2019
DOI: 10.1039/c9cp04379a
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NMR relaxation and modelling study of the dynamics of SF6 and Xe in porous organic cages

Abstract: The dynamics of gas in CC3 porous solid is explored with NMR diffusion and relaxation experiments and interpreted with molecular level modeling.

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Cited by 16 publications
(23 citation statements)
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“…The same group further studied the relaxation parameters of both xenon and sulfur hexafluoride SF 6 , a potent greenhouse gas, by combining the developed DFT models with experiments. [ 122 ] They elucidated T 1 and T 2 relaxation mechanisms and the different dynamics of the two gases in an organic cage (CC3‐R).…”
Section: Recent Applicationsmentioning
confidence: 99%
“…The same group further studied the relaxation parameters of both xenon and sulfur hexafluoride SF 6 , a potent greenhouse gas, by combining the developed DFT models with experiments. [ 122 ] They elucidated T 1 and T 2 relaxation mechanisms and the different dynamics of the two gases in an organic cage (CC3‐R).…”
Section: Recent Applicationsmentioning
confidence: 99%
“…The ideas are implemented in Matlab/octave code 46 and the steps given in flow-chart of parameter estimation (see Core of the MCMC is efficient sampling of configurations (c). For ease of introducing alternative prior distributions the current version, function MCMCstep.m, 46 follow Metropolis algorithm, 7,47,48 hence, the proposed new parameter configurations are sampled at random direction (not for instance guided by likelihood function). Each parameter is given a move individually, following Gibbs sampling algorithm.…”
Section: Mcmc Parameter Estimationmentioning
confidence: 99%
“…However, provided are a set of tools to monitor convergence and reproducibly and from a moderate computational investment it is clear how challenging (or easy) problem lies ahead. In particular with the option to follow annealing the experience so far is that MCMC can do well in parametrization of NMR-relaxation, relaxometry and line shape studies, 6,7,53,55 and should play an important role in Bayesian evidence calculation.…”
Section: Bayes Evidencementioning
confidence: 99%
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