Prediction of NMR parameters and geometry in ¹³³Cs-containing compounds using density functional theory

Area of Science:

• Nuclear waste management
• Materials science
• Computational chemistry

Background:

• Immobilizing cesium-137 (¹³⁷Cs) nuclear waste is critical.
• Geopolymer and cement matrices are studied for waste immobilization.
• Understanding cesium (Cs) binding sites in these matrices is essential but challenging.

Purpose of the Study:

• To benchmark various density functional theory (DFT) exchange-correlation functionals.
• To evaluate their accuracy in predicting cesium (¹³³Cs) NMR parameters and geometry.
• To identify reliable DFT functionals for modeling Cs immobilization in waste matrices.

Main Methods:

• Benchmarking DFT functionals using various cesium compounds (salts, oxides, perovskites, glasses, etc.).
• Evaluating performance for predicting geometry, NMR quadrupolar coupling constants, and chemical shifts.
• Prioritizing functionals that include dispersion interactions and maintain low computational cost.

Main Results:

• No single DFT functional performed best for all tested parameters.
• rev-vdW-DF2 and PBEsol+D3 emerged as leading candidates.
• These functionals showed particular strength in predicting geometry and chemical shifts relevant to Cs immobilization.

Conclusions:

• DFT is a valuable tool for understanding Cs binding in nuclear waste matrices.
• rev-vdW-DF2 and PBEsol+D3 are recommended DFT functionals for future studies.
• Accurate modeling of Cs immobilization requires careful selection of DFT functionals.