Nuclear Magnetic Resonance Relaxation Data Research Articles

Range and sensitivity of 17O nuclear spin-lattice relaxation as a probe of aqueous electrolyte dynamics.

The study of electrolytic solutions is of relevance in many research fields, ranging from biophysics, materials, and colloid science to catalysis and electrochemistry. The dependence of solution dynamics on the nature of electrolytes and their concentrations has been the subject of many experimental and computational studies, yet it remains challenging to obtain a full understanding of the factors that govern solution behavior. Here, we provide additional insights into the behavior of aqueous solutions of alkali chlorides by combining 17O relaxation data with diffusion and viscosity data and contrast their behavior with 1H nuclear magnetic resonance relaxation data. The main findings are that 17O relaxation correlates well with viscosity data but not with diffusion data, while 1H relaxation correlates with neither. Certain ionic trends match known ion-specific series behavior, especially at high concentrations. Notably, we also examine the ranges of the interactions and conclude that the majority of the effects are tied to local water reorientation dynamics.

DNA base order parameter determination without influence of chemical exchange.

Nuclear magnetic resonance (NMR) spectroscopy is a versatile tool used to investigate the dynamic properties of biological macromolecules and their complexes. NMR relaxation data can provide order parameters S2, which represent the mobility of bond vectors reorienting within a molecular frame. Determination of S2 parameters typically involves the use of transverse NMR relaxation rates. However, the accuracy in S2 determination can be diminished by elevation of the transverse relaxation rates through conformational or chemical exchange involving protonation/deprotonation or non-Watson-Crick base-pair states of nucleic acids. Here, we propose an approach for determination of S2 parameters without the influence of exchange processes. This approach utilizes transverse and longitudinal 13C chemical shift anisotropy (CSA) - dipole-dipole (DD) cross-correlation rates instead of 13C transverse relaxation rates. Anisotropy in rotational diffusion is taken into consideration. An application of this approach to nucleotide base CH groups of a uniformly 13C/15N-labeled DNA duplex is demonstrated.

Monitoring cheese ripening by single-sided nuclear magnetic resonance

The noninvasive, longitudinal study of products and food processing is of interest for the dairy industry. Here, we demonstrated that single-sided nuclear magnetic resonance (NMR) can be used for noninvasive monitoring of the cheese ripening process. The maturation of soft-ripened Camembert-like molded cheese samples was monitored for 20 d measuring 1-dimensional and 2-dimensional NMR relaxation and diffusion data at various depths, ranging from the hard surface layer to the soft center. Gelation and gel shrinkage were observed throughout ripening, and a complete loss of free water signal was observed at the cheese rind. Transversal (T2) relaxation distributions include 3 components that evolve with ripening time and position, corresponding to water inside the casein gel network, water trapped in casein, and fat. Two-dimensional T1-T2 relaxation experiments provided enhanced resolution of the 3 components, allowing quantification of the relative proportions of each phase. Furthermore, diffusion (D)-T2 relaxation correlation experiments revealed the bimodal size distribution of fat globules. The study demonstrated that single-sided NMR can provide spatially resolved signal intensity, relaxation, and diffusion parameters that reflect structural changes during the ripening process and can be exploited to understand and monitor the ripening of cheeses.

Investigation of the effects of high hydrostatic pressure on the functional properties of pea protein isolate

