AI Article Synopsis
- Monte Carlo methods utilize random updates and trial-and-error to analyze particle size distributions from small-angle scattering, particularly in low-concentration solutions or metals.
- Recent enhancements to these methods include a clear convergence criterion, adjustments for better visibility matching in scattering data, and techniques for estimating visibility thresholds and uncertainties in size distributions.
- These improvements aim to enhance the accuracy and reliability of particle size analysis.
Article Abstract
Monte Carlo (MC) methods, based on random updates and the trial-and-error principle, are well suited to retrieve form-free particle size distributions from small-angle scattering patterns of non-interacting low-concentration scatterers such as particles in solution or precipitates in metals. Improvements are presented to existing MC methods, such as a non-ambiguous convergence criterion, nonlinear scaling of contributions to match their observability in a scattering measurement, and a method for estimating the minimum visibility threshold and uncertainties on the resulting size distributions.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3627408 | PMC |
| http://dx.doi.org/10.1107/S0021889813001295 | DOI Listing |
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