The need for operando modelling of 27 Al NMR in zeolites: the effect of temperature, topology and water.

Autor:	Lei C; Department of Physical and Macromolecular Chemistry, Faculty of Science, Charles University in Prague 128 43 Prague 2 Czech Republic heardc@natur.cuni.cz., Erlebach A; Department of Physical and Macromolecular Chemistry, Faculty of Science, Charles University in Prague 128 43 Prague 2 Czech Republic heardc@natur.cuni.cz., Brivio F; Department of Physical and Macromolecular Chemistry, Faculty of Science, Charles University in Prague 128 43 Prague 2 Czech Republic heardc@natur.cuni.cz., Grajciar L; Department of Physical and Macromolecular Chemistry, Faculty of Science, Charles University in Prague 128 43 Prague 2 Czech Republic heardc@natur.cuni.cz., Tošner Z; Department of Physical and Macromolecular Chemistry, Faculty of Science, Charles University in Prague 128 43 Prague 2 Czech Republic heardc@natur.cuni.cz., Heard CJ; Department of Physical and Macromolecular Chemistry, Faculty of Science, Charles University in Prague 128 43 Prague 2 Czech Republic heardc@natur.cuni.cz., Nachtigall P; Department of Physical and Macromolecular Chemistry, Faculty of Science, Charles University in Prague 128 43 Prague 2 Czech Republic heardc@natur.cuni.cz.
Jazyk:	angličtina
Zdroj:	Chemical science [Chem Sci] 2023 Aug 03; Vol. 14 (34), pp. 9101-9113. Date of Electronic Publication: 2023 Aug 03 (Print Publication: 2023).
DOI:	10.1039/d3sc02492j
Abstrakt:	Solid state (ss-) 27 Al NMR is one of the most valuable tools for the experimental characterization of zeolites, owing to its high sensitivity and the detailed structural information which can be extracted from the spectra. Unfortunately, the interpretation of ss-NMR is complex and the determination of aluminum distributions remains generally unfeasible. As a result, computational modelling of 27 Al ss-NMR spectra has grown increasingly popular as a means to support experimental characterization. However, a number of simplifying assumptions are commonly made in NMR modelling, several of which are not fully justified. In this work, we systematically evaluate the effects of various common models on the prediction of 27 Al NMR chemical shifts in zeolites CHA and MOR. We demonstrate the necessity of operando modelling; in particular, taking into account the effects of water loading, temperature and the character of the charge-compensating cation. We observe that conclusions drawn from simple, high symmetry model systems such as CHA do not transfer well to more complex zeolites and can lead to qualitatively wrong interpretations of peak positions, Al assignment and even the number of signals. We use machine learning regression to develop a simple yet robust relationship between chemical shift and local structural parameters in Al-zeolites. This work highlights the need for sophisticated models and high-quality sampling in the field of NMR modelling and provides correlations which allow for the accurate prediction of chemical shifts from dynamical simulations. Competing Interests: There are no conflicts to declare. (This journal is © The Royal Society of Chemistry.)
Databáze:	MEDLINE
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