We have investigated model systems in which effects from non-Gaussian restricted diffusion could be separated from effects caused by multiple diffusion coefficients. We applied various models to analyze the experimental data. An analysis based on multi-exponential models does not account correctly for effects caused by restricted diffusion in a system with multiple compartments. However, separating the components due to differences in dynamic behavior prior to the diffusion analysis, combined with a diffusion analysis based on the second cumulant approximation, was more robust, and was able to handle effects from restricted diffusion in the presence of multi-component diffusion.

In this study we have applied 2D NMR experiments where the spatial inhomogeneous magnetic field (Bi) inside a porous sample is correlated to respectively internal gradient (G0), diffusion coefficient (D), and transverse relaxation time (T2) of a confined liquid. Experiments were performed on samples having different pore system geometry and heterogeneity, leading to different types of confinement of the liquid. The results show that the correlation between G0 and Bi is more sensitive to the type of confinement, and thus also of the pore geometry and heterogeneity, compared to the corresponding correlations involving D and T2.

The two most successful methods of estimating the distribution of NMR relaxation times from two dimensional data are firstly a data compression stage followed by application of the Butler-Reeds-Dawson (BRD) algorithm, and secondly a primal dual interior point method using a preconditioned conjugate gradient (PCG). Both of these methods have been presented in the literature as requiring a truncated singular value decomposition of matrices representing the exponential kernels. Other matrix factorisations are applicable to each of these algorithms, and which demonstrate the different fundamental principles behind the operation of the algorithms. In the case of the data compression approach the most appropriate matrix decomposition specifically designed for this task is the rank-revealing QR (RRQR) factorisation. In the case of the interior point method, the most appropriate method is the LDL factorisation with diagonal pivoting, also known as the Bunch-Kaufman-Parlett factorisation. The details of these differences are discussed, and the performances of the algorithms are compared numerically.

Determining 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.

Chemical reaction speed is frequently enhanced at a surface, particularly when materials like platinum are present. It is well known that porous materials such as sol-gel silicas, controlled pore glasses, templated porous materials such as SBA-15, MCM-41, MCM-48, and zeolites, offer large surface areas. This in turn makes them ideal for catalysing chemical reactions. Thus an important use for porous materials is as a substrate and media to promote chemical reactions. However small pores are not always easily accessed by some of the organic liquids in which these catalytic reactions ideally take place. Cryoporometric techniques offer the possibility of directly probing the fraction of a pore that is actually accessible to a probe liquid. This fractional volume has significant impact on the catalytic efficacy of a particular solvent that is used to promote a reaction in the pores. Pore size, pore geometry, pore throat, pore surface material (hydrophilic/hydrophobic) and choice of probe liquid all influence the accessible fraction. By performing an NMR cryoporometric measurement using a particular liquid of interest, it is possible to directly access this information, which is of prime importance for catalysis, and financially very significant on an industrial scale. Results are reported here for a set of liquids, some simple alkanes (dodecane, tetradecane and hexadecane) plus water and cyclohexane, accessing pores in sol-gel silicas of nominal pore diameters 60Å, 100Å, 200Å, 500 Å. The key conclusions were that for the alkanes, the dimension of chain length was not relevantto the filling fraction, however for the cyclohexane a molecular diameter of 3.8 Å fitted the data well.

Compressed sensing technique is a recent framework for signal sampling and recovery. It allows signal acquisition with less sampling than required by Nyquist-Shannon theorem and reduces data acquisition time in MRI. When the sampling rate is low, prior knowledge is essential to reconstruct the missing features. In this paper, a different reconstruction method is proposed by using the principal component analysis based on pattern recognition. The experiments demonstrate that this method can reduce aliasing artefacts and achieve a high peak signal-to-noise ratio compared to a compressed sensing reconstruction.

In this paper bulk diffusion techniques in case of metals and alloys will be reviewed with a special emphasis on resistometric technique. The experiment is restricted only to those metals, which can be made in the form of wire but has the advantage of applying to materials whose suitable radioactive isotopes are not available. A brief introduction along with different diffusion mechanisms with corresponding activation energies, diffusion coefficients, correlation factors, isotope effect, enhancement factor, etc. is also incorporated. Three models for bulk diffusion are also mentioned.

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