Thermal and structural properties of B2O3–H2O glasses

2003 ◽  
Vol 18 (10) ◽  
pp. 2494-2500 ◽  
Author(s):  
R. Brüning ◽  
S. Patterson
Keyword(s):  
Scattering Data ◽  
Scanning Calorimetry ◽  
Crystalline Layer ◽  
X Ray ◽  
Glass Forming ◽  
Heating And Cooling ◽  
X Ray Scattering ◽  
Structure Factors ◽  
Show Evidence ◽  
Ray Scattering

B2–2xO3–2xH2x glasses were prepared by quenching the melt contained in sealed tubes. The glass-forming range extends from x = 0 to 0.50 (equal to the stoichiometry of metaboric acid, HBO2). The glasses were characterized by differential scanning calorimetry and x-ray scattering. With increasing water content, the glass-transition temperature, Tg, decreases from 553 to 333 K. The specific heat of water-rich samples shows an unusual peak just above Tg. The origin of this peak, which is seen upon heating and cooling, has not been identified. Unlike the composition dependence of Tg, the x-ray structure factors depend for the most part linearly on the composition. In analogy with the crystalline layer compounds α-HBO2 and B(OH)3, the x-ray scattering data show evidence for layering in the medium-range order of water-rich glasses.

X-ray Intermolecular Structure Factor(XISF): separation of intra- and intermolecular interactions from total X-ray scattering data

2015 ◽  
Vol 48 (3) ◽  
pp. 950-952 ◽  
Author(s):  
Q. Mou ◽  
C. J. Benmore ◽  
J. L. Yarger
Keyword(s):  
Trust Region ◽  
Amorphous Solids ◽  
Scattering Data ◽  
Complex Molecules ◽  
X Ray ◽  
X Ray Scattering ◽  
Matlab Program ◽  
Structure Factors ◽  
Optimization Routine ◽  
Ray Scattering

XISFis a MATLAB program developed to separate intermolecular structure factors from total X-ray scattering structure factors for molecular liquids and amorphous solids. The program is built on a trust-region-reflective optimization routine with the r.m.s. deviations of atoms physically constrained.XISFhas been optimized for performance and can separate intermolecular structure factors of complex molecules.

New methods of processing of anomalous X-ray scattering data for amorphous alloys I. Determination of partial structure factors

1990 ◽  
Vol 119 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Yu.A. Babanov ◽  
R.Sh. Sadykova ◽  
V.R. Shvetsov ◽  
A.V. Serikov ◽  
A.L. Ageev ◽  
...  
Keyword(s):  
Amorphous Alloys ◽  
Scattering Data ◽  
Partial Structure ◽  
X Ray ◽  
New Methods ◽  
X Ray Scattering ◽  
Structure Factors ◽  
Ray Scattering

scholarly journals Global Small-Angle X-ray Scattering Data Analysis of Triacylglycerols in the Molten State (Part I)

2018 ◽  
Vol 122 (45) ◽  
pp. 10320-10329 ◽  
Author(s):  
Amin Sadeghpour ◽  
Marjorie Ladd Parada ◽  
Josélio Vieira ◽  
Megan Povey ◽  
Michael Rappolt
Keyword(s):  
Data Analysis ◽  
Small Angle ◽  
Scattering Data ◽  
Molten State ◽  
X Ray ◽  
X Ray Scattering ◽  
Ray Scattering

Reconstruction of viruses from solution x-ray scattering data

1995 ◽  
Author(s):  
Yibin Zheng ◽  
Peter C. Doerschuk ◽  
John E. Johnson
Keyword(s):  
Scattering Data ◽  
X Ray ◽  
X Ray Scattering ◽  
Ray Scattering

scholarly journals REGALS: a general method to deconvolve X-ray scattering data from evolving mixtures

2020 ◽  
Author(s):  
Steve P. Meisburger ◽  
Da Xu ◽  
Nozomi Ando
Keyword(s):  
A Priori ◽  
Scattering Data ◽  
Biological Macromolecules ◽  
Time Resolved ◽  
X Ray ◽  
X Ray Scattering ◽  
Open Source Software Package ◽  
Saxs Analysis ◽  
Structural Methods ◽  
Ray Scattering

AbstractMixtures of biological macromolecules are inherently difficult to study using structural methods, as increasing complexity presents new challenges for data analysis. Recently, there has been growing interest in studying evolving mixtures using small-angle X-ray scattering (SAXS) in conjunction with time-resolved, high-throughput, or chromatography-coupled setups. Deconvolution and interpretation of the resulting datasets, however, are nontrivial when neither the scattering components nor the way in which they evolve are known a priori. To address this issue, we introduce the REGALS method (REGularized Alternating Least Squares), which incorporates simple expectations about the data as prior knowledge and utilizes parameterization and regularization to provide robust deconvolution solutions. The restraints used by REGALS are general properties such as smoothness of profiles and maximum dimensions of species, which makes it well-suited for exploring datasets with unknown species. Here we apply REGALS to analyze experimental data from four types of SAXS experiment: anion-exchange (AEX) coupled SAXS, ligand titration, time-resolved mixing, and time-resolved temperature jump. Based on its performance with these challenging datasets, we anticipate that REGALS will be a valuable addition to the SAXS analysis toolkit and enable new experiments. The software is implemented in both MATLAB and python and is available freely as an open-source software package.

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