Ensemble-based modeling of the NMR spectra of solid solutions : cation disorder in Y2(Sn,Ti)2O7
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The sensitivity of NMR to the local environment, without the need for any long-range order, makes it an ideal tool for the characterization of disordered materials. Computational prediction of NMR parameters can be of considerable help in the interpretation and assignment of NMR spectra of solids, but the statistical representation of all possible chemical environments for a solid solution is challenging. Here, we illustrate the use of a symmetry-adapted configurational ensemble in the simulation of NMR spectra, in combination with solid-state NMR experiments. We show that for interpretation of the complex and overlapped lineshapes that are typically observed, it is important to go beyond a single-configuration representation or a simple enumeration of local environments. The ensemble method leads to excellent agreement between simulated and experimental spectra for Y2(Sn,Ti)2O7 pyrochlore ceramics, where the overlap of signals from different local environments prevents a simple decomposition of the experimental spectral lineshapes. The inclusion of a Boltzmann weighting confirms that the best agreement with experiment is obtained at higher temperatures, in the limit of full disorder. We also show that to improve agreement with experiment, in particular at low dopant concentrations, larger supercells are needed, which might require alternative simulation approaches as the complexity of the system increases. It is clear that ensemble-based modeling approaches in conjunction with NMR spectroscopy offer great potential for understanding configurational disorder, ultimately aiding the future design of functional materials.
Moran , R F , McKay , D , Tornstrom , P , Aziz , A , Fernandes , A , Grau-Crespo , R & Ashbrook , S E 2019 , ' Ensemble-based modeling of the NMR spectra of solid solutions : cation disorder in Y 2 (Sn,Ti) 2 O 7 ' , Journal of the American Chemical Society , vol. 141 , no. 44 , pp. 17838-17846 . https://doi.org/10.1021/jacs.9b09036
Journal of the American Chemical Society
Copyright © 2019 American Chemical Society. This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
DescriptionFunding: ERC (EU FP7 Consolidator Grant 614290 ‘‘EXONMR’’). Royal Society and Wolfson Foundation merit award (SEA). We acknowledge support from the Collaborative Computational Project on NMR Crystallography CCP-NC funded by EPSRC (EP/M022501/1) and the UKCP consortium funded by EPSRC (EP/K013564/1). For computational resources we are grateful to the UK Materials and Molecular Modelling Hub, which is partially funded by EPSRC (EP/P020194/1), and to the UK HPC Materials Chemistry Consortium, which is funded by EPSRC (EP/L000202).
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