A Small Angle Neuitron Scattering Investigation of Compacted Nanophase TiO2 and Pd

1989 ◽  
Vol 166 ◽  
Author(s):  
J. E. Epperson ◽  
R. W. Siegel ◽  
J. W. White ◽  
J. A. Eastman ◽  
Y. X. Liao ◽  
...  
Keyword(s):  
Cross Sections ◽  
Small Angle ◽  
Structural Parameters ◽  
Scattering Data ◽  
Bulk Materials ◽  
Angle Scattering ◽  
Gas Atmosphere ◽  
Nanophase Materials ◽  
Thin Disks

ABSTRACTNanocrystalline compacts of TiO2 and Pd were prepared by first condensing the Ti or Pd vapors in an inert gas atmosphere. The Ti was oxidized in situ to TiO2. Samples were prepared by scraping off and compacting the nanophase materials into thin disks. The small angle neutron scattering was measured in the as-prepared condition and after isothermal anneals of up to 23 hrs at 550°C for the TiO2 and up to 3.3 hrs at 300°C for the Pd. Scattering data were obtained in absolute cross sections. Integrated small angle scattering and maximum entropy methods were used in estimating the structural parameters. The results are interpreted in terms of a model which consists of nanometer sized grains of the materials separated by boundary regions which are, on average, much less dense than the respective bulk materials; 21% for the TiO2 and about 56% for Pd. However, the boundary regions contain voids or pores, which contribute to these density decrements. Possible sources of error are discussed.

scholarly journals Classifying and analyzing small-angle scattering data using weighted k nearest neighbors machine learning techniques

2020 ◽  
Vol 53 (2) ◽  
pp. 326-334
Author(s):  
Richard K. Archibald ◽  
Mathieu Doucet ◽  
Travis Johnston ◽  
Steven R. Young ◽  
Erika Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Angle ◽  
Structural Parameters ◽  
Nearest Neighbors ◽  
Small Angle Scattering ◽  
Machine Learning Techniques ◽  
Stochastic Gradient Descent ◽  
Scattering Data ◽  
Gradient Descent Method ◽  
Angle Scattering

A consistent challenge for both new and expert practitioners of small-angle scattering (SAS) lies in determining how to analyze the data, given the limited information content of said data and the large number of models that can be employed. Machine learning (ML) methods are powerful tools for classifying data that have found diverse applications in many fields of science. Here, ML methods are applied to the problem of classifying SAS data for the most appropriate model to use for data analysis. The approach employed is built around the method of weighted k nearest neighbors (wKNN), and utilizes a subset of the models implemented in the SasView package (https://www.sasview.org/) for generating a well defined set of training and testing data. The prediction rate of the wKNN method implemented here using a subset of SasView models is reasonably good for many of the models, but has difficulty with others, notably those based on spherical structures. A novel expansion of the wKNN method was also developed, which uses Gaussian processes to produce local surrogate models for the classification, and this significantly improves the classification accuracy. Further, by integrating a stochastic gradient descent method during post-processing, it is possible to leverage the local surrogate model both to classify the SAS data with high accuracy and to predict the structural parameters that best describe the data. The linking of data classification and model fitting has the potential to facilitate the translation of measured data into results for both novice and expert practitioners of SAS.

ELLSTAT: shape modeling for solution small-angle scattering of proteins and protein complexes with automated statistical characterization

2006 ◽  
Vol 39 (5) ◽  
pp. 671-675 ◽  
Author(s):  
William T. Heller
Keyword(s):  
Small Angle ◽  
Protein Complexes ◽  
Structural Parameters ◽  
Shape Modeling ◽  
Scattering Data ◽  
Model Parameters ◽  
Data Sets ◽  
Statistical Characterization ◽  
X Ray ◽  
Angle Scattering

A method is presented for constructing one- and two-ellipsoid, core-shell-ellipsoid, cylinder and ellipsoid-plus-cylinder models from small-angle X-ray and neutron scattering data that calculates statistics on the resulting structural parameters. The method, implemented in the softwareELLSTAT, is capable of simultaneously fitting against several data sets and calculates averages, standard deviations and coefficients of linear correlation between the structural parameters of the resulting models. In this way, an improved understanding of the extent of the variability in and the interdependency between the model parameters that fit the input scattering data is developed, thereby providing a measure of the uniqueness of the models.

Interpretation of small-angle scattering data of inhomogeneous ellipsoids

2004 ◽  
Vol 37 (5) ◽  
pp. 815-822 ◽  
Author(s):  
Gerhard Fritz ◽  
Alexander Bergmann
Keyword(s):  
Cross Sections ◽  
Small Angle ◽  
Theoretical Calculations ◽  
Negative Contrast ◽  
Small Angle Scattering ◽  
Distribution Functions ◽  
Distance Distribution ◽  
Scattering Data ◽  
Elliptical Cross ◽  
Angle Scattering

Small-angle scattering data of inhomogeneous ellipsoidal particles are discussed in terms of their pair distance distribution functionsp(r). Special attention is given to the determination of core and shell thicknesses and axis ratios as well as to large distances within the particles, since cross terms between parts of positive and negative contrast within the particle can produce misleading results, similar to homogeneous particles or Janus particles. Cross-section pair distance distribution functionspc(r) of cylinders with elliptical cross sections show similar behaviour. Theoretical calculations are compared with small-angle X-ray and neutron scattering (SAXS and SANS) data of cetyltrimethylammonium bromide in aqueous KCl solutions.

