Experimental Nuclear Magnetic Resonance Data Research Articles

Core Analysis Using Nuclear Magnetic Resonance

Core analysis is critical for oil and gas exploration and recovery. Nuclear magnetic resonance (NMR) has been widely applied to the petroleum industry for many years, and NMR core analysis plays an important role in supporting borehole NMR formation evaluation and understanding the mechanisms of petrophysics and petrochemistry. It’s unfortunate, however, that there has been a lack of commented descriptions of the basic procedures for NMR rock core analysis in the literature, which has led to issues of comparability and repeatability of NMR measurements between different laboratories. In this paper, we present a brief guideline for NMR rock core analysis of conventional T2, T1, T1-T2, and D-T2 measurements. As an established procedure in daily laboratory work, NMR core analysis includes three sophisticated steps: rock sample preparation, an NMR experiment, and data analysis. The acquisition and saturation of rock samples are described, and corresponding solutions are also considered. Detailed NMR experimental measurements, data setting, and calibration are addressed. Laplace inversion for different NMR experiments and models for estimation of porosity, irreducible water, permeability, pore structure, and oil saturation are included. The aim of this paper is to try to standardize general NMR experiments for rock core analysis.

Complementary Mass Transport Investigations in Open-Cell Foams: Full-Field Computational Fluid Dynamics Simulation with Random-Walk Microscopic Particle Tracking and Methane Nuclear Magnetic Resonance Displacement Measurements

Numerical simulation can provide detailed understanding of mass transport within complex structures. For this purpose, numerical tools are required that can resolve the complex morphology and consider the contribution of both convection and diffusion. Solving the Navier–Stokes equations alone, however, neglects self-diffusion. This influences the simulated displacement distribution of flow especially in porous media at low Péclet numbers (Pe < 16) and in near-wall regions where diffusion is the dominant mechanism. To address this problem, this study uses μCT-based computational fluid dynamics (CFD) simulations in OpenFOAM coupled with the random-walk particle tracking (PT) module disTrackFoam and cross-validated experimentally using pulsed-field gradient (PFG) nuclear magnetic resonance (NMR) measurements of gas flow within open-cell foams (OCFs). The results of the multi-scale simulations—with a resolution of 130–190 µm—and experimental PFG NMR data are compared in terms of diffusion propagators, which are microscopic displacement distributions of gas flows in OCFs during certain observation times. Four different flow rates with Péclet numbers in the range of 0.7–16 are studied in the laminar flow regime within 10 and 20 PPI OCFs, and axial dispersion coefficients were calculated. Cross-validation of PFG NMR measurements and CFD-PT simulations revealed a very good matching with integral differences below 0.04%, underpinning the capability of both complementary methods for multi-scale transport analysis.

Revealing the effect of phase and time coupling on NMR relaxation rate by random walks in phase space.

Phase-time coupling is a natural process in the phase random walks of a spin system; however, its effect on the nuclear magnetic resonance (NMR) relaxation is a challenge to the established theories such as the second-order quantum perturbation theory. This paper extends the recently developed phase diffusion method to treat the phase-time coupling effect, based on uncoupled phase diffusions, and coupled random walks. The instantaneous projection of the rotating random field is employed to get the accumulated phase of the NMR observable. In the static frame and the rotating frame, the phase diffusion coefficients are derived. The obtained theoretical results show that the phase-time coupling has a significant impact on the NMR relaxation rate: The angular frequency ω in the spectral density is modified to an apparent angular frequency ηω, where η is the phase-time coupling constant. The strongest coupling has η equaling 2, while η equaling 1 corresponds to the traditional results. As an example, the modified relaxation time expressions based on both monoexponential and nonmonoexponential functions can successfully fit the previously reported ^{13}CT_{1} NMR experimental data of polyisobutylene (PIB) in the blend of PIB and head-to-head poly(propylene). In contrast, the traditional relaxation rate expression based on the monoexponential time correlation function cannot fit such experimental data. With the phase-time coupling, the obtained characteristic time of the segmental motion is faster than that from conventional results.

High-Quality Reconstruction for Laplace NMR Based on Deep Learning.

