Characterizing slight structural disorder in solids by combined solid-state NMR and first principles calculations

Area of Science:

• Solid-state chemistry
• Materials science
• Computational chemistry

Background:

• Interpreting local structural disorder in partially ordered solids is crucial for understanding material properties.
• Traditional methods often struggle to provide detailed structural insights into subtle disorder.
• Nuclear Magnetic Resonance (NMR) spectroscopy offers a powerful, non-destructive probe of local atomic environments.

Purpose of the Study:

• To develop a general approach for the structural interpretation of local disorder in partially ordered solids.
• To combine high-resolution two-dimensional (2D) NMR with first-principles calculations for detailed structural analysis.
• To identify specific bonding geometry changes contributing to local structural disorder.

Main Methods:

• Utilized high-resolution 2D NMR spectroscopy, specifically through-space correlation experiments with variable mixing times.
• Employed advanced density functional theory (DFT) methods for first-principles calculations.
• Generated a basis set of plausible local distortions from calculated low-energy vibration modes to model disorder.

Main Results:

• Demonstrated that small chemical shift variations (around 1 ppm) can be structurally interpreted using DFT.
• Showcased the ability to probe the existence and spatial range of small-amplitude disorder via 2D NMR line shape analysis.
• Successfully simulated 2D NMR correlation lineshapes to evaluate potential sources of disorder by comparing with experimental data.

Conclusions:

• The combined 2D NMR and first-principles calculation approach provides novel structural constraints for analyzing local disorder.
• Identified specific structural deformations compatible with experimental 2D NMR data, offering insights into bonding geometry.
• The findings enable a more precise understanding of local disorder in partially ordered solids, complementing X-ray diffraction data.