Fast Nuclear Magnetic Resonance Research Articles

Accelerated Pure Shift NMR Spectroscopy with Deep Learning.

Pure shift nuclear magnetic resonance (NMR) spectroscopy presents a promising solution to provide sufficient spectral resolution and has been increasingly applied in various branches of chemistry, but the optimal resolution is generally accompanied by long experimental times. We present a proof of concept of deep learning for fast, high-quality, and reliable pure shift NMR reconstruction. The deep learning (DL) protocol allows one to eliminate undersampling artifacts, distinguish peaks with close chemical shifts, and reconstruct high-resolution pure shift NMR spectroscopy along with accelerated acquisition. More meaningfully, the lightweight neural network delivers satisfactory reconstruction performance on personal computers by several hundred simulated data learning, which somewhat lifts the prohibiting demand for a large volume of real training samples and advanced computing hardware generally required in DL projects. Additionally, an M-to-S strategy applicable to common DL cases is further exploited to boost the network generalization capability. As a result, this study takes a meaningful step toward deep learning protocols for broad chemical applications.

Theoretical analysis of flow effects in spatially encoded diffusion NMR.

The measurement of translational diffusion coefficients by nuclear magnetic resonance (NMR) spectroscopy is essential in a broad range of fields, including organic, inorganic, polymer, and supramolecular chemistry. It is also a powerful method for mixture analysis. Spatially encoded diffusion NMR (SPEN DNMR)" is a time efficient technique to collect diffusion NMR data, which is particularly relevant for the analysis of samples that evolve in time. In many cases, motion other than diffusion is present in NMR samples. This is, for example, the case of flow NMR experiments, such as in online reaction monitoring and in the presence of sample convection. Such motion is deleterious for the accuracy of DNMR experiments in general and for SPEN DNMR in particular. Limited theoretical understanding of flow effects in SPEN DNMR experiments is an obstacle for their broader experimental implementation. Here, we present a detailed theoretical analysis of flow effects in SPEN DNMR and of their compensation, throughout the relevant pulse sequences. This analysis is validated by comparison with numerical simulation performed with the Fokker-Planck formalism. We then consider, through numerical simulation, the specific cases of constant, laminar, and convection flow and the accuracy of SPEN DNMR experiments in these contexts. This analysis will be useful for the design and implementation of fast diffusion NMR experiments and for their applications.

Hypercomplex Low Rank Reconstruction for NMR Spectroscopy

Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering. Multi-dimensional NMR offers a major improvement in resolution with multi-dimensional spectrum, but significantly increases data acquisition time and produces hypercomplex data that is difficult to be handled. To reduce this time, non-uniformly sampling can be applied to obtain undersampled data and using a reconstruction approach, such as the state-of-the-art low rank method, to remove the spectral artifacts introduced by undersampling. However, only complex format of signal, including the real and imaginary parts, is considered in previous low rank approach, which is less efficient when dealing with hypercomplex data that has multiple components. To solve this problem, a hypercomplex low rank model is proposed by introducing an adjoint matrix operation and then solved with a fast matrix factorization algorithm. This method explores redundant information among all the components of hypercomplex signal. Using simulated data and real protein data, we demonstrate that the proposed method provides a fast and high-fidelity reconstruction of hypercomplex spectroscopy in fast NMR.

Fast NMR spectroscopy reconstruction with a sliding window based Hankel matrix

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the most promising analytical chemistry techniques, although it takes a long time to acquire data. Non-uniform sampling (NUS) is an effective way to reduce the sampling time, but faithful reconstruction methods are needed. The low rank Hankel matrix (LRHM) approach uses the low rank constraint to obtain high-quality spectra from NUS signals, but the reconstruction has a considerable time overhead. In this work, we propose a sliding window based low rank Hankel matrix approach to speed up the spectra reconstruction from NUS signals. Using the sliding window to construct a matrix can effectively reduce the size of the Hankel matrix for faster reconstructions. To further decrease the reconstruction time, parallel computation is applied in the proposed approach. The experiments on both synthetic data and realistic data demonstrate that the reconstruction speed of the proposed method is the fastest among compared methods without sacrificing the quality of spectra.

