Diffusion Fundamentals (E-Journal)
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Traveling Wave Magnetic Particle Imaging for determining the iron-distribution in rock
Get PDFDetermining the composition of solid materials is of high interest in areas such as material research or quality assurance. There are several modalities at disposal with which various parameters of the material can be observed, but of those only magnetic resonance imaging (MRI) or computer tomography (CT) offer anon-destructive determination of material distribution in 3D. A novel non-destructive imaging method is Magnetic Particle Imaging (MPI), which uses dynamic magnetic fields for a direct determination of the distribution of magnetic materials in 3D. With this approach, it is possible to determine and differentiate magnetic and non-magnetic behaviour. In this paper, the first proof-of-principle measurements of magnetic properties in solid environments are presented using a home-built traveling wave magnetic particle imaging scanner
Lithium Ion Transport in Polymer Electrolyte Films for Solid State Batteries – An Overview on Concepts, Techniques and Results
Get PDFFrom Ultrafast to Extremely Slow Li Ion Dynamics in (Nano-)crystalline Solids ― Dimensionality Effects and Structural Disorder
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Rock-typing of laminated sandstones by nuclear magnetic resonance in the presence of diffusion coupling
Get PDFIn this work, the aim is to assess the relative importance of the impact of diffusional coupling on NMR measurements of saturated laminated sandstone numerically at the layer scale to assess the feasibility of NMR rock-typing approaches. We use two 3D model structures based on a Boolean particle process, providing a range of structural to diffusion length ratios to explore the relationships between pore geometry, surface magnetic properties, and NMR transverse relaxation time. The influence of surface relaxivity and bulk susceptibility contrast on T2 relaxation responses is tested for layered structures to improve the rock-typing methodology. An escalation in pore coupling is observed with decreasing bed thickness as well as decreasing bulk susceptibility contrast and surface relaxivity the latter ones reducing the time available for pore coupling by reducing the effective relaxation rate. When pore coupling is strong, the T2 distribution clearly misrepresents the underlying bimodal distribution of the different morphologies. Consequently, the bimodal relaxation time becomes merged and the relative amplitude of the peaks fails to reflect the true morphologies of the models. Furthermore, we observed that in low noise conditions of numerical simulation the effect of diffusional coupling on transverse relaxation may be misinterpreted for the regularization effect on ILT solution. In such cases, careful selection of Laplace inversion method is essential for effective rock-typing by NMR
Magnetic resonance relaxation measurements using open-geometry sensors to assess the clog state of constructed wetlands
Get PDFMonitoring the T1 relaxation of wetland clog matter has previously been identified as a gauge of its clogged state [1]. Magnetic resonance (MR) sensors explored in other work have typically been of a bore-whole configuration, which may not be ideal in a wetland environment where the sensitive volume of the sensor may become physically clogged and therefore inoperable. This work investigates two open-geometry sensor designs and a short study is presented to determine the suitability of the sensors for monitoring the clog state of wetlands. It was shown that a bar magnet geometry has a higher stray field than that of the four magnet surface sensor also presented, leading to a prohibitively short T2 eff. This means that the T1 values collected are notably shorter and not useful for distinguishing between clog state for the single magnet sensor. By contrast the four magnet surface sensor has a longer T2 eff, making it more suitable for T1 measurements; where T1= 915 ± 212 ms for a very thinly clogged sample, and T1= 127 ± 27 ms for a heavily clogged sample. This offers a clearly resolvable difference in the T1 values allowing the clogging state to be easily determined and making this sensor the desirable choice for long-term embedding