Grain boundary engineering (GBE) is considered to be an attractive approach to microstructure control, which significantly enhances the grain-boundary-related properties of face-centered cubic (FCC) metals. During the twinning-related GBE, the microstructures are characterized as abundant special twin boundaries that sufficiently disrupt the connectivity of the random boundary network. However, controlling the grain boundary character distribution (GBCD) is an extremely difficult issue, as it strongly depends on diverse processing parameters. This article provides a comprehensive review of controlling GBCD during the twinning-related GBE of FCC materials. To commence, this review elaborates on the theory of twinning-related GBE, the microscopic mechanisms used in the optimization of GBCD, and the optimization objectives of GBCD. Aiming to achieve control over the GBCD, the influence of the initial microstructure, thermo-mechanical processing (TMP) routes, and thermal deformation parameters on the twinning-related microstructures and associated evolution mechanisms are discussed thoroughly. Especially, the development of twinning-related kinetics models for predicting the evolution of twin density is highlighted. Furthermore, this review addresses the applications of twinning-related GBE in enhancing the mechanical properties and corrosion resistance of FCC materials. Finally, future prospects in terms of controlling the GBCD during twinning-related GBE are proposed. This study will contribute to optimizing the GBCD and designing GBE routes for better grain-boundary-related properties in terms of FCC materials.

Refined microstructures achieved by cyclic heat treatment significantly contribute to improving the wear resistance of steels. To acquire the refined microstructures of 65Mn low-alloy steel, first, the specimens were solid solution-treated; then, they were subjected to cyclic heat treatment at cyclic quenching temperatures of 790–870 °C and quenching times of 1–4 with a fixed holding time of 5 min. The mechanical properties of 65Mn low-alloy steel in terms of hardness, tensile strength, elongation and wear resistance were characterized. Afterwards, the effect of cyclic heat treatment on microstructure evolution and the relationships between grain refinement and mechanical properties’ improvement were discussed. The results show that the average grain size firstly decreased and then increased with the increase in the quenching temperature. Hardness increased with grain refinement when the temperature was lower than 830 °C. Once the temperature exceeded 830 °C, hardness increased with the temperature increase owing to the enrichment of carbon content in the martensite. With the increase in cyclic quenching times, hardness continuously increased with grain refinement strengthening. In addition, both tensile strength and elongation could be significantly improved through grain refinement. The relationships among wear loss, hardness and average grain size showed that wear resistance was affected by the synthesis reaction of grain refinement and hardness. Higher hardness and refined grain size contributed to improving the wear resistance of 65Mn low-alloy steel.

In order to characterize the flow behaviors of SAE 5137H steel, isothermal compression tests at the temperatures of 1123 K, 1213 K, 1303 K, 1393 K, and 1483 K, and the strain rates of 0.01 s⁻¹, 0.1 s⁻¹, 1 s⁻¹, and 10 s⁻¹ were performed using a Gleeble 3500 thermo-mechanical simulator. The analysis results of true stress-strain curves show that the flow stress decreases with temperature increasing and strain rate decreasing. In order to accurately and efficiently characterize the complex flow behaviors, the intelligent learning method backpropagation–artificial neural network (BP-ANN) was combined with the particle swarm optimization (PSO), namely, the PSO-BP integrated model. Detailed comparisons of the semi-physical model with improved Arrhenius-Type, BP-ANN, and PSO-BP integrated model for the flow behaviors of SAE 5137H steel in terms of generative ability, predictive ability, and modeling efficiency were presented. The comparison results show that the PSO-BP integrated model has the best comprehensive ability, BP-ANN is the second, and semi-physical model with improved Arrhenius-Type is the lowest. It indicates that the PSO-BP integrated model can accurately describe the flow behaviors of SAE 5137H steel.

We present a method based on a microfluidic chip that produces continuous jetting of alginate microfiber in the atmosphere to facilitate its collection and assembly. Through the analysis of the factors influencing the microfiber jetting, the principle and some microfluidic chip design criteria are discussed. A special nozzle is designed near the chip outlet, and deionized water is introduced into the microchannel through the nozzle to increase the flux and thus to prevent drop formation around the outlet which impedes the continuous jetting of microfiber. The experiments have reported the effectiveness of the proposed structure and shown that the introduction of sheath flow promotes the stability of the flow field in the microchannel and does not affect the morphology of microfiber. Simulations of velocity and pressure distribution in the microchannel are also conducted. Further, the jetting microfibers are collected and assembled into various 3D complex fiber-based macroscopic structures through patterning or reeling. Since the proposed structure is rather simple and can be easily integrated into other complex structures without adding more soft-lithographical steps, microfibers with various morphology and function can be synthesized and collected in a single chip, which can be applied to various fields, such as tissue engineering, biotechnology, and drug discovery.

As a low-carbon and efficient energy source, nuclear power plays an indispensable role in China’s pursuit of carbon neutrality. However, existing studies on China’s nuclear energy development often overlook the constraints posed by uranium resources, limiting a comprehensive assessment of the pathways to carbon neutrality. This study incorporates uranium resource constraints into the China Global Energy Model (C-GEM) to analyze in detail the impact of uranium scarcity on China’s nuclear power development, electricity costs, and carbon emissions. Under a scenario of severe uranium resource constraints, the results indicate that nuclear power generation in 2060 is projected to be 35–50% lower than in a high-resource scenario, with nuclear power costs increasing by over 12% and carbon prices rising by approximately 2%. Without robust management of the uranium supply, the development of nuclear power may be constrained, hindering China’s ability to achieve its carbon neutrality targets. Therefore, this study suggests that China’s nuclear energy policies should focus on strengthening uranium resource security through enhanced domestic and international exploration, investing in advanced fuel recycling and high-efficiency reactor technologies, and integrating these measures into the broader low-carbon energy framework to ensure the sustainable development of nuclear power.