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

Abstract

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.

Keywords

Neural-like networks / LiAl₅O₈ / strong Al-O bonds / rate performance / Co-free Li-rich Mn-based cathode

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