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NMR signals within the generalized Langevin model for fractional Brownian motion

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  • Lisý, Vladimír
  • Tóthová, Jana

Abstract

The methods of Nuclear Magnetic Resonance belong to the best developed and often used tools for studying random motion of particles in different systems, including soft biological tissues. In the long-time limit the current mathematical description of the experiments allows proper interpretation of measurements of normal and anomalous diffusion. The shorter-time dynamics is however correctly considered only in a few works that do not go beyond the standard memoryless Langevin description of the Brownian motion (BM). In the present work, the attenuation function S (t) for an ensemble of spin-bearing particles in a magnetic-field gradient, expressed in a form applicable for any kind of stationary stochastic dynamics of spins with or without a memory, is calculated in the frame of the model of fractional BM. The solution of the model for particles trapped in a harmonic potential is obtained in an exceedingly simple way and used for the calculation of S (t). In the limit of free particles coupled to a fractal heat bath, the results compare favorably with experiments acquired in human neuronal tissues. The effect of the trap is demonstrated by introducing a simple model for the generalized diffusion coefficient of the particle.

Suggested Citation

  • Lisý, Vladimír & Tóthová, Jana, 2018. "NMR signals within the generalized Langevin model for fractional Brownian motion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 200-208.
  • Handle: RePEc:eee:phsmap:v:494:y:2018:i:c:p:200-208
    DOI: 10.1016/j.physa.2017.12.042
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    Cited by:

    1. Pereira-Alves, Felipe & Soares-Pinto, Diogo O. & Paiva, Fernando F., 2024. "NMR diffusion in restricted environment approached by a fractional Langevin model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
    2. Sikora, Grzegorz, 2018. "Statistical test for fractional Brownian motion based on detrending moving average algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 54-62.

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