Two-dimensional NMR inversion based on fast norm smoothing method

Two-dimensional (2D) nuclear magnetic resonance (NMR) inversion operates with massive echo train data and is an ill-posed problem. It is very important to select a suitable inversion method for the 2D NMR data processing. In this study, we propose a fast, robust, and effective method for 2D NMR inversion that improves the computational efficiency of the inversion process by avoiding estimation of some unneeded regularization parameters. Firstly, a method that combines window averaging (WA) and singular value decomposition (SVD) is used to compress the echo train data and obtain the singular values of the kernel matrix. Subsequently, an optimum regularization parameter in a fast manner using the signal-to-noise ratio (SNR) of the echo train data and the maximum singular value of the kernel matrix are determined. Finally, we use the Butler-Reeds-Dawson (BRD) method and the selected optimum regularization parameter to invert the compressed data to achieve a fast 2D NMR inversion. The numerical simulation results indicate that the proposed method not only achieves satisfactory 2D NMR spectra rapidly from the echo train data of different SNRs but also is insensitive to the number of the final compressed data points.

23Na NMR T1 relaxation measurements as a probe for diffusion and dynamics of sodium ions in salt-glycerol mixtures.

Mixtures of sodium salts with oxygen-containing molecules are useful from the perspective of applications such as sodium ion batteries because they fill the gap between deep eutectic solvents and molten salt hydrates. In a previous work, the physical properties (such as diffusion coefficients, conductivity, viscosity, and glass transition temperature) of four salts, namely, Na2B4O7 · 10H2O, NaOAc · 3H2O, NaBr, and NaOAc, were measured with glycerol. Pulsed-field gradient (PFG) nuclear magnetic resonance (NMR) was also used to measure self-diffusion coefficients of 1H-bearing species. However, the technique was not able to measure diffusion of sodium ions due to the very fast NMR relaxation rate of such species, resulting in loss of the PFG NMR signal. In the current work, this study is expanded using 23Na T1 relaxation measurements which, under certain assumptions, can be translated into diffusion coefficients. Analysis of the physical properties is then correlated with self-diffusion coefficient measurements to elucidate information about structure and ionic mobility. It is shown that NaOAc · 3H2O, NaBr, and NaOAc fit models for ionic conductivity and diffusion, which are consistent with ionic liquids where charge transport is limited by ionic mobility rather than the number of charge carriers. The waters of hydration of NaOAc · 3H2O do not appear to form a separate phase but are instead strongly coordinated to the cation. In contrast, Na2B4O7 · 10H2O appears to form a water-rich phase with enhanced sodium mobility.

A fast inversion method of 2D nuclear magnetic resonance based on the novel hybrid algorithm

To make up for the limitations and improve the accuracy of 1D nuclear-magnetic-resonance (NMR) logging in the evaluation of formation fluid properties, 2D NMR logging has become the focus of research. Increasing the sequence and inversion parameters of the 2D NMR can effectively improve the antinoise properties and resolution of the inversion, but at the same time, the reduced inversion speed and increased memory occupied will put forward higher requirements on the computer configuration and add to the cost of calculation, which poses challenges to the application of the traditional 2D NMR inversion algorithms. In view of the above defects, we have developed a new fast 2D NMR inversion LSQR-RSVD hybrid algorithm, and we have used the nonnegative least-squares (LSQR) calculation result as the initial value of the RSVD inversion. Taking oil-water and gas-water models as examples, the 2D NMR inversion effects of ( T2, D), ( T1, T2) are analyzed in detail, and ( T1, D) is also discussed by several groups of echo trains with variable echo interval ( TE) and waiting time ( TW). Compared to the inversion algorithm commonly used, the new hybrid algorithm can improve the inversion speed and significantly reduce the memory occupancy. Its remarkable advantages can further promote the application of 2D and even multidimensional NMR logging in practice.

Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning.

Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental times. We present a proof-of-concept of the application of deep learning and neural networks for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signals, which lifts the prohibiting demand for a large volume of realistic training data usually required for a deep learning approach.

