Multifunctional Surface Construction for Long-Term Cycling Stability of Li-Rich Mn-Based Layered Oxide Cathode for Li-Ion Batteries

Li-rich Mn-based layered oxides (LMLOs) are promising cathode material candidate for the next-generation Li-ion batteries (LIBs) of high energy density. However, the fast capacity fading and voltage decay as well as low Coulombic efficiency caused by irreversible oxygen release and phase transition during the electrochemical process hinder their practical application. To solve these problems, in the present study, a multifunctional surface construction involving a coating layer, spinel-layered heterostructure, and rich-in oxygen vacancies is successfully conducted by a facile thermal reduction of the LMLO particles with potassium borohydride (KBH₄ ) as the reducing agent. The multifunctional surface structure plays synergistic effects on suppressing the interface side reaction, reducing the dissolution of transition metal, increasing electron conductivity and lithium diffusion rate. As a result, electrochemical performances of the LMLO cathode are effectively enhanced. With optimization of the addition of KBH₄ , the electrode delivers a reversible capacity of 280 mAh g^-1 at 0.1 C, which maintains after 100 cycles. The capacity retention with respect to the initial capacity is as high as 98% at 1 C after 400 cycles. The present work provides insights into designing a highly effective functional surface structure of LMLO cathode materials for high-performance LIBs.

An improved representation of regional boundaries on parcellated morphological surfaces

Establishing the correspondences of brain anatomy with function is important for understanding neuroimaging data. Regional delineations on morphological surfaces define anatomical landmarks and help to visualize and interpret both functional data and morphological measures mapped onto the cortical surface. We present an efficient algorithm that accurately delineates the morphological surface of the cerebral cortex in real time during generation of the surface using information from parcellated 3D data. With this accurate region delineation, we then develop methods for boundary-preserved simplification and smoothing, as well as procedures for the automated correction of small, misclassified regions to improve the quality of the delineated surface. We demonstrate that our delineation algorithm, together with a new method for double-snapshot visualization of cortical regions, can be used to establish a clear correspondence between brain anatomy and mapped quantities, such as morphological measures, across groups of subjects.