Generalizing small-angle scattering form factors with linear transformations

2020 ◽  
Vol 53 (5) ◽  
pp. 1387-1391
Author(s):  
Matt Thompson

Nanostructure characterization using small-angle scattering is often performed by iteratively fitting a scattering model to experimental data. These scattering models are usually derived in part from the form factors of the expected shapes of the particles. Most small-angle-scattering pattern-fitting software is well equipped with form factor libraries for high-symmetry models, yet there is more limited support for distortions to these ideals that are more typically found in nature. Here, a means of generalizing high-symmetry form factors to these lower-symmetry cases via linear transformations is introduced, significantly expanding the range of form factors available to researchers. These linear transformations are composed of a series of scaling, shear, rotation and inversion operations, enabling particle distortions to be understood in a straightforward and intuitive way. This approach is expected to be especially useful for in situ studies of nanostructure growth where anisotropic structures change continuously and large data sets must be analysed.

2021 ◽  
Vol 54 (2) ◽  
pp. 580-587
Author(s):  
Joachim Wuttke

Coordinate-free expressions for the form factors of arbitrary polygons and polyhedra are derived using the divergence theorem and Stokes's theorem. Apparent singularities, all removable, are discussed in detail. Cancellation near the singularities causes a loss of precision that can be avoided by using series expansions. An important application domain is small-angle scattering by nanocrystals.


2018 ◽  
Vol 51 (4) ◽  
pp. 1151-1161 ◽  
Author(s):  
Andreas Haahr Larsen ◽  
Lise Arleth ◽  
Steen Hansen

The structure of macromolecules can be studied by small-angle scattering (SAS), but as this is an ill-posed problem, prior knowledge about the sample must be included in the analysis. Regularization methods are used for this purpose, as already implemented in indirect Fourier transformation and bead-modeling-based analysis of SAS data, but not yet in the analysis of SAS data with analytical form factors. To fill this gap, a Bayesian regularization method was implemented, where the prior information was quantified as probability distributions for the model parameters and included via a functional S. The quantity Q = χ2 + αS was then minimized and the value of the regularization parameter α determined by probability maximization. The method was tested on small-angle X-ray scattering data from a sample of nanodiscs and a sample of micelles. The parameters refined with the Bayesian regularization method were closer to the prior values as compared with conventional χ2 minimization. Moreover, the errors on the refined parameters were generally smaller, owing to the inclusion of prior information. The Bayesian method stabilized the refined values of the fitted model upon addition of noise and can thus be used to retrieve information from data with low signal-to-noise ratio without risk of overfitting. Finally, the method provides a measure for the information content in data, N g, which represents the effective number of retrievable parameters, taking into account the imposed prior knowledge as well as the noise level in data.


2013 ◽  
Vol 47 (1) ◽  
pp. 84-94 ◽  
Author(s):  
Cassio Alves ◽  
Jan Skov Pedersen ◽  
Cristiano Luis Pinto Oliveira

A versatile procedure to build high-symmetry objects and to calculate their corresponding small-angle scattering intensity is presented. Starting from a set of vertex positions, available from a large and extensible database, it is possible to build several types of bodies using spherical subunits. A fast implementation, based on the Debye formula using a histogram of distance, is then used to compute the theoretical scattering intensity. Since the model is built from the definition of a small set of parameters, it is possible to perform an optimization of structural parameters against experimental data. Finally, affine size polydispersities can be easily included by the rescaling of the histogram of the positions used in the calculations. Several examples of the calculations are presented, demonstrating the method and its applicability.


2010 ◽  
Vol 43 (3) ◽  
pp. 639-646 ◽  
Author(s):  
S. Förster ◽  
L. Apostol ◽  
W. Bras

Scatteris a new software for analysis, modeling and fitting of one- and two-dimensional small-angle scattering data of non-ordered, partially ordered or fully ordered nano- and mesoscale structures. The calculations are based on closed analytical expressions for the scattering intensity, enabling efficient evaluation of form factors and structure factors. The software allows one to sequentially fit large series of scattering curves and scattering patterns automatically. It provides further tools for data loading, beam centering, calibration, zooming, binning, lattice identification, calculation of density profiles and size distributions, and visualization of real-space structures. Presentations of experimental and calculated data can be saved as is for presentations or exported for further graphical or mathematical treatment.


1993 ◽  
Vol 03 (C8) ◽  
pp. C8-393-C8-396
Author(s):  
T. P.M. BEELEN ◽  
W. H. DOKTER ◽  
H. F. VAN GARDEREN ◽  
R. A. VAN SANTEN ◽  
E. PANTOS

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