AbstractThis study investigates the effects of high hydrostatic pressure (HHP) on some functional properties of pea protein isolate (PPI). HHP was combined with various temperature and pH conditions to investigate the combined effects of HHP‐based food processing conditions on the functional properties of PPI. Herein, PPI solutions prepared at different pH conditions (3.0, 5.0, and 7.0) were subjected to 300, 400, and 500 MPa HHP treatment at 25 and 50°C for 5 min. Water‐holding capacity (WHC), solubility, and emulsification activity of PPI samples were determined. Additionally, nuclear magnetic resonance (NMR) relaxometry and Fourier transform infrared spectroscopy experiments were performed for further analysis. Maximum WHC (p < 0.05) was observed for the samples treated at 500 MPa‐pH 5.0‐50°C whereas maximum solubility (p < 0.05) belonged to the samples subjected to 300 MPa‐pH 7.0‐50°C treatment conditions. Better emulsification activity was achieved at pH 3.0 regardless of the pressure level applied. The novelty of this study is that NMR relaxometry was introduced as a fast/nondestructive technique to investigate the changes in the functional properties of PPI samples and one of the functional parameters was correlated with NMR relaxation data. Herewith, the longest transverse relaxation time (T2) (p < 0.05) belonged to the samples with maximum WHC. The results showed that HHP is able to modify the functional properties of PPI at specific temperature‐pH combinations, and NMR relaxometry technique has a high potential for such studies.Practical ApplicationsHigh hydrostatic pressure (HHP) is an emerging technology which is used for its diverse range of applications in food science and technology. Modification of physicochemical and functional properties of food ingredients is one of the latest applications of HHP treatment. This study demonstrated that HHP treatment was able to modify some functional properties of pea protein isolate (PPI) samples such as water‐holding capacity (WHC), solubility, and emulsification activity. In addition to pressure level, pH and temperature were also effective on modifying the functional properties of PPI samples. For instance, a high pressure (500 MPa) was required to improve WHC whereas lower pressures (300 MPa) improved the solubility of the samples at high pH and temperature. The results of this study could be used in model HHP studies to improve some functional properties of PPI for different purposes.

A study on multi-exponential inversion of nuclear magnetic resonance relaxation data using deep learning

Nuclear magnetic resonance (NMR) is a powerful tool for formation evaluation in the oil industry to determine parameters, such as pore structure, fluid saturation, and permeability of porous materials, which are critical to reservoir engineering. The inversion of the measured relaxation data is an ill-posed problem and may lead to deviations of inversion results, which may degrade the accuracy of further data analysis and evaluation. This paper proposes a deep learning method for multi-exponential inversion of NMR relaxation data to improve accuracy. Simulated NMR data are first constructed using a priori knowledge based on the signal parameters and Gaussian distribution. These data are then used to train the neural network designed to consider noise characteristics, signal decay characteristics, signal energy variations, and non-negative features of the T2 spectra. With the validation from simulated data, the models introduced by multi-scale convolutional neural network (CNN) and attention mechanism outperform other approaches in terms of denoising and T2 inversion. Finally, NMR measurements of rock cores are used to compare the effectiveness of the attention multi-scale convolutional neural network (ATT-CNN) model in practical applications. The results demonstrate that the proposed method based on deep learning has better performance than the regularization method.

Explicit models of motions to analyze NMR relaxation data in proteins.

Nuclear Magnetic Resonance (NMR) is a tool of choice to characterize molecular motions. In biological macromolecules, pico- to nanosecond motions, in particular, can be probed by nuclear spin relaxation rates, which depend on the time fluctuations of the orientations of spin interaction frames. For the past 40 years, relaxation rates have been successfully analyzed using the Model-Free (MF) approach, which makes no assumption on the nature of motions and reports on the effective amplitude and timescale of the motions. However, obtaining a mechanistic picture of motions from this type of analysis is difficult at best, unless complemented with molecular dynamics (MD) simulations. In spite of their limited accuracy, such simulations can be used to obtain the information necessary to build explicit models of motions designed to analyze NMR relaxation data. Here, we present how to build such models, suited in particular to describe motions of methyl-bearing protein side chains and compare them with the MF approach. We show on synthetic data that explicit models of motions are more robust in the presence of rotamer jumps which dominate the relaxation in methyl groups of protein side chains. We expect this work to motivate the use of explicit models of motion to analyze MD and NMR data.

