scholarly journals Disorder in La1−x Ba1+x GaO4−x/2 ionic conductor: resolving the pair distribution function through insight from first-principles modeling

2019 ◽  
Vol 52 (4) ◽  
pp. 712-721 ◽  
Author(s):  
Mauro Coduri ◽  
Simone Casolo ◽  
Niina Jalarvo ◽  
Marco Scavini

Ionic conduction in dry LaBaGaO4 occurs through the vacant oxygen sites formed by the substitution of Ba for La. The resulting La1−x Ba1+x GaO4−x/2 solid solution shows significant disorder characteristics. The local structure of compositions x = 0, 0.20 and 0.30 was studied using the pair distribution function (PDF). Unfortunately, increasing peak overlap and the number of independent structural parameters make PDF modeling challenging when dealing with low-symmetry phases. To overcome this problem, density functional theory (DFT) was employed to create different structural models, each one with a different relative position for the substitutional Ba ion with respect to the oxygen vacancy. The atomic distributions generated by DFT were used as a starting point to refine experimental PDF data. All models result in the formation of Ga2O7 dimers, with their major axis oriented along the c axis. At the local scale, the most stable DFT model also provides the best fit of the PDF. This accounts for the dopant as first and second neighbors of the vacancy and of the O bridge in the dimer, suggesting that substitutional barium ions act as pinning centers for oxygen vacancies. Above 6 Å the average orthorhombic structure fits the PDF better than the DFT models, thus indicating that Ga2O7 dimers are not correlated with each other to form extended ordered structures. The combination of DFT simulations and X-ray diffraction/PDF refinements was used successfully to model the local atomic structure in La1−x Ba1+x GaO4−x/2, thus suggesting that this approach could be positively applied in general to disordered systems.

2021 ◽  
Vol 54 (2) ◽  
Author(s):  
Alan A. Coelho ◽  
Philip A. Chater ◽  
Michael J. Evans

A method for generating the atomic pair distribution function (PDF) from powder diffraction data by the removal of instrument contributions, such as Kα2 from laboratory instruments or peak asymmetry from neutron time-of-flight data, has been implemented in the computer programs TOPAS and TOPAS-Academic. The resulting PDF is sharper, making it easier to identify structural parameters. The method fits peaks to the reciprocal-space diffraction pattern data whilst maximizing the intensity of a background function. The fit to the raw data is made `perfect' by including a peak at each data point of the diffraction pattern. Peak shapes are not changed during refinement and the process is a slight modification of the deconvolution procedure of Coelho [J. Appl. Cryst. (2018), 51, 112–123]. Fitting to the raw data and subsequently using the calculated pattern as an estimation of the underlying signal reduces the effects of division by small numbers during atomic scattering factor and polarization corrections. If the peak shape is sufficiently accurate then the fitting process should also be able to determine the background if the background intensity is maximized; the resulting calculated pattern minus background should then comprise coherent scattering from the sample. Importantly, the background is not allowed complete freedom; instead, it comprises a scan of an empty capillary sample holder with a maximum of two additional parameters to vary its shape. Since this coherent scattering is a calculated pattern, it can be easily recalculated without instrumental aberrations such as capillary sample aberration or Kα2 from laboratory emission profiles. Additionally, data reduction anomalies such as incorrect integration of data from two-dimensional detectors, resulting in peak position errors, can be easily corrected. Multiplicative corrections such as polarization and atomic scattering factors are also performed. Once corrected, the pattern can be scaled to produce the total scattering structure factor F(Q) and from there the sine transform is applied to obtain the pair distribution function G(r).


ChemInform ◽  
2007 ◽  
Vol 38 (42) ◽  
Author(s):  
Katharine Page ◽  
Matthew W. Stoltzfus ◽  
Young-Il Kim ◽  
Thomas Proffen ◽  
Patrick M. Woodward ◽  
...  

2007 ◽  
Vol 19 (16) ◽  
pp. 4037-4042 ◽  
Author(s):  
Katharine Page ◽  
Matthew W. Stoltzfus ◽  
Young-Il Kim ◽  
Thomas Proffen ◽  
Patrick M. Woodward ◽  
...  

2015 ◽  
Vol 48 (3) ◽  
pp. 869-875 ◽  
Author(s):  
A. A. Coelho ◽  
P. A. Chater ◽  
A. Kern

A fast method for calculating the atomic pair distribution function is described in the context of performing refinements of structural models. Central to the speed of synthesis is the approximation of Gaussian functions of varying full widths at half-maximum using a narrower Gaussian with a fixed full width at half-maximum. The initial Gaussians are first laid down as delta functions which are then convoluted with the narrower Gaussian to form the final pattern. The net result is an algorithm, which has been included in the Rietveld refinement computer programTOPAS, that synthesizes and refines structural parameters a factor of 300–1000 times faster than alternative algorithms/programs, with speed advantages increasing as the number of atomic pairs increases.


2011 ◽  
Vol 44 (4) ◽  
pp. 788-797 ◽  
Author(s):  
Katharine Mullen ◽  
Igor Levin

Information on the size and structure of nanoparticles can be obtainedviaanalysis of the atomic pair distribution function (PDF), which is calculated as the Fourier transform of X-ray/neutron total scattering. The structural parameters are commonly extracted by fitting a model PDF calculated from atomic coordinates to the experimental data. This paper discusses procedures for minimizing systematic errors in PDF calculations for nanoparticles and also considers the effects of noise due to counting statistics in total scattering data used to obtain the PDF. The results presented here demonstrate that smoothing of statistical noise in reciprocal-space data can improve the precision of parameter estimates obtained from PDF analysis, facilitating identification of the correct model (from multiple plausible choices) from real-space PDF fits.


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