Carbon-13 chemical shift tensor measurements for nitrogen-dense compounds

Area of Science:

• Solid-state Nuclear Magnetic Resonance (NMR) Spectroscopy
• Computational Chemistry
• Materials Science

Background:

• ¹³C chemical shift tensors are crucial for understanding molecular structure and electronic environments.
• Experimental data for many nitrogen-dense organic compounds, such as cytosine and imidazole, are scarce in the literature.
• Accurate tensor data is vital for validating computational chemistry methods and developing NMR-based structure determination techniques.

Purpose of the Study:

• To experimentally determine the principal values of ¹³C chemical shift tensors for five important nitrogen-dense compounds.
• To compare experimental results with theoretical calculations to assess the accuracy of quantum chemical methods.
• To contribute to a growing database of ¹³C chemical shift tensors for future applications in NMR crystallography and data mining.

Main Methods:

• ¹H→¹³C cross-polarization magic-angle spinning (CP/MAS) NMR experiments were performed at a high magnetic field (18.8 T).
• High-field experiments were used to minimize ¹⁴N-¹³C residual dipolar coupling effects.
• Density functional theory (DFT) calculations, including hybrid (PBE0) and double-hybrid (PBE0-DH) functionals with large basis sets (TZ2P, pc-3) and COSMO solvation models, were employed.

Main Results:

• Principal values of ¹³C chemical shift tensors were successfully obtained for cytosine, uracil, imidazole, guanidine hydrochloride, and aminoguanidine hydrochloride.
• Calculations using PBE0 or PBE0-DH functionals with TZ2P or pc-3 basis sets, respectively, showed excellent agreement with experimental data.
• Intermolecular effects, modeled using large molecular clusters and electrostatic embedding (COSMO), were essential for accurate theoretical predictions.

Conclusions:

• The study provides valuable experimental ¹³C chemical shift tensor data for fundamental nitrogen-dense compounds.
• Specific DFT functionals and basis sets were identified as highly reliable for predicting these tensors.
• The generated data will serve as a benchmark for computational methods and support the development of NMR-based applications.