Fully gapped superconducting state in interstitial-carbon-doped Zr5Pt3

We report a comprehensive study of the ${\mathrm{Zr}}_{5}{\mathrm{Pt}}_{3}{\mathrm{C}}_{x}$ superconductors, with interstitial carbon between 0 and 0.3. At a macroscopic level, their superconductivity, with ${T}_{c}$ ranging from 4.5 to 6.3 K, was investigated via electrical-resistivity, magnetic-susceptibility, and specific-heat measurements. The upper critical fields ${\ensuremath{\mu}}_{0}{H}_{\mathrm{c}2}\ensuremath{\sim}7$ T were determined mostly from measurements of the electrical resistivity in applied magnetic field. The microscopic electronic properties were investigated by means of muon-spin rotation and relaxation ($\ensuremath{\mu}\mathrm{SR}$) and nuclear magnetic resonance (NMR) techniques. In the normal state, NMR relaxation data indicate an almost ideal metallic behavior, confirmed by band-structure calculations, which suggest a relatively high electronic density of states at the Fermi level, dominated by the Zr $4d$ orbitals. The low-temperature superfluid density, obtained via transverse-field $\ensuremath{\mu}\mathrm{SR}$, suggests a fully gapped superconducting state in ${\mathrm{Zr}}_{5}{\mathrm{Pt}}_{3}$ and ${\mathrm{Zr}}_{5}{\mathrm{Pt}}_{3}{\mathrm{C}}_{0.3}$, with zero-temperature gap ${\mathrm{\ensuremath{\Delta}}}_{0}=1.20$ and 0.60 meV and magnetic penetration depth ${\ensuremath{\lambda}}_{0}$ = 333 and 493 nm, respectively. The exponential dependence of the NMR relaxation rates below ${T}_{c}$ further supports nodeless superconductivity. The absence of spontaneous magnetic fields below the onset of superconductivity, as determined from zero-field $\ensuremath{\mu}\mathrm{SR}$ measurements, confirms the preserved time-reversal symmetry in the superconducting state of ${\mathrm{Zr}}_{5}{\mathrm{Pt}}_{3}{\mathrm{C}}_{x}$. In contrast to a previous study, our $\ensuremath{\mu}\mathrm{SR}$ and NMR results suggest conventional superconductivity in the ${\mathrm{Zr}}_{5}{\mathrm{Pt}}_{3}{\mathrm{C}}_{x}$ family, independent of the C content.

Liquid carbon dioxide drying and subsequent combustion behavior of high-moisture coal at high pressure

• LCO 2 drying increases combustion rate by 37% for coal with 20% water at 500 °C. • The faster combustion of LCO 2 –coal slurry produces 35% higher particle temperature. • Both NMR and weight loss data support LCO 2 drying mechanism in high moisture coal. • The high pressure boiler with LCO 2 drying cycle increases power output by 11.3%. In this work, the combustion and radiation characteristics of high-moisture coal treated with liquid carbon dioxide (LCO 2 ) at high pressure were examined. The coal conversion and gas yield were measured to determine the combustion rate constants at four different temperatures. The particle temperature and radiative heat flux emitted from the burning coal bed were measured using a two-color pyrometer and radiometer, respectively. The changes in the moisture content of the coal microstructure after LCO 2 treatment were measured using nuclear magnetic resonance (NMR) and thermogravimetric analysis (TGA). The combustion kinetic results showed faster LCO 2 –coal slurry combustion, which frequently occurred at low temperatures. The particle temperature and radiative heat flux of the LCO 2 –coal slurry increased faster to higher steady-state values than those of raw coal. The influence of LCO 2 on the combustion of high-moisture coal was not observed on graphite devoid of water or volatile content. This indicates an effective exchange between LCO 2 and water in coal micropores when coal is exposed to LCO 2 . In addition to the TGA data, the LCO 2 drying mechanism in high-moisture coal was confirmed using NMR relaxation data. The performance of the LCO 2 drying process was compared with that of the conventional boiler with oven drying unit in terms of the water removal effect, combustion, and radiation behavior. An energy analysis was undertaken to calculate the amount of total energy produced from three power cycles occurring in a high-pressure boiler, including one combining the LCO 2 drying cycle with the steam power cycle.

