Aaron Wade1,2,Alice Llewellyn1,2,Tom Heenan1,2,Dan Brett1,2,Paul Shearing1,2
UCL1,Faraday Institution2
Aaron Wade1,2,Alice Llewellyn1,2,Tom Heenan1,2,Dan Brett1,2,Paul Shearing1,2
UCL1,Faraday Institution2
As the demand for high performance lithium-ion batteries grows, the use of nickel-rich cathodes is becoming more widespread. Of particular focus, due to its high theoretical capacity of 275mAh g-1, and low cobalt percentage, is nickel-manganese-cobalt-oxide LiNi<sub>0.8</sub>Mn<sub>0.1</sub>Co<sub>0.1</sub>O<sub>2</sub> (NMC811). However, this materials is plagued with unavoidable degradation mechanisms associated with volume changes that lead to particle cracking(1). Moreover, this cracking is particularly problematic at higher voltages(2), limiting the usable capacity. These defects can be detected via ex-situ nano-scale X-ray Computed Tomography (CT) (3), however, resolution limitations associated with micro-scale methods lead to defect analyses often being qualitative, rather than quantitative.<br/>A solution is to combine the in-situ tomographic data, with quantitative algorithms, which can determine the state-of-health (SoH) for hundreds of particles within a single scan. This has multiple benefits over traditional approaches. Firstly, defects that cannot be directly visualized due to particle averaging of data can be deconvoluted and extracted, allowing tracking and quantification. This enables more accessible, lower resolution scans (micro-CT) to replace time consuming, high resolution characterisation (nano-CT). Secondly, the large number of particles (hundreds or even thousands per scan) enable higher statistical confidence in the results compared to nano-scale work (where typically an order of magnitude fewer particles are examined). Thirdly, this methodology can overcome signal-to-noise issues commonly experienced with in-situ work, and can therefore be achieved within the laboratory without necessitating access to synchrotron facilities. And finally, due to in-situ measurements, the cause of defects within a particle can be explicitly revealed, removing the uncertainty over damage sustained during fabrication.<br/>The work combines lab-scale micro-scale imaging (spatial resolution ca. 200µm), with a novel algorithm, to quantitatively assess the SoH for particles as they are charged to increasingly higher voltages. This enables the voltage at which cracking initiates to be confirmed, alongside the degree of reversibility of such degradation to be measured. This knowledge will enable a deeper insight into how particles behave not only during standard conditions, but at the extreme voltages, paving the way for batteries with higher useable capacities and better capacity retention. Finally, due to the large datasets and imaging throughput, this methodology lends itself well for machine learning algorithms for automatic crack detection systems.<br/>1. Li H, Liu A, Zhang N, Wang Y, Yin S, Wu H, et al. An Unavoidable Challenge for Ni-Rich Positive Electrode Materials for Lithium-Ion Batteries. Chem Mater. 2019;31(18):7574–83.<br/>2. Li W, Asl HY, Xie Q, Manthiram A. Collapse of LiNi1- x- yCoxMnyO2 Lattice at Deep Charge Irrespective of Nickel Content in Lithium-Ion Batteries. J Am Chem Soc. 2019;141(13):5097–101.<br/>3. Heenan TMM, Wade A, Tan C, Parker JE, Matras D, Leach AS, et al. Identifying the Origins of Microstructural Defects Such as Cracking within Ni-Rich NMC811 Cathode Particles for Lithium-Ion Batteries. 2020;2002655.<br/>4. Tan C, Daemi S, Taiwo O, Heenan T, Brett D, Shearing P. Evolution of Electrochemical Cell Designs for In-Situ and Operando 3D Characterization. Materials (Basel) [Internet]. 2018;11(11):2157. Available from: http://www.mdpi.com/1996-1944/11/11/2157