2013
DOI: 10.1107/s0021889813001295
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Improvements and considerations for size distribution retrieval from small-angle scattering data by Monte Carlo methods

Abstract: A method is presented and applied, capable of retrieving form-free particle size distributions complete with uncertainties from small-angle scattering patterns. Special attention is paid to particle observability in the scattering patterns, accurate estimation of data uncertainty and the effect of uncertainty on the resulting size distribution statistics.

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Cited by 97 publications
(126 citation statements)
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“…Uncertainties allow for the weighting of the data to their uncertainty, so that accurate datapoints weigh more heavily in least-squares optimization than datapoints with large uncertainties. Furthermore, the provision of uncertainties allows for determination of a goodness of fit value which indicates whether or not the model fits the data (on average) to within the uncertainty of the data [137,133]. A lack of uncertainties does not allow any further evaluation of a model than an estimation by eye, whose analysis capabilities are easily swayed by the choice of axes and datapoint size.…”
Section: What's Next? a Few Words On Data Fittingmentioning
confidence: 99%
“…Uncertainties allow for the weighting of the data to their uncertainty, so that accurate datapoints weigh more heavily in least-squares optimization than datapoints with large uncertainties. Furthermore, the provision of uncertainties allows for determination of a goodness of fit value which indicates whether or not the model fits the data (on average) to within the uncertainty of the data [137,133]. A lack of uncertainties does not allow any further evaluation of a model than an estimation by eye, whose analysis capabilities are easily swayed by the choice of axes and datapoint size.…”
Section: What's Next? a Few Words On Data Fittingmentioning
confidence: 99%
“…We found that the simplest model for interpretation of all data was that of spherical particle morphology. Their size distribution was retrieved by employing a Monte Carlo SAXS data evaluation procedure recently published by Pauw et al, (2013). The main advantage of this procedure is that no size distribution type such as Gaussian, Schultz-Zimm, etc., needs to be assumed a priori.…”
Section: Particle Characterizationmentioning
confidence: 99%
“…2 ), 32 which most SANS fitting programs provide. This is particularly applicable to SANS on complicated systems for the aforementioned reasons.…”
mentioning
confidence: 99%