Improved Description of Organic Matter in Shales by Enhanced Solid Fraction Detection with Low-Field 1H NMR Relaxometry

Nuclear magnetic resonance (NMR) relaxometry is routinely used to characterize the oil fraction in unconventional shale formations with low-field benchtop NMR hardware. However, organic phases with restricted mobility like kerogen and bitumen are typically not detectable with the standard Carr–Purcell–Meiboom–Gill (CPMG) method on such equipment, with the rapid spin–spin (T2) signal decay of these solid/viscous components not visible to the measurement. The solid-echo (SE) and mixed-echo (ME) pulse sequences offer an alternative to the CPMG, extending the lifetime of the time-domain signal and allowing the capture of solid hydrogen-containing species. Accordingly, we combine NMR relaxation data generated from the application of the CPMG, SE, and ME experiments to a set of powdered oil- and brine-saturated Eagle Ford shale samples, with the aim of identifying and quantifing both the immobile (kerogen/bitumen) and mobile (oil/brine) components. Two-dimensional relaxometry correlating spin–lattice relation times T1 to the effective decay times T2 obtained using CPMG and SE techniques provides more complete information on the immobile phases in the shales, such as solid organic matter and clay-bound water. The three spin–echo techniques have similar efficiencies when detecting signal from mobile and low-viscosity fluids, while significant differences are seen in shale samples containing immobile organics. Overall, the combination of the three spin echo techniques provides an improved description of the solid, viscous, and liquid components in the investigated shales at low magnetic field.

Soil hydraulic interpretation of nuclear magnetic resonance measurements based on circular and triangular capillary models

AbstractGeophysical nuclear magnetic resonance (NMR) applications are used to estimate pore size distributions (PSDs) of rocks and sediments. This is commonly realized by empirical calibration using information about the surface‐to‐volume ratio of the material. Recent research has developed joint inversion concepts for NMR relaxation data that provides the PSD with a minimum of information. The application requires the NMR signal of a sample at saturation and at least one at partial saturation and at known suction. The new inversion concept physically simulates the desaturation process as part of the forward operator. The cross‐section of the model capillaries in the underlying bundle can be either circular or triangular. Our study investigates the performance of the NMR joint inversion to predict water retention function (WRF) and capillary‐based hydraulic conductivity (Kcap) as functions of saturation for different sands. The angularity of the pores has no significant impact on the estimated WRF but affects the Kcap estimation significantly. Our study shows that the WRF is predicted reliably for sand samples under fast diffusion conditions. The Kcap estimations are also plausible but tend to systematic overestimation, for which we identified the tortuosity being the main reason. Because NMR relaxation data generally do not provide tortuosity information, a plausible tortuosity model remains an issue of classical calibration. Further development of the approach will thus consider tortuosity measurements (e.g., by electrical resistivity measurements and/or gradient NMR) and will consider the relaxation mechanisms outside fast diffusion conditions to enhance its applicability for coarse soils.

NMR relaxometry of oil paint binders.

Mobile nuclear magnetic resonance (NMR) is a flexible technique for nondestructive characterization of water in plants, the physical properties of polymers, moisture in porous walls, or the binder in paintings by relaxation measurements. NMR relaxation data report material properties and therefore can also help to characterize the state of tangible cultural heritage. In this work, we discuss the relaxation behavior in two series of naturally aged paint mock-up samples. First, paints with different pigment concentrations were prepared and investigated in terms of the longitudinal and transverse relaxation-time distributions. We document the evolution of both relaxation-time distributions during the initial drying stage and demonstrate the heightened importance of transverse over longitudinal relaxation measurements. Second, we observe nonlinear dependences of the relaxation times on the pigment concentration in a typical oil binder. Third, in a study of naturally aged paint samples prepared in the years between 1914 and 1951 and subsequently aged under controlled conditions, we explore the possibility of determining the age of paintings using partial least square regression (PLS) by fitting T1 -T2 data with the sample age. Our results suggest some correlation, albeit with significant scatter. Estimating the age of a painting stored under unknown conditions from NMR relaxation data is therefore not feasible, as the cumulative effects of light irradiation, humidity, and biological degradation further obfuscate the chemical and physical impact of aging on the relaxation times in addition to the impact of pigment concentration.

