Highly Efficient Analysis on Biomass Carbohydrate Mixtures by DREAMTIME NMR Spectroscopy.

Proton (H) NMR spectroscopy presents a powerful tool for biomass mixture studies by revealing the involved chemical compounds with identified ingredients and molecular structures. However, conventional H NMR generally suffers from spectral congestion when measuring biomass mixtures, particularly biomass carbohydrate samples, that contain various physically and chemically similar compounds. In this study, a targeted detection NMR approach, DREAMTIME, is exploited for studying biomass carbohydrate mixtures by spectroscopically targeting the desired compounds in separate 1D NMR spectra.

Combining Fast Pure Shift NMR and GEMSTONE-Based Selective TOCSY for Efficient NMR Analysis of Complex Systems.

As one of the commonly used intact detection techniques, liquid NMR spectroscopy offers unparalleled insights into the chemical environments, structures, and dynamics of molecules. However, it generally encounters the challenges of crowded or even overlapped spectra, especially when probing complex sample systems containing numerous components and complicated molecular structures. Herein, we exploit a general NMR protocol for efficient NMR analysis of complex systems by combining fast pure shift NMR and GEMSTONE-based selective TOCSY.

A rotating beam-blocker method for cone beam CT scatter correction.

Background: Cone beam CT (CBCT) is widely utilized in clinics. However, the scatter artifact degrades the CBCT image quality, hampering the expansion of CBCT applications. Recently, beam-blocker methods have been used for CBCT scatter correction and proved their high cost-effectiveness.

Fast Pure Shift NMR Spectroscopy Using Attention-Assisted Deep Neural Network.

Pure shift NMR spectroscopy enables the robust probing on molecular structure and dynamics, benefiting from great resolution enhancements. Despite extensive application landscapes in various branches of chemistry, the long experimental times induced by the additional time dimension generally hinder its further developments and practical deployments, especially for multi-dimensional pure shift NMR. Herein, this study proposes and implements the fast, reliable, and robust reconstruction for accelerated pure shift NMR spectroscopy with lightweight attention-assisted deep neural network.

Pure-Shift-Based Proton Magnetic Resonance Spectroscopy for High-Resolution Studies of Biological Samples.

Proton magnetic resonance spectroscopy (H MRS) presents a powerful tool for revealing molecular-level metabolite information, complementary to the anatomical insight delivered by magnetic resonance imaging (MRI), thus playing a significant role in in vivo/in vitro biological studies. However, its further applications are generally confined by spectral congestion caused by numerous biological metabolites contained within the limited proton frequency range. Herein, we propose a pure-shift-based H localized MRS method as a proof of concept for high-resolution studies of biological samples.

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.

Efficient determination of scalar coupling networks by band selective decoupled 2D NMR spectroscopy.

Scalar (J) couplings constitute one of vital features observed in NMR spectroscopy and show valuable information for molecular structure elucidation and conformation analysis. However, existing J coupling measurement techniques are generally confined by the concerns of resolution, SNR, and experimental efficiency. Herein, we exploit an efficient 2D NMR protocol to deal with the above concerns by enabling rapid, sensitive, and high-resolution J coupling extraction.

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.

Graph Neural Tree: A novel and interpretable deep learning-based framework for accurate molecular property predictions.

Determining various properties of molecules is a critical step in drug discovery. Recently, with the improvement of large heterogeneous datasets and the development of deep learning approaches, more and more scientists have turned their attention to neural network-based virtual preliminary screening to reduce the time and monetary cost of drug discovery. However, the poor interpretability of deep learning masks causality, so models' conclusions are often beyond the comprehension of human users, which reduces the credibility of the model and makes it difficult for chemists to further narrow the huge chemical space based on models' results.

A Pure Shift-Based Nuclear Magnetic Resonance Method for In-Phase Three-Dimensional Diffusion-Ordered Spectroscopy.

Diffusion-ordered nuclear magnetic resonance spectroscopy (DOSY) plays a vital role in mixture studies. However, its applications to complex mixture samples are generally limited by spectral congestion along the chemical shift domain caused by extensive coupling networks and abundant compounds. Herein, we develop the in-phase multidimensional DOSY strategy for complex mixture analyses by simultaneously revealing molecular self-diffusion behaviors and multiplet structures with optimal spectral resolution.

Adaptable Singlet-Filtered Nuclear Magnetic Resonance Spectroscopy for Chemical and Biological Applications.

Proton nuclear magnetic resonance (H NMR) spectroscopy presents a powerful detection tool for studying chemical compositions and molecular structures. In practical chemical and biological applications, H NMR experiments are generally confronted with the challenge of spectral congestions caused by abundant observable components and intrinsic limitations of a narrow frequency distribution range and extensive coupling splitting. Herein, a one-dimensional (1D) general NMR method is proposed to individually extract the signals of targeted proton groups based on their endogenous spin singlet states excited from coupling interactions, and it is suitable for high-resolution detections on complex chemical and biological samples.

Simultaneous determination of multiple coupling networks by high-resolution 2D J-edited NMR spectroscopy.

J coupling constitutes an important NMR parameter for molecular-level composition analysis and conformation elucidation. Dozens of J-based approaches have been exploited for J coupling measurement and coupling network determination, however, they are generally imposed to insufficient spectral resolution to resolve crowded NMR resonances and low measurement efficiency that a single experiment records one J coupling network. Herein, we propose a general NMR method to collect high-resolution 2D J-edited NMR spectra, which are characterized with advantages of pure absorptive lineshapes, decoupled chemical shift dimension, as well as eliminated axial peaks, thus facilitating J coupling partner assignments and J coupling constant measurements.

Multiplet analysis by strong-coupling-artifact-suppression 2D J-resolved NMR spectroscopy.

