Constructing neural-like network channels from the surface to interface for enhanced high-rate cycling stability in Co-free Li-rich cathode

Co-free Li-rich Mn-based cathode materials (LMNO) have gradually become powerful competitors with ultra-high specific discharge capacity and energy density. However, high-rate performance and severe voltage decay restrict the commercial application of LMNO. Herein, LiAl5O8 acts as a templating agent to construct 3D neural-like networks in LMNO, enabling fast ion diffusion and improving rate performance. Proton exchange is predominantly facilitated by the process of LiAl5O8 constructed to generate vacancies for oxygen preservation, while strong Al-O bonds stabilize interfacial lattice oxygen, effectively suppressing voltage decay due to structural evolution. As a result, the designed cathode exhibits a discharge specific capacity of 154.65 mAh g-1 at 5 C and 91.68% capacity retention after 400 cycles (vs . 66.67% of LMNO), effectively suppressing voltage decay with 90.90% voltage retention (vs . 81.08% of LMNO). The constructed neural-like network structure engineering provides an innovative direction for improving the high-rate performance and structural stability of LMNO.