Laplace nuclear magnetic resonance (NMR) exploits relaxation and diffusion phenomena to reveal information regarding molecular motions and dynamic interactions, offering chemical resolution not accessible by conventional Fourier NMR. Generally, the applicability of Laplace NMR is subject to the performance of signal processing and reconstruction algorithms involving an ill-posed inverse problem. Here, we propose a proof-of-concept of a deep-learning-based method for rapid and high-quality spectra reconstruction from Laplace NMR experimental data. This reconstruction method is performed based on training on synthetic exponentially decaying data, which avoids a vast amount of practically acquired data and makes it readily suitable for one-dimensional relaxation and diffusion measurements by commercial NMR instruments.

Experimental and computational studies on the formation of mixed amide-hydride solid solutions for CsNH2–CsH system

In this study, experimental determination and computational prediction are combined to investigate the formation of a mixed amide-hydride solid solution for the CsNH2–CsH system in a wide compositional range. The experimentally obtained results strongly indicate that a complete amide-hydride solid solution Cs(NH2)xH1-x with a stable cubic structure is achievable when the molar fraction of amide (x) is lower than 0.9. These results validate and confirm our data computationally via first-principles calculations, including the simulations of infrared (IR) and nuclear magnetic resonance (NMR) spectra for structures of various compositions as well as the determination of the dipolar coupling constants. Both the computed vibrational frequencies and 1H chemical shifts of CsNH2 and CsH moieties in the Cs(NH2)xH1-x (x = 0.2, 0.5, 0.8, 1) solid solution structures agree with the experimental IR and 1H MAS NMR data of the mixed xCsNH2+(1-x)CsH samples, confirming the formation of the solid solutions. The closest interproton distance in the homogeneous Cs(NH2)0·5H0.5 solid solution is computed to be 3.67 Å, which is larger than that of the known Rb(NH2)0·5H0.5 solid solution (3.29 Å). This work's combination of theoretical research and experimentation provides a suitable framework for the structural analysis and property estimation of other M-N-H solid solutions.

Refined nuclear magnetic dipole moment of rhenium: Re185 and Re187

The refined values of the magnetic dipole moments of $^{185}$Re and $^{187}$Re nuclei are obtained. For this, we perform a combined relativistic coupled cluster and density functional theory calculation of the shielding constant for the ReO$_4^-$ anion. In this calculation, we explicitly include the effect of the finite nuclear magnetization distribution in the single-particle nuclear model using the Woods-Saxon potential for the valence nucleon. By combining the obtained value of the shielding constant $\sigma=4069(389)$~ppm with the available experimental nuclear magnetic resonance data we obtain the values: $\mu(^{185}{\rm Re})=3.1567(3)(12) \mu_N, \mu(^{187}{\rm Re})=3.1891(3)(12) \mu_N$, where the first uncertainty is the experimental one and the second is due to theory. The refined values of magnetic moments are in disagreement with the tabulated values, $\mu(^{185}{\rm Re})=3.1871(3) \mu_N, \mu(^{187}{\rm Re})=3.2197(3) \mu_N$, which were obtained using the shielding constant value calculated for the atomic cation Re$^{7+}$ rather than the molecular anion. The updated values of the nuclear magnetic moments resolve the disagreement between theoretical predictions of the hyperfine structure of H-like rhenium ions which were based on the tabulated magnetic moment values and available experimental measurements. Using these experimental data we also extract the value of the parameter of nuclear magnetization distribution introduced in [J. Chem. Phys. \textbf{153}, 114114 (2020)], which is required to predict hyperfine structure constants for rhenium compounds.

NMRpQuant: an automated software for large scale urinary total protein quantification by one-dimensional 1H NMR profiles.

Summary 1H nuclear magnetic resonance (NMR) spectroscopy is an established bioanalytical technology for metabolic profiling of biofluids in both clinical and large-scale population screening applications. Recently, urinary protein quantification has been demonstrated using the same 1D 1H NMR experimental data captured for metabolic profiling. Here, we introduce NMRpQuant, a freely available platform that builds on these findings with both novel and further optimized computational NMR approaches for rigorous, automated protein urine quantification. The results are validated by interlaboratory comparisons, demonstrating agreement with clinical/biochemical methodologies, pointing at a ready-to-use tool for routine protein urinalyses.Availability and implementation NMRpQuant was developed on MATLAB programming environment. Source code and Windows/macOS compiled applications are available at https://github.com/pantakis/NMRpQuant, and working examples are available at https://doi.org/10.6084/m9.figshare.18737189.v1.Supplementary information Supplementary data are available at Bioinformatics online.

