Prediction of NMR Parameters and geometry in 133Cs-containing compounds using Density Functional Theory
Nurit Manukovsky, Noy Vaisleib, Michal Arbel-Haddad, Amir Goldbourt

TL;DR
This study benchmarks density functional theory functionals to accurately predict NMR parameters and geometry of Cs compounds, aiding the understanding of Cs binding in waste matrices.
Contribution
It identifies suitable DFT functionals for modeling 133Cs NMR parameters and geometry in various compounds relevant to nuclear waste immobilization.
Findings
rev-vdW-DF2 and PBEsol+D3 perform best for geometry and chemical shifts
No single functional excels across all parameters
Benchmarking guides functional choice for Cs-containing materials
Abstract
The need to immobilize low-level nuclear waste, in particular 137Cs-bearing waste, has led to a growing interest in geopolymer-based waste matrices, in addition to optimization attempts of cement matrix compositions for this specific application. Although the overall phase composition and structure of these matrices are well characterized, the binding sites of Cs in these materials have not been clearly identified. Recent studies have suggested that combining the sensitivity of solid-state Nuclear Magnetic Resonance (SSNMR) to the local atomic structure with other structural techniques provides insights into the mode of Cs binding and release. Density Functional Theory (DFT) can provide the connection between spectroscopic parameters and geometric properties. However, the reliability of DFT results strongly relies on the choice of a suitable exchange-correlation functional, which for…
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Taxonomy
TopicsAdvanced NMR Techniques and Applications
