What is it about?

This study introduces a new method that harnesses the principles of MRI at a smaller scale to reveal extremely small details inside materials. Instead of relying on strong magnetic fields, we use the natural movement of molecules—diffusion dynamics—to capture the sample's geometric features. By carefully analyzing the signal shapes and comparing them with computer simulations of molecular behavior, we can uncover the structure of complex structures. This approach helps us understand how substances travel through tiny spaces, such as the small tubes found in materials, which could lead to better filters, improved drug delivery systems, and more efficient materials in various industries.

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Why is it important?

This work is important because it introduces a novel approach to probing the microstructure of materials using weak magnetic fields and diffusion dynamics. Traditional MRI techniques are limited by the strength of available magnetic fields, which constrains the resolution and level of detail that can be captured. In contrast, our method is only limited by the capabilities of modern simulation programs, which can model tens of thousands of particles within complex microstructures or mesoporous materials. Furthermore, our analysis reveals memory kernels—key functions that encapsulate the system's dynamic history. These memory kernels enable the evaluation of correlation functions and time-series parameters, which are essential for accurately determining transport properties in viscoelastic fluids and other complex systems. Additionally, the shape of the acquired signal, often collected but not analyzed in conventional experiments, is fully utilized in our method. This means that significant improvements in material characterization can be achieved without increasing experimental time, cost, or complexity. Together, these advancements offer a timely and innovative solution with the potential to impact the development of better filters, optimized drug delivery systems, and more efficient materials across various industries.

Perspectives

Diffusion in non-Markovian particles and viscoelastic fluids, especially within complex geometries, is a notoriously difficult problem because conventional analytical solutions cannot fully capture the intricate dynamics and memory effects. In our work, we address this challenge by simulating the dynamics at a femtosecond time scale and extracting the necessary parameters. We then employ a generalized Langevin framework to extend these parameters to the millisecond scale—matching the time frame of experimental observations. This strategy effectively bridges the gap between ultra-fast simulations and real-world experiments, offering a new way to understand and quantify memory kernels. Ultimately, our approach not only deepens the fundamental understanding of transport phenomena in complex systems but also holds the potential to significantly improve existing imaging techniques and structural studies.

Moe Niknam
University of California Los Angeles

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This page is a summary of: Microstructural geometry revealed by NMR line shape analysis, The Journal of Chemical Physics, February 2025, American Institute of Physics,
DOI: 10.1063/5.0245237.
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