A Simulated Annealing Method for Determining Atomic Distributions from NMR Data:  Silicon and Aluminum in Faujasite

Abstract

Simulated annealing is applied to the determination of distributions of silicon and aluminum atoms on the faujasite lattice from 29Si NMR data. The method is described and compared to others. The local silicon environments, Si(nAl), n = 0−4, are reproduced to high accuracy by the technique. More detailed features of local structure are then available, such as numbers of Al on different ring systems in the zeolite. Dempsey's rule is found to be strictly followed in the four rings of the zeolite X sample studied, while this is not the case for zeolite Yin agreement with previous work. The results are consistent with a possible small violation of Lowenstein's rule in the zeolite X sample.

A Computational Method To Characterize Framework Aluminum in Aluminosilicates

“…This information is less accurate than the average from XRD, but the ability of EXAFS to probe the local structure around a selected element allows easy in situ measurements. [14][15][16][17][18][19][20] Herein, we present an alternative theoretical approach in which we identify those experimentally accessible properties that are crucially dependent on the aluminum distribution and associated cation distribution. [5] Several attempts have been made to probe structural variations by MAS-NMR spectroscopy, [6][7][8] but as soon as even small amounts of aluminum are present in the framework, the resolution is degraded and the spectra provide insufficient information about aluminum site preferences.…”

Using Crystallography and NMR to Count the Number of Three-Aluminum Six-Rings in Fully Zn2+-Exchanged Zeolite Y. These Six-Rings Concentrate at Single Six-Ring Positions

“…A previous simulated annealing (SA) study of faujasites, using as input the 29 Si NMR data of Melchior et al ., produced multiple-unit-cell simulated crystal structure models with either a silicon or an aluminum atom at each tetrahedral site. The SA method was used to minimize an objective function, F = ∑ n =0,4 ( f n sim – f n expt ) 2 , which represents the difference between the simulation and experiment.…”

“…(For example, when n = 0, the central Si atom is surrounded by zero Al atoms, hence by four Si atoms.) It is important to note that no physical interaction energy between any of the atoms is assumed or used anywhere in the objective function; the only factors that determine the results are the input experimental 29 Si NMR peak areas, the topology of the FAU framework, Loewenstein’s rule, and to some extent the details of the computations and the annealing schedule . The resulting system has a distribution of Si and Al atoms that is consistent with all of those inputs and can be inspected to determine the number and positioning of the Al atoms in any substructure of interest, e.g., S6R or D6R.…”

A Computational Method to Characterize Framework Aluminum in Aluminosilicates.

“…[7,[10][11][12][13] The aluminum distribution on the crystal level, as well as the distribution on the level of a single unit cell, remains a subject of debate. [14][15][16][17][18][19][20] Herein, we present an alternative theoretical approach in which we identify those experimentally accessible properties that are crucially dependent on the aluminum distribution and associated cation distribution. Once these properties have been identified, we compute the most likely positions of aluminum in zeolites by matching simulation results with available experimental data.…”