Interpretation Of Nuclear Magnetic Resonance Data Research Articles

Local Structure in Disordered Melilite Revealed by Ultrahigh Field 71Ga and 139La Solid-State Nuclear Magnetic Resonance Spectroscopy.

Multinuclear Nuclear Magnetic Resonance (NMR) spectroscopy of quadrupolar nuclei at ultrahigh magnetic field provides compelling insight into the short-range structure in a family of fast oxide ion electrolytes with La1+xSr1-xGa3O7+0.5x melilite structure. The striking resolution enhancement in the solid-state 71Ga NMR spectra measured with the world's unique series connected hybrid magnet operating at 35.2 T distinctly resolves Ga sites in four- and five-fold coordination environments. Detection of five-coordinate Ga centers in the site-disordered La1.54Sr0.46Ga3O7.27 melilite is critical given that the GaO5 unit accommodates interstitial oxide ions and provides excellent transport properties. This work highlights the importance of ultrahigh magnetic fields for the detection of otherwise broad spectral features in systems containing quadrupolar nuclei and the potential of ensemble-based computational approaches for the interpretation of NMR data acquired for site-disordered materials.

A critical mini-review on the low-field nuclear magnetic resonance investigation of pore coupling effects in near-surface environments

The assessment and monitoring of groundwater resources is of increasing importance to ensure the continuous supply of fresh water for human activity and endangered ecosystems. These groundwater resources include fully saturated aquifers, water in unsaturated soil, and water trapped as rock moisture in weathered bedrocks. Low-field nuclear magnetic resonance (NMR) is a method with unique sensitivity to pore water, as it is based on the magnetization and relaxation behavior of the spin magnetic moment of hydrogen atoms forming water molecules. It is a cost-effective and minimally-invasive technology that can help characterize the pore structures and the groundwater distribution and transport in different types of subsurface materials. However, the interpretation of NMR data from samples with complex bimodal or multimodal porous geometries requires the consideration of pore coupling effects. A pore-coupled system presents significant magnetization exchange between macro- and micropores within the measurement time, making the independent characterization of each pore environment difficult. Developing a better understanding of pore coupling is of great importance for the accurate estimation of hydrogeological parameters from NMR data. This mini-review presents the state-of-art in research exploring the two factors controlling pore coupling: surface geochemistry and network connectivity, summarizes existing experimental and numerical modeling approaches that have been used to study pore coupling and discusses the pore coupling effects in fully and partially saturated conditions. At the end of this review, we outline major knowledge gaps and highlight the research needs in the vadose zone.

Electron-nuclear hyperfine coupling in quantum kagome antiferromagnets from first-principles calculation and a reflection of the defect effect

The discovery of ideal spin-1/2 kagome antiferromagnets Herbertsmithite and Zn-doped Barlowite represents a breakthrough in the quest for quantum spin liquids (QSLs), and nuclear magnetic resonance (NMR) spectroscopy plays a prominent role in revealing the quantum paramagnetism in these compounds. However, interpretation of NMR data that is often masked by defects can be controversial. Here, we show that the most significant interaction strength for NMR, i.e. the hyperfine coupling (HFC) strength, can be reasonably reproduced by first-principles calculations for these proposed QSLs. Applying this method to a supercell containing Cu-Zn defects enables us to map out the variation and distribution of HFC at different nuclear sites. This predictive power is expected to bridge the missing link in the analysis of the low-temperature NMR data.

Molecular Sensors for NMR-Based Detection.

Reliable and precise methods capable of unambiguously identifying target analytes in real-world samples are indispensable in various fields, ranging from biological studies and diagnosis to quality control. Among various analytic techniques, nuclear magnetic resonance (NMR) is uniquely powerful as it provides multidimensional data useful for structural analysis at the atomic level. The rich information obtained from various NMR experiments allows one to access not only molecular structures and interactions but also the dynamics and diffusional properties. However, the interpretation of NMR data in the analysis of real-world mixtures can be challenging and is often complicated by the overlap of the NMR resonances of each component. Moreover, the inherently low sensitivity of the NMR technique hampers its implementation in many detections, where the analytes of interest are present at low concentrations. By a combination of heteronuclear NMR, dedicatedly designed sensors, ingenious transduction mechanisms, and powerful NMR pulse sequences, significant advancements were made to conquer these limitations. The present review summarizes the sensing systems that effectively facilitate NMR-based detection with an emphasis on the chemical perspective of sensor design and transduction mechanism. Advances in hyperpolarized sensors to boost the sensitivity of detection will also be included where appropriate.