LigninGraphs: lignin structure determination with multiscale graph modeling

Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub.Graphical abstract

Studying the Dynamics of a Complex G-Quadruplex System: Insights into the Comparison of MD and NMR Data.

Molecular dynamics (MD) simulations are coming of age in the study of nucleic acids, including specific tertiary structures such as G-quadruplexes. While being precious for providing structural and dynamic information inaccessible to experiments at the atomistic level of resolution, MD simulations in this field may still be limited by several factors. These include the force fields used, different models for ion parameters, ionic strengths, and water models. We address various aspects of this problem by analyzing and comparing microsecond-long atomistic simulations of the G-quadruplex structure formed by the human immunodeficiency virus long terminal repeat (HIV LTR)-III sequence for which nuclear magnetic resonance (NMR) structures are available. The system is studied in different conditions, systematically varying the ionic strengths, ion numbers, and water models. We comparatively analyze the dynamic behavior of the G-quadruplex motif in various conditions and assess the ability of each simulation to satisfy the nuclear magnetic resonance (NMR)-derived experimental constraints and structural parameters. The conditions taking into account K+-ions to neutralize the system charge, mimicking the intracellular ionic strength, and using the four-atom water model are found to be the best in reproducing the experimental NMR constraints and data. Our analysis also reveals that in all of the simulated environments residues belonging to the duplex moiety of HIV LTR-III exhibit the highest flexibility.

A Multi-Scale Fractal Approach for Coal Permeability Estimation via MIP and NMR Methods

Permeability in porous media has an important role in many engineering applications, which depends mainly on the pore size, distribution, and connectivity of porous media. As the pore structure distribution of coal has a multi-scale fractal dimension characteristic, this study aimed to propose a multi-scale fractal dimension characteristics units model (MFU) to describe the pore structure distribution by analyzing the multi-scale fractal dimension characteristics of coal pore media. Then, a multi-scale fractal permeability model was established based on MFU. The pore structure distribution was obtained by mercury injection porosimetry (MIP) and nuclear magnetic resonance (NMR) experiments. Based on MIP and NMR experimental data, the permeability contribution of different pore diameters were calculated. The results show that the permeability contribution of the micropore was minimal and can be ignored. The permeability contribution of mesopores was about 1–5%, and the permeability contribution of macropores was about 95–99%, which plays a decisive role in the seepage process. The calculated results, based on multi-scale fractal permeability model and the experimental permeability data, are in the same order of magnitude. The permeability prediction based on proposed model is better than classical single fractal permeability model.

Determination of pore size distribution in tight gas sandstones based on Bayesian regularization neural network with MICP, NMR and petrophysical logs

Mercury injection capillary pressure (MICP) is one of the most important means for quantitative study of pore structure, however, its application is often limited due to its expensive, destructive and dangerous characteristics. Nuclear magnetic resonance (NMR) then becomes a common alternative by log-linear conversion of transverse relaxation time (T2) spectrum into pseudo-MICP or pore size distribution (PSD) though the accuracy and generality is unconvincing. Petrophysical logs have rich geological-petrophysical information but are seldom used in pore structure research. In this paper, PSD determination of tight gas sandstones based on Bayesian regularization neural network (BRNN) with MICP, NMR and petrophysical logs is studied. A numerical workflow is developed to preprocess MICP and NMR experimental data and determine PSD. Subsequently, two BRNN models are constructed respectively with joint laboratory T2 spectrum and petrophysical logs as main inputs and pore radius and mercury saturation from MICP measurement as training target. It is revealed that, accurate PSD can be determined from prediction results of BRNN models, wherein the NMR-based BRNN can be employed to obtain PSD from more easily accessible NMR measurement and the log-based one is expected to be generalized in the field. Conversion of PSD by fitting pore radius with T2 through their piecewise linear or quintic polynomial relation in log-log coordinate is equivalent to underfitting or overfitting in accuracy of machine learning and is confined to laboratory NMR with joint MICP. Detailed discussion on BRNN modeling, pore radius fitting and the corresponding numerical workflow proposed here may provide valuable insight into petrophysical-log analysis and quantitative evaluation on pore structure of complex reservoirs.