Fast quantitative 2D NMR for metabolomics and lipidomics: A tutorial.

Nuclear magnetic resonance (NMR) is a well-known analytical technique for the analysis of complex mixtures. Its quantitative capability makes it ideally suited to metabolomics or lipidomics studies involving large sample collections of complex biological samples. To overcome the ubiquitous limitation of spectral overcrowding when recording 1D NMR spectra on such samples, the acquisition of 2D NMR spectra allows a better separation between overlapped resonances while yielding accurate quantitative data when appropriate analytical protocols are implemented. Moreover, the experiment duration can be considerably reduced by applying fast acquisition methods. Here, we describe the general workflow to acquire fast quantitative 2D NMR spectra in the "omics" context. It is illustrated on three representative and complementary experiments: UF COSY, ZF-TOCSY with nonuniform sampling, and HSQC with nonuniform sampling. After giving some details and recommendations on how to apply this protocol, its implementation in the case of targeted and untargeted metabolomics/lipidomics studies is described.

Fast Quantitative 2D NMR for Untargeted and Targeted Metabolomics.

Nuclear magnetic resonance (NMR) spectroscopy exhibits a great potential for the quantitative analysis of complex biological samples such as those encountered in metabolomics. To overcome the ubiquitous problem of overlapping peaks in 1D NMR spectra of complex mixtures, acquisition of 2D NMR spectra allows a better separation between overlapped resonances while yielding accurate quantitative data when appropriate analytical protocols are implemented. The experiment duration can be made compatible with high-throughput studies on large sample collections by relying on fast acquisition methods. Here, we describe the general metabolomics workflow to acquire fast quantitative 2D NMR data with a focus on targeted or untargeted analyses.

Nuclear magnetic resonance structure-based drug design.

Nuclear magnetic resonance structure-based drug design.

Quantitative 1H-1H Distances in Protonated Solids by Frequency-Selective Recoupling at Fast Magic Angle Spinning NMR.

Nuclear magnetic resonance (NMR) spectroscopy of protons in protonated solids is challenging. Fast magic angle spinning (MAS) and homonuclear decoupling schemes, in conjunction, with high magnetic fields have improved the proton resolution. However, experiments to quantitatively measure 1H-1H distances still remain elusive due to the dense proton-proton dipolar coupling network. A novel MAS solid-state NMR pulse sequence is proposed to selectively recouple and measure interproton distances in protonated samples. The phase-modulated sequence combined with a judicious choice of transmitter frequency is used to measure quantitative 1H-1H distances on the order of 3 Å in l-histidine·HCl·H2O, despite the presence of other strongly coupled protons. This method provides a major boost to NMR crystallography approaches for structural determination of pharmaceutical molecules by directly measuring 1H-1H distances. The band-selective nature of the sequence also enables observation of selective 1H-1H correlations (e.g., HN-HN/HN-Hα/ΗΝ-ΗMethyl) in peptides and proteins, which should serve as useful restraints in structure determination.

A multidimensional 1H NMR lipidomics workflow to address chemical food safety issues.

Although it is still at a very early stage compared to its mass spectrometry (MS) counterpart, proton nuclear magnetic resonance (NMR) lipidomics is worth being investigated as an original and complementary solution for lipidomics. Dedicated sample preparation protocols and adapted data acquisition methods have to be developed to set up an NMR lipidomics workflow; in particular, the considerable overlap observed for lipid signals on 1D spectra may hamper its applicability. The study describes the development of a complete proton NMR lipidomics workflow for application to serum fingerprinting. It includes the assessment of fast 2D NMR strategies, which, besides reducing signal overlap by spreading the signals along a second dimension, offer compatibility with the high-throughput requirements of food quality characterization. The robustness of the developed sample preparation protocol is assessed in terms of repeatability and ability to provide informative fingerprints; further, different NMR acquisition schemes-including classical 1D, fast 2D based on non-uniform sampling or ultrafast schemes-are evaluated and compared. Finally, as a proof of concept, the developed workflow is applied to characterize lipid profiles disruption in serum from β-agonists diet fed pigs. Our results show the ability of the workflow to discriminate efficiently sample groups based on their lipidic profile, while using fast 2D NMR methods in an automated acquisition framework. This work demonstrates the potential of fast multidimensional 1H NMR-suited with an appropriate sample preparation-for lipidomics fingerprinting as well as its applicability to address chemical food safety issues.