Hydraulic Conductivity Calibration of Logging NMR in a Granite Aquifer, Laramie Range, Wyoming.

In granite aquifers, fractures can provide both storage volume and conduits for groundwater. Characterization of fracture hydraulic conductivity (K) in such aquifers is important for predicting flow rate and calibrating models. Nuclear magnetic resonance (NMR) well logging is a method to quickly obtain near-borehole hydraulic conductivity (i.e., KNMR ) at high-vertical resolution. On the other hand, FLUTe flexible liner technology can produce a K profile at comparable resolution but requires a fluid driving force between borehole and formation. For three boreholes completed in a fractured granite, we jointly interpreted logging NMR data and FLUTe K estimates to calibrate an empirical equation for translating borehole NMR data to K estimates. For over 90% of the depth intervals investigated from these boreholes, the estimated KNMR are within one order of magnitude of KFLUTe . The empirical parameters obtained from calibrating the NMR data suggest that "intermediate diffusion" and/or "slow diffusion" during the NMR relaxation time may occur in the flowing fractures when hydraulic aperture are sufficiently large. For each borehole, "intermediate diffusion" dominates the relaxation time, therefore assuming "fast diffusion" in the interpretation of NMR data from fractured rock may lead to inaccurate KNMR estimates. We also compare calibrations using inexpensive slug tests that suggest reliable KNMR estimates for fractured rock may be achieved using limited calibration against borehole hydraulic measurements.

Bioinformatics tools for the analysis of NMR metabolomics studies focused on the identification of clinically relevant biomarkers.

Metabolomics, a systems biology approach focused on the global study of the metabolome, offers a tremendous potential in the analysis of clinical samples. Among other applications, metabolomics enables mapping of biochemical alterations involved in the pathogenesis of diseases, and offers the opportunity to noninvasively identify diagnostic, prognostic and predictive biomarkers that could translate into early therapeutic interventions. Particularly, metabolomics by Nuclear Magnetic Resonance (NMR) has the ability to simultaneously detect and structurally characterize an abundance of metabolic components, even when their identities are unknown. Analysis of the data generated using this experimental approach requires the application of statistical and bioinformatics tools for the correct interpretation of the results. This review focuses on the different steps involved in the metabolomics characterization of biofluids for clinical applications, ranging from the design of the study to the biological interpretation of the results. Particular emphasis is devoted to the specific procedures required for the processing and interpretation of NMR data with a focus on the identification of clinically relevant biomarkers.

Nuclear-Magnetic-Resonance Response of Brine, Oil, and Methane in Organic-Rich Shales

Summary The application of nuclear-magnetic-resonance (NMR) methods to evaluate the fluid content in hydrocarbon reservoirs requires the understanding of the NMR response of the fluids present in the rock. The presence of multiple fluids such as liquid, gaseous, or adsorbed phases in nanometer-sized pores (associated with various minerals and organic matter) adds another degree of complexity to the interpretation of NMR data in shales. We present a laboratory study on the NMR responses of brine, oil, and methane in shales at 2 MHz. NMR transverse relaxation time (T2) distributions were acquired on core plugs from the Haynesville, Barnett, and Woodford shale formations. The NMR T2 distributions were acquired after brine (2.5% potassium chloride) and oil (dodecane) imbibition and saturation. After brine imbibition, we observed an increase in porosity at T2 ≤ 1 ms. However, after saturation at increasing pressures we observe a porosity increase at T2 ≈ 6–20 ms. Dodecane imbibition and saturation induced a porosity increase at T2 ≈ 10 ms. The measurements with methane were conducted on Haynesville core plugs at a methane pressure of 4,000 psi. The NMR T2 signal of methane in shales appears to be at approximately 10 ms. These results show that the NMR response of methane and oil is very similar in shales. Monitoring the saturation increase with NMR shows that brine can enter the entire pore spectrum, whereas oil and methane have access only to a fraction of the pore space.