Benefitting from the capability of recording scalar (J) couplings and bonding information, 2D J-resolved NMR spectroscopy constitutes an important tool for molecular structure analysis and mixture component identification. Unfortunately, conventional 2D J-resolved experiments generally encounter challenges of insufficient spectral resolution and strong coupling artifacts. In this study, a general NMR approach is exploited to record absorption-mode artifact-free 2D J-resolved spectra.

High-Resolution Reconstruction for Multidimensional Laplace NMR.

As a perfect complement to conventional NMR that aims for chemical structure elucidation, Laplace NMR constitutes a powerful technique to study spin relaxation and diffusion, revealing information on molecular motions and spin interactions. Different from conventional NMR adopting Fourier transform to deal with the acquired data, Laplace NMR relies on specially designed signal processing and reconstruction algorithms resembling the inverse Laplace transform, and it generally faces severe challenges in cases where high spectral resolution and high spectral dimensionality are required. Herein, based on the tensor technique for high-dimensional problems and the sparsity assumption, we propose a general method for high-resolution reconstruction of multidimensional Laplace NMR data.

Diffusion Analysis on Complex Mixtures under Adverse Magnetic Field Conditions by Spatially-Selective Pure Shift-Based DOSY.

Diffusion-ordered NMR spectroscopy (DOSY) serves as a noninvasive spectroscopic method for studying intact mixtures and identifying individual components present in mixtures according to their diffusion behaviors. However, DOSY techniques generally fail to discriminate complex compositions which exhibit crowded or overlapped NMR signals, particularly under adverse magnetic field conditions. Herein, we exploit the spatially selective pure shift-based DOSY strategy to address this challenge by eliminating inhomogeneous line broadenings and extracting pure shift singlets, thereby expediting diffusion analyses on complex mixtures.

Highly Efficient Determination of Complex NMR Multiplet Structures in Inhomogeneous Magnetic Fields.

Proton-proton scalar () coupling plays an important role in disentangling molecular structures and spatial conformations. But it is challenging to extract coupling networks from congested H NMR spectra, especially in inhomogeneous magnetic fields. Herein, we propose a general liquid NMR protocol, named HR-G-SERF, to implement highly efficient determination of individual couplings and corresponding coupling networks via simultaneously suppressing effects of spectral congestions and magnetic field inhomogeneity.

NMR Relaxation Measurements on Complex Samples Based on Real-Time Pure Shift Techniques.

Longitudinal spin-lattice relaxation () and transverse spin-spin relaxation () reveal valuable information for studying molecular dynamics in NMR applications. Accurate relaxation measurements from conventional 1D proton spectra are generally subject to challenges of spectral congestion caused by coupling splittings and spectral line broadenings due to magnetic field inhomogeneity. Here, we present an NMR relaxation method based on real-time pure shift techniques to overcome these two challenges and achieve accurate measurements of and relaxation times from complex samples that contain crowded NMR resonances even under inhomogeneous magnetic fields.

High-Resolution Probing of Heterogeneous Samples by Spatially Selective Pure Shift NMR Spectroscopy.

Liquid NMR spectroscopy generally encounters two major challenges for high-resolution measurements of heterogeneous samples, namely, magnetic field inhomogeneity caused by spatial variations in magnetic susceptibility and spectral congestion induced by crowded NMR resonances. In this study, we demonstrate a spatially selective pure shift NMR approach for high-resolution probing of heterogeneous samples by suppressing effects of field inhomogeneity and coupling simultaneously. A Fourier phase encoding strategy is proposed and implemented for spatially selective pure shift experiments to enhance signal intensity and further boost the applicability.

A pure shift and spin echo based approach for high-resolution diffusion-ordered NMR spectroscopy.

Diffusion-ordered NMR spectroscopy (DOSY) can be used for separating mixture components according to their individual diffusion behaviors, thus offering a powerful tool for the analysis of compound mixtures. However, conventional DOSY experiments generally encounter the problem of limited resolution in the spectral domain, particularly for applications to complex mixtures that contains crowed resonances in 1D NMR. In addition, chemical exchange effects, bringing about spurious component signals, pose another limitation for interpreting DOSY measurements.

A Single-Scan Inhomogeneity-Tolerant NMR Method for High-Resolution Two-Dimensional J-Resolved Spectroscopy.

Objective: A robust and general single-scan NMR method, SGEN-J, is proposed for real-time recording high-resolution two-dimensional (2D) homonuclear J-resolved spectra under inhomogeneous magnetic fields.

Methods: This proposed NMR method is designed based on the combination of a selective gradient encoding module to encode chemical shifts with spatial positions, and a J-modulation decoding module to reveal encoded structural information. Multi-band SGEN-J is further implemented to effectively enhance spectral sensitivity with a sustained tolerance of field inhomogeneity.

General Two-Dimensional Absorption-Mode J-Resolved NMR Spectroscopy.

Two-dimensional (2D) J-resolved NMR technique offers a natural solution for disentangling complex mixtures that suffer from crowded spectra in 1D NMR. The applicability of classical 2D J-resolved spectroscopy is inevitably limited by phase-twist lineshapes and strong coupling artifacts. Here, a general and robust NMR method is proposed to record 2D absorption-mode J-resolved spectra in rapid acquisition manner.

Ultrahigh-Resolution NMR Spectroscopy for Rapid Chemical and Biological Applications in Inhomogeneous Magnetic Fields.

NMR spectroscopy is a commonly used analytical technique in practical applications, and its applicability is further promoted by pure chemical shift techniques based on spectral simplification for analyses. Unfortunately, magnetic field inhomogeneity caused by adverse experimental conditions remains an obstacle restricting NMR applications. In this study, we introduce a new NMR method for high-resolution pure shift proton (H) NMR measurements in inhomogeneous magnetic fields.