Vol 49, No 10 (2018)

A nuclear magnetic resonance (NMR) based experimental procedure to determine the dynamic viscosity (η) in blood plasma solutions is presented. An equation relating η and the transverse proton magnetic relaxation rate (\(1/T_{2}\)) is obtained after considering plasma an extremely diluted water solution of albumin with no long range hydrodynamics interactions among macromolecules, and a fast exchange of water molecules between the free and associated water. Carr–Purcell–Meiboom–Gill pulse sequence was used to measure the transverse proton magnetic relaxation time (T₂) in a magnetic resonance console coupled to one homogeneous magnetic system (0.095 T). A η value of 1.68 ± 0.08 mPa s was obtained in 20 control samples, which statistically matched the value obtained in the same samples using an Ostwald viscometer (1.61 ± 0.04 mPa s). η was determined in 172 patients with multiple myeloma (2.47 ± 0.15 mPa s) and 72 with sickle cell disease (2.45 ± 0.24 mPa s) showing a statistically significant increase over the control individuals. The results show the utility of this NMR method to estimate dynamic viscosity in plasma for medical purposes, and a comparison with other methods is done.

Nano-self-assembly γ-Al₂O₃ (NSAA) is a catalytic material and can be used as a physical model of micro–nano-porous material for understanding shale and tight formations. Herein, we describe the development of an in situ, fast, and quantitative method for the potential nuclear magnetic resonance (NMR) characterization of pore system in a nano-self-assembly precursor (NSAP) for the first time. The surface relaxivity ρ₂ of small pores, which are water saturated is found to be 0.65 and 0.85 μm μs⁻¹, while ρ₂ for large pores, which are hydrocarbon saturated is 0.55 μm μs⁻¹. This points to the interaction of water molecules with inorganic pore surface being stronger than that of hydrocarbon molecules. A new method for reconstructing the capillary pressure curve (Pc) is used to comprehensively reflect the pore structure of NSAA and predict NMR Pc. The pore network model of NSAA is proposed based on NMR quantitative characterization and SEM evaluation, and it has pore channels, which are suitable for macromolecule diffusion. A set of low-field NMR methods for evaluating the pore connectivity of nanomaterials are established. The experiments demonstrate that low-field NMR is a robust tool for characterizing pore channels for macromolecule transport in catalytic materials, with an important application field being shale oil and tight sand oil development and petroleum refining.

The study of gas flow characteristics in wind instruments, specifically recorders, presents a challenge. Most currently available techniques are invasive, and computational fluid dynamics (CFD) simulations must be relied upon. In this paper, a magnetic resonance imaging (MRI) technique is presented that is non-invasive and non-destructive, and offers quantitative results that can be used to better understand gas flow in a recorder. In particular, 3-D MRI measurements of the velocity field in a real playing recorder are presented. A divergence map and maps of the correlation coefficient (\(R^2\)) are presented alongside the velocity maps as a tools for validation. A log ratio of signal amplitudes is also mapped to indicate regions with larger turbulent fluctuations. This methodology affords a useful tool for the validation of CFD simulations for the study of wind instruments.

Two-dimensional low-field hydrogen nuclear magnetic resonance (2D ¹H LF-NMR) analysis of chemical compounds measures T₁ and T₂ relaxation times observed as exponential decay curves. Once relaxation curves are measured and stored in the format of discrete digital signals, they must be transformed, mathematically, into spectra that can be read and interpreted. We used primal–dual interior method for convex objectives (PDCO) that provided more accurate reconstructions than the standard algorithms. It is the objective of this paper to estimate the most suitable PDCO parameterization that provide accurate and robust reconstructions of relaxation curves into 2D spectra for LF-NMR under different signal-to-noise ratios. Finding optimal regularization parameters is an active field of mathematical research. PDCO, however, by making use of two regularization parameters instead of a single one, presents a much harder task, where the consolidated search criteria of a single parameter cannot be extended to two parameters. We featured a method based on numerical experiments and simulations that identified optimal and unique model coefficients that maximize PDCO reconstruction accuracy under different signal-to-noise conditions. The coefficients, as determined for artificial signals, increase the confidence of accurate reconstruction for 2D LF-NMR analysis on real lab samples.