Aggregation-Prone Structural Ensembles of Transthyretin Collected With Regression Analysis for NMR Chemical Shift.

Monomer dissociation and subsequent misfolding of the transthyretin (TTR) is one of the most critical causative factors of TTR amyloidosis. TTR amyloidosis causes several human diseases, such as senile systemic amyloidosis and familial amyloid cardiomyopathy/polyneuropathy; therefore, it is important to understand the molecular details of the structural deformation and aggregation mechanisms of TTR. However, such molecular characteristics are still elusive because of the complicated structural heterogeneity of TTR and its highly sensitive nature to various environmental factors. Several nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics (MD) studies of TTR variants have recently reported evidence of transient aggregation-prone structural states of TTR. According to these studies, the stability of the DAGH β-sheet, one of the two main β-sheets in TTR, is a crucial determinant of the TTR amyloidosis mechanism. In addition, its conformational perturbation and possible involvement of nearby structural motifs facilitates TTR aggregation. This study proposes aggregation-prone structural ensembles of TTR obtained by MD simulation with enhanced sampling and a multiple linear regression approach. This method provides plausible structural models that are composed of ensemble structures consistent with NMR chemical shift data. This study validated the ensemble models with experimental data obtained from circular dichroism (CD) spectroscopy and NMR order parameter analysis. In addition, our results suggest that the structural deformation of the DAGH β-sheet and the AB loop regions may correlate with the manifestation of the aggregation-prone conformational states of TTR. In summary, our method employing MD techniques to extend the structural ensembles from NMR experimental data analysis may provide new opportunities to investigate various transient yet important structural states of amyloidogenic proteins.

NMR Spin Relaxation Theory of Biomolecules Undergoing Highly Asymmetric Exchange with Large Interaction Partners

The transient interactions of proteins and other molecules with much larger structures, such as synthetic or biological nanoparticles, lead to certain types of enhanced nuclear magnetic resonance (NMR) spin relaxation effects, which can be accurately measured by multidimensional solution NMR techniques. These relaxation effects provide new information about the nanostructures and the protein, their interactions, internal dynamics, and associated kinetic and thermodynamic parameters, such as exchange rates and populations. Although theoretical treatments exist that cover either the fast or slow exchange limits, a theoretical treatment that applies to all practically relevant exchange processes is still missing. A unified theoretical framework is presented for this purpose based on a stochastic Liouville equation (SLE). It covers nuclear spin dynamics, overall rotational diffusion of both the protein and the nanostructure, the exchange process between a free state and a bound state, and internal protein dynamics. Although the numerical implementation of the SLE typically involves large matrices, it is shown here that it is computationally still tractable for situations relevant in practice. Application of the theory demonstrates how transverse relaxation is substantially impacted by the kinetics of binding on a wide range of exchange timescales. It is further shown that when exchange occurs on the appropriate timescale, transverse relaxation is able to report on internal dynamics far slower than observable by traditional transverse relaxation experiments. The SLE will allow the realistic and quantitative interpretation of experimental NMR data reporting about transient protein-nanoparticle interactions, thereby providing a powerful tool for the characterization of protein dynamics modes on a vast range of timescales including motions that may be functionally relevant.

Describing NMR relaxation by effective phase diffusion equation

This paper proposes an effective diffusion equation method to analyze nuclear magnetic resonance (NMR) relaxation. NMR relaxation is a spin system recovery process, where the evolution of the spin system is affected by the random field due to Hamiltonians, such as dipolar couplings. The evolution of magnetization can be treated as a random walk in phase space described either by a normal or fractional phase diffusion equation. Based on these phase diffusion equations, the NMR relaxation rates and equations can be obtained, exemplified in the analysis of relaxations affected by an arbitrary random field, and by dipolar coupling for both like and unlike spins. The obtained theoretical results are consistent with the reported results in the literature. Additionally, the anomalous relaxation expression obtained from the Mittag-Leffler function based time correlation function can successfully fit the previously reported 13C T1 NMR experimental data of polyisobutylene (PIB) in the blend of PIB and head-to-head poly(propylene) (hhPP). Furthermore, the proposed phase diffusion approach provides an intuitive way to interpret NMR relaxation, particularly for the fractional NMR relaxation, which is still a challenge to explain by the available theoretical methods. The paper provides additional insights into NMR and magnetic resonance imaging (MRI) relaxation experiments.