Predicting NMR relaxation of proteins from molecular dynamics simulations with accurate methyl rotation barriers.

The internal dynamics of proteins occurring on time scales from picoseconds to nanoseconds can be sensitively probed by nuclear magnetic resonance (NMR) spin relaxation experiments, as well as by molecular dynamics (MD) simulations. This complementarity offers unique opportunities, provided that the two methods are compared at a suitable level. Recently, several groups have used MD simulations to compute the spectral density of backbone and side chain molecular motions and to predict NMR relaxation rates from these. Unfortunately, in the case of methyl groups in protein side chains, inaccurate energy barriers to methyl rotation were responsible for a systematic discrepancy in the computed relaxation rates, as demonstrated for the AMBER ff99SB*-ILDN force field (and related parameter sets), impairing quantitative agreement between simulations and experiments. However, correspondence could be regained by emending the MD force field with accurate coupled cluster quantum chemical calculations. Spurred by this positive result, we tested whether this approach could be generally applicable, in spite of the fact that different MD force fields employ different water models. Improved methyl group rotation barriers for the CHARMM36 and AMBER ff15ipq protein force fields were derived, such that the NMR relaxation data obtained from the MD simulations even now display very good agreement with the experiment. Results herein showcase the performance of present-day MD force fields and manifest their refined ability to accurately describe internal protein dynamics.

Effects of gas pressurization on the interpretation of NMR hydrocarbon measurements in organic rich shales

The estimation of total hydrocarbons (HCs) in place is one of the most important economic challenges in unconventional resource plays. Nuclear magnetic resonance (NMR) has proven to be a valuable tool in directly quantifying both hydrocarbons and brines in the laboratory and the field. Some major applications of NMR interpretation include pore body size distributions, wettability, fluid types, and fluid properties. However, for tight formations, the effects of the factors on NMR relaxation data are intertwined. One purpose of this study is to review the interpretation of NMR response of HCs in a tight rock matrix through illustrated examples. When comparing NMR data between downhole wireline and laboratory measurement, three important elements need to be considered: 1) temperature differences, 2) system response differences, and 3) pressure (mainly due to the lost gasses.) The effect of temperature on HCs would be presented with experimental results for bulk fluids. Whereas, the effect of pressure is investigated by injecting gas back into rock matrix saturated with original fluids. The experiments were performed within an NMR transparent Daedalus ZrO2 pressure cell, which operates at pressures up to 10,000 psi. The results show that, at ambient temperature and pressure, NMR responds to a fraction of HCs, which is volatile enough to be observed as an NMR relaxation sequence. The invisible fraction of HCs to NMR sequence at ambient condition can be up to 20% of the total extractable HCs. Molecular relaxation is impacted by fluid viscosity, pore size, and surface affinity. In other words, the fluid with higher viscosity (either due to temperature or gas loss), presenting in smaller pore, or highly affected by the pore surface, will relax faster, and would be partially invisible to NMR, especially in the field. This is critical to the interpretation of NMR response for liquid rich source rocks, in which all of the above molecular relaxing restrictions can be found. Thus, engineers can underestimate movable HCs by using routine core analysis data.

Reducing bias in the analysis of solution-state NMR data with dynamics detectors.

Nuclear magnetic resonance (NMR) is sensitive to dynamics on a wide range of correlation times. Recently, we have shown that analysis of relaxation rates via fitting to a correlation function with a small number of exponential terms could yield a biased characterization of molecular motion in solid-state NMR due to limited sensitivity of experimental data to certain ranges of correlation times. We introduced an alternative approach based on "detectors" in solid-state NMR, for which detector responses characterize motion for a range of correlation times and reduce potential bias resulting from the use of simple models for the motional correlation functions. Here, we show that similar bias can occur in the analysis of solution-state NMR relaxation data. We have thus adapted the detector approach to solution-state NMR, specifically separating overall tumbling motion from internal motions and accounting for contributions of chemical exchange to transverse relaxation. We demonstrate that internal protein motions can be described with detectors when the overall motion and the internal motions are statistically independent. We illustrate the detector analysis on ubiquitin with typical relaxation data sets recorded at a single high magnetic field or at multiple high magnetic fields and compare with results of model-free analysis. We also compare our methodology to LeMaster's method of dynamics analysis.

