Two-Dimensional Laplace NMR Reconstruction through Deep Learning Enhancement.

Laplace NMR is a powerful tool for studying molecular dynamics and spin interactions, providing diffusion and relaxation information that complements Fourier NMR used for composition determination and structure elucidation. However, Laplace NMR demands sophisticated signal processing algorithms such as inverse Laplace transform (ILT). Due to the inherently ill-posed nature of ILT problems, it is generally challenging to perform satisfactory Laplace NMR processing and reconstruction, particularly for two-dimensional Laplace NMR.

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.

SCREENES: Enhancing non-uniform sampling reconstruction for symmetrical NMR spectroscopy.

Background: Symmetrical NMR spectroscopy, such as Total Correlation Spectroscopy (TOCSY) and other homonuclear spectroscopy, displays symmetry in chemical shift but are generally not symmetrical in terms of intensity, which constitutes a pivotal branch of multidimensional NMR spectroscopy and offers a robust tool for elucidating the structures and dynamics of complex samples, particularly in the context of biological macromolecules. Non-Uniform Sampling (NUS) stands as a critical technique for accelerating multidimensional NMR experiments. However, symmetrical NMR spectroscopy inherently presents dynamic peak intensities, where cross peaks tend to be substantially weaker compared to diagonal peaks.

GRIN-toolbox: A versatile and light toolbox for NMR inversion.

NMR technique serves as a powerful analytical tool with diverse applications in fields such as chemistry, biology, and material science. However, the effectiveness of NMR heavily relies on data post-processing which is often modeled as regularized inverse problem. Recently, we proposed the Generally Regularized INversion (GRIN) algorithm and demonstrated its effectiveness in NMR data processing.

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.

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.

High-resolution diffusion-order NMR spectroscopy in inhomogeneous magnetic fields via intermolecular zero-quantum coherences.

Diffusion-order NMR spectroscopy (DOSY) presents a powerful tool for studying solution mixtures by recording diffusion coefficients of individual components and separating their signals into respective 1D NMR spectra. Existing DOSY experiments, however, are generally unsuitable for measurements under adverse magnetic field conditions, because calculations for diffusion coefficients strictly rely on resolved resonances in the 1D NMR spectral domain. Herein, we propose a general DOSY method by introducing intermolecular zero-quantum coherence mechanism into molecular diffusion evolution to overcome the challenge of magnetic field inhomogeneity and to record high-resolution DOSY spectra free of magnetic field inhomogeneity.

Neural Network Method for Diffusion-Ordered NMR Spectroscopy.

Diffusion-ordered NMR spectroscopy (DOSY) presents an essential tool for the analysis of compound mixtures by revealing intrinsic diffusion behaviors of the mixed components. For the interpretation of the diffusion information, intrinsically designed algorithms for a DOSY spectrum reconstruction are required. The estimated diffusion coefficients are desired to have consistency for all the spectral signals from the same molecule and good separation of signals from different molecules.

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.

A General Reconstruction Method for Multidimensional Sparse Sampling Nuclear Magnetic Resonance Spectroscopy.

Multidimensional NMR spectroscopy provides a powerful tool for structure elucidation and dynamic analysis of complex samples, particularly for biological macromolecules. Multidimensional sparse sampling effectively accelerates NMR experiments while an efficient reconstruction method is generally required for unraveling spectra. Various reconstruction methods were proposed for pure Fourier NMR (only involving chemical shifts and couplings detection).

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.

High-Resolution Reconstruction for Diffusion-Ordered NMR Spectroscopy.

Diffusion-ordered NMR spectroscopy (DOSY) presents an essential tool for the analysis of compound mixtures by revealing intrinsic diffusion behaviors of mixed components. The applicability of DOSY measurements on complex mixtures is generally limited by the performance of data reconstruction algorithms. Here, based on constraints on low rank and sparsity of DOSY data, we propose a reconstruction method to achieve high-resolution DOSY spectra with excellent peak alignments and accurate diffusion coefficients for measurements of complex mixtures even when component signals are congested and mixed together along the spectral dimension.

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.

Dispersive liquid-liquid microextraction combined with acetonitrile stacking through capillary electrophoresis for the determination of three selective serotonin reuptake inhibitor drugs in body fluids.

Dispersive liquid-liquid microextraction was combined with acetonitrile stacking in capillary electrophoresis for the identification of three selective serotonin reuptake inhibitors (citalopram, fluoxetine, and fluvoxamine) in human fluids such as urine and plasma. Parameters that affect the extraction and stacking efficiency, such as the type and volume of the extraction and disperser solvent, extraction time, salt addition for dispersive liquid-liquid microextraction, and sample matrices, pH, and concentration of the separation buffer for stacking, were investigated and optimized. Under optimum conditions, the enrichment factors were in the range of 1195-1441.

Application of strong transverse magneto-optical Kerr effect on high sensitive surface plasmon grating sensors.

A high sensitive sensor is demonstrated by exploiting strong transverse magneto-optical Kerr effect on a ferromagnetic surface plasmon grating. The surface plasmon grating, made of a hybridized Au/Fe/Au layer, exhibits a very dispersive Kerr parameter variation near the surface plasmon polariton (SPP) wavelength via coherent scattering of the SPP on the grating structure. Interrogating this Kerr parameter can be utilized for detecting chemical or biological objects in a fluid medium.

On-line sample preconcentration by sweeping and poly(ethylene oxide)-mediated stacking for simultaneous analysis of nine pairs of amino acid enantiomers in capillary electrophoresis.

This study proposes a sensitive method for the simultaneous separation and concentration of 9 pairs of amino acid enantiomers by combining poly(ethylene oxide) (PEO)-based stacking, β-cyclodextrin (β-CD)-mediated micellar electrokinetic chromatography (MEKC), and 9-fluoroenylmethyl chloroformate (FMOC) derivatization. The 9 pairs of FMOC-derivatized amino acid enantiomers were baseline separated using a discontinuous system, and the buffer vials contained a solution of 150 mM Tris-borate (TB), 12.5% (v/v) isopropanol (IPA), 0.

On-line concentration and separation of cationic and anionic neurochemicals by capillary electrophoresis with UV absorption detection.

This paper presents on-line simultaneous concentration and separation of cationic and anionic neurochemicals by capillary electrophoresis (CE) with UV absorbance spectroscopy. Neurochemical stacking exploits differences in local electric field and viscosity between the sample zone and the background electrolyte (BGE). To achieve these discontinuous conditions for CE, neurochemicals were prepared in a solution containing 1mM formic acid and 20% (v/v) acetonitrile (ACN).

Stacking and separation of aspartic acid enantiomers under discontinuous system by capillary electrophoresis with light-emitting diode-induced fluorescence detection.

We describe the stacking and separation of d- and l-aspartic acid (Asp) by capillary electrophoresis (CE) with light-emitting diode-induced fluorescence detection (LEDIF). In the presence of cyanide, d- and l-Asp were derivatized with naphthalene-2,3-dicarboxaldehyde (NDA) to form fluorescent derivatives prior to CE-LEDIF. The separation of NDA-derivatized d- and l-Asp was accomplished using a discontinuous system - buffer vials contained a solution of 0.