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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.00581 |
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| _version_ | 1866913181414195200 |
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| author | Niknam, Moe Bouchard, Louis-S. |
| author_facet | Niknam, Moe Bouchard, Louis-S. |
| contents | The dynamics of viscoelastic fluids are governed by a memory function, essential yet challenging to compute, especially when diffusion faces boundary restrictions. We propose a computational method that captures memory effects by analyzing the time-correlation function of the pressure tensor, a viscosity indicator, through the Stokes-Einstein equation's analytic continuation into the Laplace domain. We integrate this equation with molecular dynamics (MD) simulations to derive necessary parameters. Our approach computes NMR lineshapes using a generalized diffusion coefficient, accounting for temperature and confinement geometry. This method directly links the memory function with thermal transport parameters, facilitating accurate NMR signal computation for non-Markovian fluids in confined geometries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_00581 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Nuclear Induction Lineshape: Non-Markovian Diffusion with Boundaries Niknam, Moe Bouchard, Louis-S. Chemical Physics Mathematical Physics The dynamics of viscoelastic fluids are governed by a memory function, essential yet challenging to compute, especially when diffusion faces boundary restrictions. We propose a computational method that captures memory effects by analyzing the time-correlation function of the pressure tensor, a viscosity indicator, through the Stokes-Einstein equation's analytic continuation into the Laplace domain. We integrate this equation with molecular dynamics (MD) simulations to derive necessary parameters. Our approach computes NMR lineshapes using a generalized diffusion coefficient, accounting for temperature and confinement geometry. This method directly links the memory function with thermal transport parameters, facilitating accurate NMR signal computation for non-Markovian fluids in confined geometries. |
| title | Nuclear Induction Lineshape: Non-Markovian Diffusion with Boundaries |
| topic | Chemical Physics Mathematical Physics |
| url | https://arxiv.org/abs/2310.00581 |