• Cabana, Jordi
• Andrade, Vincent De

Abstract

<jats:p>Current industrial methodologies for lithium-ion battery cathode materials are suited to the production of well-defined, dense, spherical secondary particle structures assembled from nanocrystalline primary particles. Specifically, LiNi<jats:sub>x</jats:sub>Mn<jats:sub>y</jats:sub>Co<jats:sub>z</jats:sub>O<jats:sub>2</jats:sub> (LNMC) cathode material is synthesized by first growing the hydroxide precursor, Ni<jats:sub>x</jats:sub>Mn<jats:sub>y</jats:sub>Co<jats:sub>z</jats:sub>(OH)<jats:sub>2 </jats:sub>(NMC), through a coprecipitation method using a stoichiometric ratio of mixed transition metal sulfate solutions, sodium hydroxide, and ammonia hydroxide pumped into a continuous stirred tank reactor (CSTR). Each new composition of cathode material requires an extensive synthesis parameter optimization study to refine the even formation of metal species, density, and uniformity of size and shape of the final cathode material. The characterization and visualization of the three-dimensional secondary cathode structure, internal morphology, and void network is an elusive task using traditional scanning electron microscopy (SEM) because it is 2D technique that requires the use of mechanical cross-sectioning which can cause the deformation of the internal structure. Yet the variation of the internal structure and density changes within LNMC secondary particles can have large effects on both the cycling kinetics, capacity, and lifetime of the final cathode material.</jats:p><jats:p>In this study, we present a five-parameter optimization study of Ni<jats:sub>0.25</jats:sub>Mn<jats:sub>0.25</jats:sub>Co<jats:sub>0.50</jats:sub>(OH)<jats:sub>2</jats:sub> (NMC112) using our lab-built CSTR. The optimization and morphological changes of the primary and secondary particles during high temperature lithiation is investigated further. Using synchrotron-based transmission X-ray microscopy tomography (TXM) in combination with a novel deep learning semantic segmentation process, the three-dimensional cathode structure and internal pore network of LNMC112 particles was mapped across different lithiation temperature and reaction times. The deep learning semantic segmentation allowed for the quantification of void volume, shape, size, and final overall density of the cathode secondary particles. The results of these morphological differences show effects in both the kinetics and cyclability through galvanostatic cycling. The quantification of the three-dimensional cathode microstructure shown in this study is agnostic of the specific cathode composition. Therefore, it is highly suited to better inform optimization targets of the coprecipitation synthesis, as well as illuminating the importance the internal pore network of secondary particles.</jats:p>

Topics

• density
• impedance spectroscopy
• microstructure
• pore
• morphology
• scanning electron microscopy
• tomography
• Sodium
• Lithium
• void
• sectioning