Ion dynamics in a new class of materials: nanoglassy lithium alumosilicates

In many cases nanocrystalline materials, prepared through high-energy ball milling, reveal enhanced ion dynamics when compared to the situation in the coarse-grained analogues. This effect, which has particularly been seen for lithium alumosilicates, has been ascribed to structural disorder, i.e., the introduction of defect sites during mechanical treatment. Much less is, however, known about ion transport in nanostructured amorphous materials, e.g., nanoglassy compounds, which are regarded as a new class of functional materials. Following earlier studies on nanoglassy lithium alumosilicates and borates, here we studied ion dynamics in nanoglassy petalite LiAlSi4O10. While conductivity spectroscopy unequivocally reveals that long-range ion dynamics in nanoglassy LiAlSi4O10 decreases upon milling, local dynamics, sensed by 7Li nuclear magnetic resonance (NMR) spin-lattice relaxation, points to enhanced Li ion mobility compared to the non-treated glass. Most likely, as for nanocrystalline ceramics also for nanoglassy samples a heterogeneous structure, consisting of bulk and interfacial regions, is formed. For LiAlSi4O10 these interfacial regions, characterized by a higher degree of free volume, might act as hosts for spins experiencing fast longitudinal NMR relaxation. Obviously, these regions do not form a through-going network, which would allow the ions to move over long distances as quickly as in the unmilled glass.

Degradation of the Formamidinium Cation and the Quantification of the Formamidinium–Methylammonium Ratio in Lead Iodide Hybrid Perovskites by Nuclear Magnetic Resonance Spectroscopy

The highest efficiency in perovskite solar cells is currently achieved with mixed-cation hybrid perovskites. The ratio in which the cations are present in the perovskite structure has an important effect on the optical properties and the stability of these materials. At present, the formamidinium cation is an integral part of many of the highest efficiency perovskite systems. In this work, we introduce a nuclear magnetic resonance (NMR) spectroscopy protocol for the identification and differentiation of mixed perovskite phases and of a secondary phase due to formamidinium degradation. The influence of the cooling rate used in a precipitation method on the FA/MA ratio in formamidinium–methylammonium lead iodide perovskites (FAxMA1–xPbI3) was investigated and compared to the FA/MA ratio in thin films. In order to obtain the FA/MA ratio from fast and accessible liquid-state 1H NMR spectra, the influence of the acidity of the solution on the line width of the resonances was elucidated. The ratio of the organi...

Accelerating NMR-Based Structural Studies of Proteins by Combining Amino Acid Selective Unlabeling and Fast NMR Methods

In recent years, there has been a growing interest in fast acquisition and analysis of nuclear magnetic resonance (NMR) spectroscopy data for high throughput protein structure determination. Towards this end, rapid data collection techniques and methods to simplify the NMR spectrum such as amino acid selective unlabeling have been proposed recently. Combining these two approaches can speed up further the structure determination process. Based on this idea, we present three new two-dimensional (2D) NMR experiments, which together provide 15N, 1HN, 13Cα, 13Cβ, 13C′ chemical shifts for amino acid residues which are immediate C-terminal neighbors (i + 1) of residues that are selectively unlabeled. These experiments have high sensitivity and can be acquired rapidly using the methodology of G-matrix Fourier transform (GFT) NMR spectroscopy combined with non-uniform sampling (NUS). This is a first study involving the application of fast NMR methods to proteins samples prepared using a specific labeling scheme. Taken together, this opens up new avenues to using the method of selective unlabeling for rapid resonance assignment of proteins.

How to face the low intrinsic sensitivity of 2D heteronuclear NMR with fast repetition techniques: go faster to go higher!