Relating nuclear magnetic resonance relaxation time distributions to void-size distributions for unconsolidated sand packs

Nuclear magnetic resonance (NMR) is used in near-surface geophysics to understand the pore-scale properties of geologic material. The interpretation of NMR data in geologic material assumes that the NMR relaxation time distribution ([Formula: see text]-distribution) is a linear transformation of the void-size distribution (VSD). This interpretation assumes fast diffusion and can be violated for materials with high surface relaxivity and/or large pores. We compared [Formula: see text]-distributions to VSDs using grain-size distributions (GSDs) as a proxy for VSDs. Measurements were collected on water-saturated sand packs with a range of grain sizes and surface relaxivities, such that some samples were expected to violate the fast diffusion assumption. Samples were prepared from silica sand with three different average grain sizes and were coated with the iron-oxide mineral hematite to vary the surface relaxivity. We found analytically that outside the fast diffusion regime, the [Formula: see text]-distributions are broader than in the fast diffusion regime, which could lead to misinterpretation of NMR data. The experimental results showed that the [Formula: see text]-distributions were not linear transformations of the GSDs. The GSDs were a single peak independent of the hematite coating. The [Formula: see text]-distributions were broader than the measured GSDs, and the center of the distribution depended on the coating. Using an equation that does not assume fast diffusion to transform the [Formula: see text]-distributions to NMR-estimated VSDs resulted in distributions that were centered on a single radius. However, our attempts to recover the VSDs, as estimated from laser particle size analysis, were unsuccessful; the NMR-estimated VSDs were broader and yielded average pore radii that were much smaller than expected. We found that our approach was useful for determining relative VSDs from [Formula: see text]-distributions; however, future research is needed to develop a method for calibrating the NMR-estimated VSDs for unconsolidated sands.

The effect of spatial variation in surface relaxivity on nuclear magnetic resonance relaxation rates

Nuclear magnetic resonance (NMR) relaxation measurements are sensitive to the physiochemical environment of water in saturated porous media and can provide information about the properties of geologic material. Interpretation of NMR data typically relies on three assumptions: that pores within the geologic material are effectively isolated such that the diffusion of a proton between pores is limited (i.e., there is weak coupling); that relaxation occurs in the fast-diffusion regime; and that surface relaxivity [Formula: see text] is uniform throughout the measured volume. We investigated the effect of spatial variation in [Formula: see text] on the NMR relaxation measurement and evaluated two equations relating [Formula: see text] to the NMR relaxation rate for samples containing two types of surfaces, each with a different surface relaxivity. One equation was valid when there is weak diffusional coupling between pores, the other is valid when there is strong diffusional coupling. We prepared a suite of samples composed of quartz sand and an iron-coated quartz sand. NMR relaxation occurred in two distinct regions: the weak- and strong-coupling regions. In the weak-coupling region, the equation did not accurately represent the relationship between the two [Formula: see text] values and the NMR relaxation rate, suggesting that further research is required to understand the effect of spatially variable [Formula: see text] in this relaxation region. In the strong-coupling region, the equation accurately represented the relationship between the two values of [Formula: see text] and the NMR relaxation rate. The results from these laboratory experiments represented a first step towards accounting for spatial variability in [Formula: see text] in the interpretation of NMR data.

Case study of quantification of oil viscosity and saturation in complex reservoirs using advanced nuclear magnetic resonance log interpretation techniques