Accuracy of MD solvent models in RNA structure refinement assessed via liquid-crystal NMR and spin relaxation data

Molecular dynamics (MD) simulations play an important role in characterizing Ribonucleic Acid (RNA) structure, augmenting information from experimental techniques such as Nuclear Magnetic Resonance (NMR). In this work, we examine the accuracy of structural representation resulting from application of a number of explicit and implicit solvent models and refinement protocols against experimental data ranging from high density of residual dipolar coupling (RDC) restraints to completely unrestrained simulations. For a prototype A-form RNA helix, our results indicate that AMBER RNA force field with either implicit or explicit solvent can produce a realistic dynamic representation of RNA helical structure, accurately cross-validating with respect to a diverse array of NMR observables. In refinement against NMR distance restraints, modern MD force fields are found to be equally adequate, with high fidelity cross-validation to the residual dipolar couplings (RDCs) and residual chemical shift anisotropies (RCSAs), while slightly over-estimating structural order as monitored via NMR relaxation data. With restraints trimmed to encode only for base pairing information, cross-validation quality significantly deteriorates, now exhibiting a pronounced dependence on the choice of the solvent model. This deterioration is found to be partially reversible by increasing planarity restraints on the nucleobase geometry. For completely unrestrained MD simulations, the choice of water model becomes very important, with the best-performing TIP4P-Ew accurately reproducing both the RDC and RCSA data, while closely matching the NMR-derived order parameters. The information provided here will serve as a foundation for MD-based refinement of solution state NMR structures of RNA.

Nodeless superconductivity and preserved time-reversal symmetry in the noncentrosymmetric Mo3P superconductor

We report a comprehensive study of the noncentrosymmetric superconductor Mo$_3$P. Its bulk superconductivity, with $T_c = 5.5$ K, was characterized via electrical resistivity, magnetization, and heat-capacity measurements, while its microscopic electronic properties were investigated by means of muon-spin rotation/relaxation ($\mu$SR) and nuclear magnetic resonance (NMR) techniques. In the normal state, NMR relaxation data indicate an almost ideal metallic behavior, confirmed by band-structure calculations, which suggest a relatively high electron density of states, dominated by the Mo $4d$-orbitals. The low-temperature superfluid density, determined via transverse-field $\mu$SR and electronic specific heat, suggest a fully-gapped superconducting state in Mo$_3$P, with $\Delta_0= 0.83$ meV, the same as the BCS gap value in the weak-coupling case, and a zero-temperature magnetic penetration depth $\lambda_0 = 126$ nm. The absence of spontaneous magnetic fields below the onset of superconductivity, as determined from zero-field $\mu$SR measurements, indicates a preserved time-reversal symmetry in the superconducting state of Mo$_3$P and, hence, spin-singlet pairing.

An Efficient Method for NMR Data Compression Based on Fast Singular Value Decomposition

To improve the processing speed of nuclear magnetic resonance (NMR) echo data, data compression is essential prior to NMR inversion due to the large amount of raw echo data acquired via NMR logging. In this letter, a fast singular value decomposition (FSVD) method is proposed to compress NMR data, which differs from the SVD method by developing a lower dimensional submatrix that can capture most action in the kernel matrix based on a random Hadamard matrix, and then decomposing the sub-matrix using SVD. The 2-D NMR relaxation data are taken as examples to evaluate the efficiency of the FSVD method. The inverted T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> spectra after FSVD compression are compared with spectra after SVD compression and spectra without compression through numerical simulation experiments. Results show that under the same conditions, the compression time is shorter for the FSVD method than for the SVD method, the inversion time is far shorter for compressed NMR data than for uncompressed NMR data, and the accuracy of the inverted T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> spectra after compression is close to that without compression. In addition, the effect of the Hadamard matrix on the accuracy and speed of the FSVD method is studied through 1000 random simulations. Findings show that the compression results of the FSVD method with different Hadamard matrices are close, indicating that the efficiency of this method is not affected by the Hadamard matrix.

