Authors: Katharina Märker, Philip J. Reeves, Chao Xu, Kent J. Griffith, and Clare P. Grey
Case study author: Christopher O'Keefe
Department of Chemistry, University of Cambridge

The detailed information provided by NMR spectroscopy has made it an indispensable tool in the study of battery materials. Many of the cathode materials used in Li-ion batteries contain paramagnetic transition metal (TM) ions, which makes the acquisition of \(^7\)Li NMR spectra for these materials challenging due to rapidly decaying signals, broad features, and large hyperfine shifts. If the TMs are disordered in the cathode, these issues are exacerbated, and the assignment of the NMR spectra becomes more difficult. Expected hyperfine shifts of \(^7\)Li resonances due to interactions from individual TMs can be calculated using density functional theory (DFT); these contributions are (generally) additive such that the total \(^7\)Li hyperfine shift of each Li in the structure can be determined by considering the possible TMs in the first and second coordination sphere.

In a recent publication in Chem. Mater. 2019, 31, 2545-2554, this approach was used to understand the structural changes and Li dynamics in a high-capacity layered oxide cathode material, Li\(_{1-x}\)Ni\(_{0.8}\)Mn\(_{0.1}\)Co\(_{0.1}\)O\(_2\) (NMC811). Table 1 lists the expected hyperfine \(^7\)Li shifts from the paramagnetic TMs in the first (i.e., Li-O-TM bond angle of 90°) and second (i.e., Li-O-TM bond angle of 180°) coordination spheres. A \(^7\)Li spectrum (Figure 1) was then calculated for the pristine (i.e., x = 0) cathode material by considering the hyperfine shifts resulting from all possible 784 Li environments based on a random distribution of TMs. The calculated spectrum (red trace) matches reasonably well with the experimental spectrum (black trace), with discrepancies a result of possible TM ordering. At higher states of charge (i.e., 0.25 ≤ x ≤ 0.65), the experimental \(^7\)Li NMR patterns narrow, and spectra were calculated by considering changes in the bond pathway shifts (due to Ni oxidation and lattice expansion) and accounting for the effect of Li+ mobility. A combination of \(^7\)Li NMR and DFT was shown to be a useful tool to study the dynamics of Li ions in a challenging paramagnetic material. The characterization of Li mobility is crucial for understanding the rate performance of cathode materials. Our group has also developed bond pathway analysis to several other nuclei, including \(^{23}\)Na, \(^{31}\)P, \(^{25}\)Mg and \(^{17}\)O, enabling the evolution of the local structures of paramagnetic cathode materials to be closely monitored.

Professor Sharon Ashbrook, School of Chemistry, University of St Andrews

The sensitivity of NMR spectroscopy to the local environment makes it an ideal tool for the characterization of disordered materials. Many structural models derived from XRD do not identify the position of light atoms such as \(^1\)H, but the position of these can have significant impact on the physical and chemical properties of materials.

In work published in J. Am. Chem. Soc. 141, 3024 (2019) and funded by the ERC, we have used an NMR crystallographic approach to understand the hydration of the silicate minerals that are present many hundreds of km below our feet in the Earth’s transition zone. Little is known about the mechanism and position of this hydration, despite the implications it has for mantle dynamics. We used ab initio random structure searching (AIRSS) to generate a set of possible hydrated structures for wadsleyite (\(\beta\)-Mg\(_2\)SiO\(_4\)), removing one or more Mg cations and randomly placing protons in the system. k-means clustering approaches, implemented in Soprano, allowed us to select sets of structures for which NMR parameters could then be calculated and compared to experimental measurements. It was not possible to reproduce the experimental spectra by simply considering the ground state structure, but good agreement was obtained when four low energy states were considered. Thus, the combination of experimental measurement, structure searching and computational prediction are able to provide new and detailed insight into the structure of the inner Earth.