compared with SiO₂ and other inorganic atomic oxygen protective coatings, it has better flexibility and is better at anti-atomic oxygen performance. In this paper, the plasma polymerization deposition technique was used to prepare large-area siloxane coatings on different substrates with different thicknesses for improving atomic oxygen resistance by optimizing the process parameters. The thickness of the coating was measured by different methods, and the results showed that the thickness distribution was consistent. By observing the surface morphology of the coating, it was uniform and compact without obvious defects, so the uniformity of large-area coating was also ideal. The adhesion and heat/humidity resistance of siloxane coatings were examined by pull-off testing and damp-heat testing, respectively. The results showed that the siloxane coatings with a thickness of about 400 nm exhibited better physical properties. At the same time, the ground simulation testing of atomic oxygen confirmed that siloxane coatings with a thickness of 418 nm presented the best performance of atomic oxygen resistance. The atomic oxygen erosion yield of siloxane coatings with a thickness of 418 nm was as low as 5.39 × 10⁻²⁷ cm³/atoms, which was three orders of magnitude lower than that of the uncoated Kapton substrate and presented a good anti-atomic oxygen performance. Meanwhile, it has also successfully passed the damp-heat test. The coating thickness is only several hundred nanometers and does not increase the weight of the spacecraft, which makes it a relatively ideal LEO atomic oxygen protection material. Furthermore, a possible mechanism was proposed to explicate the physicochemical process of atomic oxygen attacking the coating materials.