Joint inversion of nuclear magnetic resonance data from partially saturated rocks using a triangular pore model

Measurement of nuclear magnetic resonance (NMR) relaxation is a well-established laboratory/borehole method to characterize the storage and transport properties of rocks due to its direct sensitivity to the corresponding pore-fluid content (water/oil) and pore sizes. Using NMR, the correct estimation of, e.g., permeability strongly depends on the underlying pore model. Usually, one assumes spherical or cylindrical pores for interpreting NMR relaxation data. To obtain surface relaxivity and thus, the pore-size distribution, a calibration procedure by, e.g., mercury intrusion porosimetry or gas adsorption has to be used. Recently, a joint inversion approach was introduced that used NMR measurements at different capillary pressures/saturations (CPS) to derive surface relaxivity and pore-size distribution (PSD) simultaneously. We further extend this approach from a bundle of parallel cylindrical capillaries to capillaries with triangular cross sections. With this approach, it is possible to account for residual or trapped water within the pore corners/crevices of partially saturated pores. In addition, we have developed a method that allows determining the shape of these triangular capillaries by using NMR measurements at different levels of drainage and imbibition. We show the applicability of our approach on synthetic and measured data sets and determine how the combination of NMR and CPS significantly improves the interpretation of NMR relaxation data on fully and partially saturated porous media.

Magnetic Resonance and Conductivity Study of Lead–Cadmium Fluorosilicate Glasses and Glass-Ceramics

The fluorine motional dynamics in fluorosilicate glasses and glass-ceramics of the SiO2–PbF2–CdF2 system was investigated by complex impedance spectroscopy and 19F nuclear magnetic resonance (NMR) spectroscopy, and the coordination environment of a Cu2+ paramagnetic probe was examined by electron paramagnetic resonance (EPR) spectroscopy. Glass-ceramics were obtained by heat treatment of glass at a temperature between the glass-transition (Tg) and the onset of crystallization (Tx). Ionic conductivity of about 1.6 × 10–4 S/cm was obtained at 500 K for glass. The conductivity of the glass-ceramics was found to be lower than that obtained for the glass. The temperature dependence of the 19F NMR spin–lattice relaxation times was investigated between 300 and 770 K. The 19F NMR results of glass exhibit the qualitative features associated with fluorine mobility, namely, the presence of a relaxation rate maximum below Tg. The NMR relaxation data of the glass-ceramics were analyzed assuming two fluorine dynamics. ...

NMR Relaxation and Molecular Dynamics Simulations of Side Chain Dynamics in Proteins

Molecular dynamics (MD) simulations and nuclear magnetic resonance (NMR) spin relaxation experiments have become increasingly powerful to study protein dynamics at atomic resolution due to steady improvements in physical models and computation power. Good agreement between generalized Lipari-Szabo (S2NH) order parameters derived from experiment and simulation is now observed for the backbone dynamics of a number of proteins. Unfortunately, the agreement for side chains, as e.g. probed by S2CH3 for methyl-containing side chains, is much poorer. In this work we discuss several issues with methyl side chains that need to be addressed to close the gap between NMR and MD. Accounting for protein tumbling is one very important factor to obtain a good agreement. In our hands, the application of improved water force fields with an appropriate way of including anisotropic overall protein tumbling improves the prediction of experimentally measured dynamic observables by MD simulations. We demonstrate these aspects for T4 lysozyme as a representative example. Our results guide the way for extracting from the NMR relaxation data the most accurate parameters that describe protein side chain dynamics and report on conformational entropy. Additionally, we reparametrized side chain dihedral angle energy barriers for methyl rotation in the Amber99SB∗-ILDN force field. We demonstrate that this leads to a much more realistic dynamics of methyl groups and is another important feature to close the gap between spectral densities from NMR relaxation and MD simulation.