Anyang Institute of Technology

Nickel, silver/graphene double-layer composite coatings were prepared by pulsed electrodeposition. The composition, structure, mechanics and tribological properties of the coatings were investigated. Research shows that the Ag grains in the composite coatings decreased with the increase of graphene content. When the amount of graphene added to the bath reached 0.8 g/L, a large number of cracks were generated in the composite coating. When the amount of graphene added to the bath is 0.4 g/L, the microhardness of the composite coating is about 144 HV, much higher than that of the Ag coating (about 89 HV). Compared with Ag coating, the addition of graphene reduces the friction coefficient and wear rate of the coating. There are a lot of furrows on the Ag coating surface, and after the addition of graphene, the multilayer graphene makes the surface of the abrasion smooth, effectively reducing the friction coefficient of the coating, and the friction coefficient is more stable during the friction process.

Diabetic wounds are a major clinical challenge, driven by hyperglycemia, oxidative stress, persistent inflammation, and bacterial infection. Conventional dressings offer limited benefit, creating demand for advanced therapeutic strategies. This review analyzes hydrogel-based wound dressings and flexible electronic devices. Hydrogels are categorized by angiogenesis promotion, antioxidant activity, anti-inflammatory regulation, antibacterial action, and electrical conductivity. Flexible electronics are examined for adaptability, sensitivity, and real-time monitoring potential. Hydrogels maintain moist environments, support tissue regeneration, and deliver multifunctional bioactivity. Growth factor-loaded and electroactive hydrogels promote angiogenesis. Reactive oxygen species (ROS)-responsive systems restore redox balance. Anti-inflammatory and antibacterial hydrogels regulate macrophages and reduce infection risk. Conductive hydrogels accelerate healing through electrical stimulation. Flexible electronics provide continuous monitoring, intelligent feedback, and remote management, enhancing treatment precision. Their integration with hydrogels represents a paradigm shift from passive dressings to active diagnostic and therapeutic systems. Challenges remain in material design, interfacial stability, and long-term biocompatibility. These issues guide future innovation and clinical translation, offering a foundation for smart diabetic wound management.

AI literacy is increasingly important in college students' academic achievement, daily life, and future employability. However, current research predominantly overlooks the heterogeneity in students' AI literacy, especially how individual psychological characteristics and features of AI technology contribute to this variation. This oversight limits the formulation of tailored strategies to meet the students' various demands in an era shaped by rapid AI advancement. This study aims to adopt an individual-centered approach to identify distinct AI literacy profiles among college students. In addition, it investigates, based on affordance theory, how positive emotions, instrumental motivation, perceived ease of use, and psychological anthropomorphism predict assignment to different profiles. A total of 808 Chinese college students participated in this survey. Latent profile analysis (LPA) was employed to classify students into distinct AI literacy profiles. Multinomial logistic regression was conducted to examine how psychological and technological factors predict profile classification. This study identified four distinct AI literacy profiles among college students: preliminary contact type, ethical orientation type, balanced development type, and behavioral conservatism type. These profiles showed significant differences in positive emotions, instrumental motivation, perceived ease of use, and psychological anthropomorphism, highlighting diverse psychological and technological characteristics inherent to each group. This study underscores the heterogeneity of AI literacy within the college student population and detects four distinct AI literacy profiles with unique psychological and technological traits. The findings indicate that students' AI literacy is profoundly affected by emotional tendencies, motivational drives, and technological variables, highlighting the need for tailored educational strategies that address the distinct psychological and technological drivers of each literacy profile.