In the hydrocarbon exploration process, after a prospect has been identified and an exploration well has been drilled, a critical piece of information is the oil type. Earlier wireline or while-drilling well-logging technologies provided rock properties and saturation information, but relied on expensive sampling and testing to determine oil properties. This weakness was overcome through the introduction of nuclear magnetic resonance (NMR) logs that can provide formation properties—lithology-independent porosity, porosity distribution, permeability, etcetera—and information about the reservoir fluid viscosity. NMR data were recently acquired in complex, high-clay content, low-salinity oil reservoirs. Traditional petrophysical interpretations throughout these reservoirs were confronted with a complex lithology—comprising feldspathic litharenites and volcanic lithic components—high clay content and low formation water salinity, of 3-4 Kppm NaCl eq. This extended abstract shows how acquisition and interpretation of NMR data not only provided porosity and porosity distribution, but also identified oil viscosity across the logged intervals. Advanced NMR log interpretation techniques (2D-NMR maps of diffusion (D) versus T2, int) were used to identify oil NMR signal. This technique produced a continuous profile of diffusion and intrinsic T2 distribution maps. Once the oil NMR signal was identified, an estimation of the oil viscosity was also possible because D and T2, int are related with viscosity. Several available correlations have been used and results were comparable with production data.

Estimation of NMR log parameters from conventional well log data using a committee machine with intelligent systems: A case study from the Iranian part of the South Pars gas field, Persian Gulf Basin

Nuclear Magnetic Resonance (NMR) log provides useful information for petrophysical study of the hydrocarbon bearing intervals. Free fluid porosity (effective porosity), rock permeability and bound fluid volume (BFV) could be obtained by processing and interpretation of NMR data. The present study proposes an improved strategy to make a quantitative correlation between the NMR log parameters and conventional well logs by integration of different intelligent systems using the concept of committee machine. The proposed committee machine with intelligent systems (CMIS) combines the results of Fuzzy Logic (FL), Neuro-Fuzzy (NF) and Neural Network (NN) algorithms for overall estimation of the NMR log parameters from conventional well log data. It assigns a weight factor to each of the individual intelligent algorithms showing its contribution in overall prediction. The weight factors are derived in two ways: simple averaging and weighted averaging. In the weighted averaging method a genetic algorithm (GA) was employed to obtain the optimal contribution of each algorithm in construction of the CMIS. The proposed methodology was applied to the South Pars gas field, Persian Gulf Basin. The petrophysical logs from two wells were used for constructing the intelligent models and a third well from the field was used to evaluate the reliability of the developed models. The results indicate the higher performance of the GA optimized model over the individual intelligent systems performing alone.

A computer-based protocol for semiautomated assignments and 3D structure determination of proteins.

A strategy is presented for the semiautomated assignment and 3D structure determination of proteins from heteronuclear multidimensional Nuclear Magnetic Resonance (NMR) data. This approach involves the computer-based assignment of the NMR signals, identification of distance restraints from nuclear Overhauser effects, and generation of 3D structures by using the NMR-derived restraints. The protocol is described in detail and illustrated on a resonance assignment and structure determination of the FK506 binding protein (FKBP, 107 amino acids) complexed to the immunosuppressant, ascomycin. The 3D structures produced from this automated protocol attained backbone and heavy atom rmsd of 1.17 and 1.69 A, respectively. Although more highly resolved structures of the complex have been obtained by standard interpretation of NMR data (Meadows et al. (1993) Biochemistry, 32, 754-765), the structures generated with this automated protocol required minimal manual intervention during the spectral assignment and 3D structure calculations stages. Thus, the protocol may yield an approximate order of magnitude reduction in the time required for the generation of 3D structures of proteins from NMR data.

Protein dynamics and NMR relaxation: comparison of simulations with experiment

Comparisons between molecular dynamics simulations of proteins and experiment have been based on temperature factors1–5 derived from X-ray spectra and on the stability of hydrogen bonds6. Here we present a novel method of testing molecular dynamics simulations against nuclear magnetic resonance (NMR) relaxation measurements, based on the recently developed model-free approach7 to the interpretation of NMR data. As NMR relaxation in proteins is determined by dynamics on the picosecond–nanosecond time scale, a comparison of NMR experiments and molecular dynamics simulations that last for less than ∼100ps is more meaningful than previous ones3–6 involving much longer time scales. We make contact between a molecular dynamics simulation8 and a 13C-NMR relaxation study9 of pancreatic trypsin inhibitor (PTI) by comparing generalized order parameters (which are measures of the extent of angular motion of the bonds) extracted from the relaxation data9 with those calculated10 from a 96-ps trajectory8. We show that the theoretical order parameters indicate less motion than their experimental counterparts. The relative flexibility of the residues studied, however, is reasonably well described by the simulation.