From molecular quantum electrodynamics at finite temperatures to nuclear magnetic resonance

The algebraic reformulation of molecular Quantum Electrodynamics (mQED) at finite temperatures is applied to Nuclear Magnetic Resonance (NMR) in order to provide a foundation for the reconstruction of much more detailed molecular structures, than possible with current methods. Conventional NMR theories are directly related to the effective spin model, which idealizes nuclei as fixed points in a lattice . However, the delocalization of spins due to the thermal energy is more realistically described by the amplitude square of the nuclear wave function ∣Ψ β (X)∣2 with , instead of fixed points in . In addition, the phenomenological integration of thermalization only allows an investigation of the molecular structure based on the position of the punctiform center of an NMR signal, but not based on the width and shape of NMR signals. Hence, a lot information on molecular structures remain hidden in experimental NMR data. In this document it is shown how ∣Ψ β (X)∣2, can be reconstructed from NMR data. To this end, it is shown how NMR spectra can be calculated directly from mQED at finite temperatures without involving the effective description. The new method connects all data points—the positions, widths, heights and shapes—of NMR signals directly with the molecular structure, which allows more detailed investigations of the underlying system. Furthermore, it is shown that the presented method corrects wrong predictions of the effective spin model. The fundamental problem of performing numerical calculations with the infinite-dimensional radiation field is solved by using a purified representation of a KMS state on a W *-algebra. It is outlined that the presented method can be applied to any molecular system whose electronic ground state can be calculated using a common quantum chemical method. Therefore, the presented method can replace the effective description which forms the basis for NMR theory since 1950.

A continuous time random walk method to predict dissolution in porous media based on validation of experimental NMR data

We develop a reactive transport model for dissolution of porous materials using a Continuous Time Random Walk (CTRW) formulation with first-order kinetics. Our model is validated with a dataset for a Ketton carbonate rock sample undergoing dissolution on injection of an acid, monitored using Nuclear Magnetic Resonance (NMR). The experimental data includes the 3D porosity distribution at the beginning and end of the experiment, 1D porosity profiles along the direction of flow during dissolution, as well as the molecular fluid displacement probability distributions (propagators). With the calibration of only a single parameter, we successfully predict the porosity changes and the propagators as a signature of flow heterogeneity evolution in the dissolution experiment.We also demonstrate that heterogeneity in the flow field leads to an effective reaction rate, limited by transport of reactants, that is almost three orders of magnitude lower than measured under batch reaction conditions. The effective reaction rate predicted by the model is in good agreement with the experimentally measured rate. Furthermore, as dissolution proceeds, the formation of channels in the rock focused the flow in a few fast-flowing regions. The predicted dissolution patterns are similar to those observed experimentally. This study establishes a workflow to calibrate and validate the CTRW reactive transport model with NMR experiments.

Experimental and Theoretical Nuclear Magnetic Resonance Data from Tryptanthrin, an Alkaloid with Potential Activity Against Human Coronavirus

Coronaviruses belong to the subfamily Coronavirinae, which are large viruses with a single Ribonucleic acid tape and a nucleocapsid. In a recent study it was found that the molecule of tryptanthrin has antiviral potential against human coronavirus NL63. In this work, we used a combination of theoretical techniques with experimental nuclear magnetic resonance data to assist in the structural characterization of the Tryptanthrin molecule. Tryptanthrin was extracted from Couroupita guianensis leaves. The theoretical calculations of hydrogen-1, homonuclear spin–spin coupling constants and the quantum theory of atoms in molecules were performed employing density functional theory. Our results show that the hydrogen-1 chemical shifts calculated at the gaseous phase and solvent presented similar performance. The quantum theory of atoms in molecules calculations was able to quantify and qualify bonded interactions based on five topological parameters obtained at the analyzed bond critical point to tryptanthrin molecule. We obtained a satisfactory correlation between tryptanthrin delocalization index values and its experimental and calculated proton–proton coupling constants. In conclusion, the theoretical techniques used in this work in combination with NMR experimental data can be a useful tool in aiding the structural determination of organic compounds.

