Literature DB >> 30672073

NMR Crystallography: Evaluation of Hydrogen Positions in Hydromagnesite by 13 C{1 H} REDOR Solid-State NMR and Density Functional Theory Calculation of Chemical Shielding Tensors.

Jinlei Cui1, David L Olmsted2, Anil K Mehta3, Mark Asta2,4, Sophia E Hayes1.   

Abstract

Solid-state NMR measurements coupled with density functional theory (DFT) calculations demonstrate how hydrogen positions can be refined in a crystalline system. The precision afforded by rotational-echo double-resonance (REDOR) NMR to interrogate 13 C-1 H distances is exploited along with DFT determinations of the 13 C tensor of carbonates (CO3 2- ). Nearby 1 H nuclei perturb the axial symmetry of the carbonate sites in the hydrated carbonate mineral, hydromagnesite [4 MgCO3 ⋅Mg(OH)2 ⋅4 H2 O]. A match between the calculated structure and solid-state NMR was found by testing multiple semi-local and dispersion-corrected DFT functionals and applying them to optimize atom positions, starting from X-ray diffraction (XRD)-determined atomic coordinates. This was validated by comparing calculated to experimental 13 C{1 H} REDOR and 13 C chemical shift anisotropy (CSA) tensor values. The results show that the combination of solid-state NMR, XRD, and DFT can improve structure refinement for hydrated materials.
© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  13C{1H} REDOR; CSA lineshape; NMR spectroscopy; computational chemistry; hydromagnesite

Year:  2019        PMID: 30672073     DOI: 10.1002/anie.201813306

Source DB:  PubMed          Journal:  Angew Chem Int Ed Engl        ISSN: 1433-7851            Impact factor:   15.336


  3 in total

1.  Characterization of Chemisorbed Species and Active Adsorption Sites in Mg-Al Mixed Metal Oxides for High-Temperature CO2 Capture.

Authors:  Alicia Lund; G V Manohara; Ah-Young Song; Kevin Maik Jablonka; Christopher P Ireland; Li Anne Cheah; Berend Smit; Susana Garcia; Jeffrey A Reimer
Journal:  Chem Mater       Date:  2022-04-21       Impact factor: 10.508

2.  Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Methods.

Authors:  Mojtaba Haghighatlari; Jie Li; Farnaz Heidar-Zadeh; Yuchen Liu; Xingyi Guan; Teresa Head-Gordon
Journal:  Chem       Date:  2020-06-16       Impact factor: 22.804

3.  Exploring Accuracy Limits of Predictions of the 1H NMR Chemical Shielding Anisotropy in the Solid State.

Authors:  Jiří Czernek; Jiří Brus
Journal:  Molecules       Date:  2019-05-03       Impact factor: 4.411

  3 in total

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