scholarly journals Vesicle Viewer: Online Analysis of Small Angle Scattering from Lipid Vesicles

2021 ◽  
Author(s):  
Aislyn Lewis-Laurent ◽  
Milka Doktorova ◽  
Frederick A. Heberle ◽  
Drew Marquardt
Keyword(s):  
Lipid Bilayers ◽  
Small Angle ◽  
Web Applications ◽  
Structural Parameters ◽  
Lipid Vesicles ◽  
Small Angle Scattering ◽  
Scattering Data ◽  
Web Based ◽  
Angle Scattering ◽  
Area Per Lipid

In this project, we developed an internet-based application, called Vesicle Viewer, to visualize and analyze small angle scattering data generated in the study of lipid bilayers. Vesicle Viewer models SAS data using the EZ-SDP model. In this way, key bilayer structural parameters, such as area per lipid and bilayer thickness, can be easily determined. This application primarily uses Django, a python package specialized for the development of robust web applications. In addition, several other libraries are used to support the more technical aspects of the project – notable examples are MatPlotLib (for graphs) and NumPy (for calculations). Without the barrier of downloading and installing software, the development of this web-based application will allow scientists all over the world to take advantage of this solution, regardless of their preferred operating system.

scholarly journals Vesicle Viewer: Online Analysis of Small Angle Scattering from Lipid Vesicles

2021 ◽  
Author(s):  
Aislyn Lewis-Laurent ◽  
Milka Doktorova ◽  
Frederick A. Heberle ◽  
Drew Marquardt
Keyword(s):  
Lipid Bilayers ◽  
Small Angle ◽  
Web Applications ◽  
Structural Parameters ◽  
Lipid Vesicles ◽  
Small Angle Scattering ◽  
Scattering Data ◽  
Web Based ◽  
Angle Scattering ◽  
Area Per Lipid

In this project, we developed an internet-based application, called Vesicle Viewer, to visualize and analyze small angle scattering data generated in the study of lipid bilayers. Vesicle Viewer models SAS data using the EZ-SDP model. In this way, key bilayer structural parameters, such as area per lipid and bilayer thickness, can be easily determined. This application primarily uses Django, a python package specialized for the development of robust web applications. In addition, several other libraries are used to support the more technical aspects of the project – notable examples are MatPlotLib (for graphs) and NumPy (for calculations). Without the barrier of downloading and installing software, the development of this web-based application will allow scientists all over the world to take advantage of this solution, regardless of their preferred operating system.

scholarly journals 2017 publication guidelines for structural modelling of small-angle scattering data from biomolecules in solution: an update

2017 ◽  
Vol 73 (9) ◽  
pp. 710-728 ◽  
Author(s):  
Jill Trewhella ◽  
Anthony P. Duff ◽  
Dominique Durand ◽  
Frank Gabel ◽  
J. Mitchell Guss ◽  
...  
Keyword(s):  
Small Angle ◽  
Task Force ◽  
Data Bank ◽  
Small Angle Scattering ◽  
Scattering Data ◽  
Quality Data ◽  
Angle Scattering ◽  
Publication Guidelines ◽  
Guidelines And Recommendations

In 2012, preliminary guidelines were published addressing sample quality, data acquisition and reduction, presentation of scattering data and validation, and modelling for biomolecular small-angle scattering (SAS) experiments. Biomolecular SAS has since continued to grow and authors have increasingly adopted the preliminary guidelines. In parallel, integrative/hybrid determination of biomolecular structures is a rapidly growing field that is expanding the scope of structural biology. For SAS to contribute maximally to this field, it is essential to ensure open access to the information required for evaluation of the quality of SAS samples and data, as well as the validity of SAS-based structural models. To this end, the preliminary guidelines for data presentation in a publication are reviewed and updated, and the deposition of data and associated models in a public archive is recommended. These guidelines and recommendations have been prepared in consultation with the members of the International Union of Crystallography (IUCr) Small-Angle Scattering and Journals Commissions, the Worldwide Protein Data Bank (wwPDB) Small-Angle Scattering Validation Task Force and additional experts in the field.

Comparison of iterative desmearing procedures for one-dimensional small-angle scattering data

2011 ◽  
Vol 44 (1) ◽  
pp. 32-42 ◽  
Author(s):  
Thomas Vad ◽  
Wiebke F. C. Sager
Keyword(s):  
Small Angle ◽  
Incident Beam ◽  
Scattering Data ◽  
Data Sets ◽  
Convergence Criteria ◽  
Small Momentum Transfer ◽  
One Dimensional ◽  
Noise Filter ◽  
Large Numbers ◽  
Angle Scattering

Two simple iterative desmearing procedures – the Lake algorithm and the Van Cittert method – have been investigated by introducing different convergence criteria using both synthetic and experimental small-angle neutron scattering data. Implementing appropriate convergence criteria resulted in stable and reliable solutions in correcting resolution errors originating from instrumental smearing,i.e.finite collimation and polychromaticity of the incident beam. Deviations at small momentum transfer for concentrated ensembles of spheres encountered in earlier studies are not observed. Amplification of statistical errors can be reduced by applying a noise filter after desmearing. In most cases investigated, the modified Lake algorithm yields better results with a significantly smaller number of iterations and is, therefore, suitable for automated desmearing of large numbers of data sets.

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