Porous carbons with their abundant availability and high electrical conductivity present significant potential as cathode materials for Na-S batteries. In this study, we report the synthesis of nitrogen and phosphorus co-doped porous carbon (P-N co-doped PC) through a one-step carbonization process utilizing ammonium polyphosphate as the source of nitrogen and phosphorus. The average reversible capacities for P-N co-doped PC were 920, 861, 823 and 756 mA h g-1 at current densities of 0.2, 0.5, 1.0 and 2.0 C, respectively. Notably, upon returning to current densities of 1.0, 0.5 and 0.2 C, the capacities were restored to 805, 853 and 906 mA h g-1, highlighting the exceptional stability of the P-N co-doped PC. The pronounced capacitive storage mechanism of P-N co-doped PC can be attributed to the presence of numerous surface defects and active sites resulting from the co-doping of nitrogen and phosphorus. In particular, the remarkable cycling stability exhibited by the P-N co-doped PC can be ascribed to the exceptional stability of the surface layer that has undergone phosphorus doping, thereby facilitating the migration of ions. This research contributes valuable insights into the development of advanced Na-S batteries through the utilization of heteroatom-doped functionalized porous carbons.
Efficient heat dissipation is crucial for various industrial and technological applications, ensuring system reliability and performance. Advanced thermal management systems rely on materials with superior thermal conductivity and stability for effective heat transfer. This study investigates the thermal conductivity, viscosity, and stability of hybrid Al2O3-CuO nanoparticles dispersed in Therminol 55, a medium-temperature heat transfer fluid. The nanofluid formulations were prepared with CuO-Al2O3 mass ratios of 10:90, 20:80, and 30:70 and tested at nanoparticle concentrations ranging from 0.1 wt% to 1.0 wt%. Experimental results indicate that the hybrid nanofluids exhibit enhanced thermal conductivity, with a maximum improvement of 32.82% at 1.0 wt% concentration, compared to the base fluid. However, viscosity increases with nanoparticle loading, requiring careful optimization for practical applications. To further analyze and predict thermal conductivity, a Type-2 Fuzzy Neural Network (T2FNN) was employed, demonstrating a correlation coefficient of 96.892%, ensuring high predictive accuracy. The integration of machine learning enables efficient modeling of complex thermal behavior, reducing experimental costs and facilitating optimization. These findings provide insights into the potential application of hybrid nanofluids in solar thermal systems, heat exchangers, and industrial cooling applications.