Chemical Biology Tools to Elucidate the Solution Conformation and Cellular Interactions of Cyclic Adenosine 5′‐Diphosphate Ribose

The movement of calcium ions (Ca2+) is intimately involved in regulating many physiological processes, and so there is great interest in the structure of molecules, especially second messengers, that regulate this flow. One such messenger is cyclic adenosine 5′‐diphosphate ribose (cADPR), which is suspected to play a role in Ca2+ release in cardiomyocytes. The speculated mechanism of action involves the binding of cADPR to FKBP12.6, releasing FKBP12.6 from RyR2 channels, through which Ca2+ enters the sarcoplasm from the sarcoplasmic reticulum during ventricular systole. Guided by the principle that structure determines function, we aim to advance the investigation of regulatory cADPR interactions by synthesizing and studying cADPR and its analogs using the combined tools of liquid chromatography‐mass spectrometry (LCMS) and nuclear magnetic resonance (NMR) spectroscopy. The endogenous cyclization of nicotinamide adenine dinucleotide (NAD+) to cADPR by CD38 is mimicked in vitro using ADP‐ribosylcyclase (ADP‐RC, sourced from Aplysia californica). The low substrate specificity of ADP‐RC conveniently permits the cyclization of various cADPR analogs. Kinetic assays were prompted by an observed feedback inhibition of ADP‐RC by nicotinamide (IC50 ~ 1mM), and performed using LCMS. Following synthesis and purification, NMR experimental data provided insight into favored structural conformations of cADPR (Saatori et al., 2018) Photoaffinity labeling (PAL) was aimed at identifying the cognate binding partners of cADPR, some of which have been previously described by Walseth et al. (1993). Conformational analyses of cADPR analogs revealed structural trends with correlations to the biological activity of the analogs.Support or Funding InformationDepartment of Chemistry, Saint John’s University

Modeling and Observer-Based Monitoring of RAFT Homopolymerization Reactions

Reversible addition–fragmentation chain–transfer (RAFT) polymerization of methyl methacrylate (MMA) is modeled and monitored using a multi-rate multi-delay observer in this work. First, to fit the RAFT reaction rate coefficients and the initiator efficiency in the model, in situ 1 H nuclear magnetic resonance (NMR) experimental data from small-scale (&lt;2 mL) NMR tube reactions is obtained and a least squares optimization is performed. 1 H NMR and size exclusion chromatography (SEC) experimental data from large-scale (&gt;400 mL) reflux reactions is then used to validate the fitted model. The fitted model accurately predicts the polymer properties of the large-scale reactions with slight discordance at late reaction times. Based on the fitted model, a multi-rate multi-delay observer coupled with an inter-sample predictor and dead time compensator is designed, to account for the asynchronous multi-rate measurements with non-constant delays. The multi-rate multi-delay observer shows perfect convergence after a few sampling times when tested against the fitted model, and is in fair agreement with the real data at late reaction times when implemented based on the experimental measurements.

Nuclear magnetic resonance T2 cutoffs of coals: A novel method by multifractal analysis theory

Nuclear magnetic resonance (NMR) has been widely used in petrophysical characterization of coals. For NMR experimental data analysis, transverse relaxation time (T2) cutoff value is a key parameter to identify the form of movable and irreducible fluids, and to evaluate permeability and full-scale pore size distribution (PSD). Conventionally, the T2 cutoff value is procured by a series of centrifugal experiments, which is much complicated and time consuming, and is also hard to be used in fields such as well logging. Thus, a convenient and practical method is needed for T2 cutoff value prediction. Based on series of centrifugal experiments, this study firstly determined an optimal centrifugal pressure of 1.38 MPa for T2 cutoff value calculation. The results from centrifugal experiments show that the T2 cutoff values of bituminous coals and anthracite coals in the range of 0.62–15.11 ms. Then, the multifractal analysis theory is introduced into the estimation of T2 cutoff values of coals. The results showed that the NMR T2 distribution of 100% water-saturated coal is multifractal, and the multifractal parameters of multifractal dimension (Dq) and singularity strength range (Δα) can be used to evaluate the T2 cutoff value of coals. Finally, a new multifractal-based NMR T2 cutoff calculation model was provided, and the model was verified by centrifugal experimental data of six coal samples. It is concluded that the provided multifractal analysis method is effective, convenient and independent of any other experiments. The model derived from the study of coal, is also applicable for researches of other rock types such as shales and so on.