A molten salt precipitation method in conjunction with low energy ball milling is used to produce the hybrid Co₃O₄–Fe₂O₃/C anode with attractive electrochemical performance in K-ion and Na-ion batteries.

Graphite, with appealing features such as good stability, high electrical conductivity, and natural abundance, is still the main commercial anode material for lithium-ion batteries. The charge-discharge rate capability of graphite anodes is not significant for the development of mobile devices and electric vehicles. Therefore, the feasibility investigation of the rate capability enhancement of graphite by manipulating the structure is worthwhile and of interest. In this study, an effective ball-milling process has been set up by which graphite nanostructures with a high surface area are produced. An in-depth investigation into the effect of ball milling on graphite structure as well as electrochemical performance, particularly rate capability, is conducted. Here, we report that graphite nanoflakes with 350 m² g⁻¹ surface area deliver retained capacity of ~75 mAh g⁻¹ at 10 C (1 C = 372 mA g⁻¹). Finally, the Li⁺ surface-storage mechanism is recognised by associating the structural characteristics with electrochemical properties.

Austenite grain growth kinetics in the presence of secondary precipitates of a microalloyed steel (30MSV6) was studied employing quantitative metallographic techniques. Austenitizing experiments were carried out at 1,000, 1,100 and 1,200 °C. According to the experimental data, abnormal grain growth behaviour is observed at 1,100 °C while it is normal at 1,000 and 1,200 °C. TEM observation represents multicomponent carbonitride precipitate, (Ti,V)(C,N), in the as-received steel with a mean radius of 35 nm. A mathematical model is proposed considering austenite grain growth along with dissolution and coarsening kinetics of the multicomponent precipitates. The austenite grain growth model for short-term non-isothermal and subsequent long-term isothermal heating stages was developed using a statistical approach. The model includes an algorithm to estimate the size distribution of austenite grains. Precipitate mean field dissolution and Lifshitz–Slyozov–Wagner coarsening models were integrated in the proposed model to calculate the pinning pressure retarding the grain boundary movement. The mean austenite grain size and the grain size distribution (normal and abnormal) calculated by the model are in good agreement with experimental data.