Nuclear Magnetic Resonance (NMR) is one of the most widely used analytical techniques in numerous domains where molecules are objects of investigation. However, major limitations of multidimensional NMR experiments come from their low sensitivity and from the long times needed for their acquisition. In order to overcome such limitations, fast repetition NMR techniques allowed for the reduction of 2D experimental time and for the conversion of the gained time into a higher number of scans leading to a better sensitivity. Since 2012, Dr. Jonathan FARJON developed new NMR techniques based on fast-pulsing methods to compensate the inherent low sensitivity per unit of time of NMR. Thus, he was contributing for new advances in NMR especially with fast repetition 2D heteronuclear NMR techniques. New opportunities are highlighted with fast repetition techniques: the access to information on low abundant nuclei, the enhancement of the detection for low concentrated compounds and the probing of weak interactions like hydrogen bonds at natural abundance.

Fast 2D NMR Spectroscopy for In vivo Monitoring of Bacterial Metabolism in Complex Mixtures.

The biological toolbox is full of techniques developed originally for analytical chemistry. Among them, spectroscopic experiments are very important source of atomic-level structural information. Nuclear magnetic resonance (NMR) spectroscopy, although very advanced in chemical and biophysical applications, has been used in microbiology only in a limited manner. So far, mostly one-dimensional 1H experiments have been reported in studies of bacterial metabolism monitored in situ. However, low spectral resolution and limited information on molecular topology limits the usability of these methods. These problems are particularly evident in the case of complex mixtures, where spectral peaks originating from many compounds overlap and make the interpretation of changes in a spectrum difficult or even impossible. Often a suite of two-dimensional (2D) NMR experiments is used to improve resolution and extract structural information from internuclear correlations. However, for dynamically changing sample, like bacterial culture, the time-consuming sampling of so-called indirect time dimensions in 2D experiments is inefficient. Here, we propose the technique known from analytical chemistry and structural biology of proteins, i.e., time-resolved non-uniform sampling. The method allows application of 2D (and multi-D) experiments in the case of quickly varying samples. The indirect dimension here is sparsely sampled resulting in significant reduction of experimental time. Compared to conventional approach based on a series of 1D measurements, this method provides extraordinary resolution and is a real-time approach to process monitoring. In this study, we demonstrate the usability of the method on a sample of Escherichia coli culture affected by ampicillin and on a sample of Propionibacterium acnes, an acne causing bacterium, mixed with a dose of face tonic, which is a complicated, multi-component mixture providing complex NMR spectrum. Through our experiments we determine the exact concentration and time at which the anti-bacterial agents affect the bacterial metabolism. We show, that it is worth to extend the NMR toolbox for microbiology by including techniques of 2D z-TOCSY, for total “fingerprinting” of a sample and 2D 13C-edited HSQC to monitor changes in concentration of metabolites in selected metabolic pathways.

How to face the low intrinsic sensitivity of 2D heteronuclear NMR with fast repetition techniques: go faster to go higher!

Nuclear magnetic resonance (NMR) is one of the most widely used analytical techniques in numerous domains where molecules are objects of investigation. However, major limitations of multidimensional NMR experiments come from their low sensitivity and from the long times needed for their acquisition. In order to overcome such limitations, fast repetition NMR techniques allowed for the reduction of 2D experimental time and for the conversion of the gained time into a higher number of scans leading to a better sensitivity. Thus, fast repetition 2D heteronuclear NMR techniques have allowed new advances in NMR, especially to access infomation on low abundant nuclei, to enhance the detection of low concentrated compounds and to probe weak interactions like hydrogen bonds at natural abundance. Copyright © 2017 John Wiley & Sons, Ltd.

Modern solid state NMR techniques and concepts in structural studies of synthetic polymers

In this mini-review, we present the solid state nuclear magnetic resonance (NMR) methods which trace the new tendency in structural studies of synthetic polymers. The review is organized into three sections. In the first part, short theoretical background and introduction to very fast magic angle spinning NMR technique with sample rotation 60 kHz are shown. The second part presents method for enhancing the sensitivity of NMR experiments by application of dynamic nuclear polarization magic angle spinning technique. In the third section, the power of the NMR crystallography approach which can be used for fine refinement of polymers structure on the atomic level is discussed. Copyright © 2016 John Wiley & Sons, Ltd.