RMCSANS—modelling the inter-particle term of small angle scattering data via the reverse Monte Carlo method

2010 ◽  
Vol 22 (40) ◽  
pp. 404216 ◽  
Author(s):  
O Gereben ◽  
L Pusztai ◽  
R L McGreevy
2013 ◽  
Vol 46 (2) ◽  
pp. 365-371 ◽  
Author(s):  
Brian R. Pauw ◽  
Jan Skov Pedersen ◽  
Samuel Tardif ◽  
Masaki Takata ◽  
Bo B. Iversen

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.


2010 ◽  
Vol 34 (3) ◽  
pp. 158-164 ◽  
Author(s):  
Christian Meesters ◽  
Bruno Pairet ◽  
Anja Rabenhorst ◽  
Heinz Decker ◽  
Elmar Jaenicke

Soft Matter ◽  
2019 ◽  
Vol 15 (36) ◽  
pp. 7237-7249
Author(s):  
Katsumi Haita

A particle-mesh-based two-dimensional pattern reverse Monte Carlo (RMC) analysis method (PM-2DpRMC) is proposed for analyzing two-dimensional small-angle-scattering (2D-SAS) patterns. The validities of this PM-2DpRMC method were confirmed.


2000 ◽  
Vol 78 (6) ◽  
pp. 3240-3251 ◽  
Author(s):  
Paolo Mariani ◽  
Flavio Carsughi ◽  
Francesco Spinozzi ◽  
Sandro Romanzetti ◽  
Gerd Meier ◽  
...  

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