Magnetic Resonance in Chemistry最新文献

Quantitative nuclear magnetic resonance (qNMR) can determine the concentration of compounds in solution with remarkable trueness and precision, if the experimental conditions are chosen correctly. However, some users still have difficulty with the correct implementation of these requirements. Knowing which requirements are mandatory and which can be neglected for a given accuracy is one of the major problems. Failure to follow basic requirements s will lead to unreliable results. On the other hand, avoiding unnecessary constraints—for the desired level of trueness and/or precision—can save precious time. The aim of this tutorial is therefore to review in the second section the basic principles of quantitative NMR and explain the impact of different acquisition and processing conditions on trueness and precision. These general guidelines provide both precision and trueness of 1%. To reach 1‰, one has to optimize further their experimental conditions and consider the instrumental imperfections.

Over the past 20 years, the use of quantitative nuclear magnetic resonance (qNMR) technology has grown significantly in pharmaceutical industry. However, its broader adoption is often limited by specialized expertise required to implement best practices. Recent discussions within the qNMR community in China (qNMR-C) have highlighted the benefits of establishing an applicable qNMR platform method—one that serves as a universal approach, adaptable across multiple products. This approach aims to standardize a single set of qNMR parameters to address the majority of quantitative applications and making qNMR more accessible, particularly for researchers new to the field. The present study outlines the rationale behind the proposed qNMR platform method and demonstrates its strategic framework through a series of designed tests. Key parameters influencing qNMR accuracy and precision, including signal-to-noise ratio, data processing, integration approaches, relaxation delays, T₁ relaxation times, and sample weight, were systematically evaluated. A collaborative effort involving 12 NMR instruments across eight laboratories assessed the method's applicability and demonstrated its proper design space. Another objective of this study is to streamline the qNMR workflow, enabling novices to produce reliable, high-quality data early in their learning while ensuring reproducible and meaningful results. Furthermore, this work calls upon the global qNMR community to engage in the continued validation of the proposed platform method, fostering collective knowledge and verifying its robustness across diverse applications.

Statistical analysis of backbone ¹³C^α and ¹⁵N chemical shielding tensors (CST) computed using the DFT-GIAO method is presented for 40 alanine residues located centrally in three-residue segments extracted from α-helical and β-sheet regions of 12 proteins with high-resolution crystal structures. Our results show that the projections of ¹³C^α shielding along the three covalent bond directions, C^α–C^β, C^α–H^α, and C^α–N, exhibit significantly higher sensitivity to secondary structure than the principal components. The increased sensitivity is due to the changes in the orientation of ¹³C^α CST in the molecular frame of the two secondary structures. Similarly, the projections of backbone amide ¹⁵N shielding along the covalent bonds N–H, N–C^α and along the normal direction to the peptide plane have a reasonably higher sensitivity to the secondary structure than the principal components. Unlike ¹³C^α nuclei, the orientation of amide ¹⁵N CST in the molecular frame is found to be invariant in the two secondary structures. The calculated amide ¹⁵N chemical shielding anisotropies (CSA) are larger in helix than in sheet structure, consistent with the experimental ¹⁵N chemical shift studies reported in the literature. Furthermore, two-dimensional correlation plots of backbone ¹³C^α and ¹⁵N CSA parameters show a clear distinction between the two major secondary structure elements.

Multiple 2D HSQC NMR methods have been developed for the absolute quantitation of metabolites in complex mixtures without need for external calibration or standard additions. However, analytical comparison between these methods is lacking. This study aims at comparing the performance of two “intrinsically quantitative” heteronuclear methods for the targeted quantitation of metabolite mixtures: HSQC₀ and Q QUIPU HSQC. Each method was applied on a model metabolite mixture in the same total experiment time. Both methods were accelerated with non-uniform sampling (NUS), then further accelerated by combining NUS with variation of the pulse sequence repetition time (VRT). Multiple analytical metrics were evaluated and compared for quantitation, including trueness, repeatability, and sensitivity. Globally, accelerated versions of the pulse sequences, using NUS and VRT, performed better than NUS-only acquisitions. On the one hand, provided enough sensitivity is achieved, better performance was observed for HSQC₀, which also appears as a more user-friendly technique. On the other hand, Q QUIPU HSQC was shown to be more repeatable and more sensitive.

Determining the constitution and configuration is a critical step in characterizing the structure of small molecules. In addition to the classical nuclear magnetic resonance (NMR) method conducted in isotropic solutions, the emerging anisotropic NMR parameters such as residual dipolar couplings (RDCs) were also employed to clarify the structures of organic molecules. These RDCs not only confirmed that the unexpectedly synthesized product was a quinazolinone but also validated the relative configuration of the diastereoisomeric hydroindole in a polyarylisocyanide lyotropic liquid crystalline solution through the induction of anisotropy. Singular value decomposition (SVD) was employed to fit the experimental RDC data against the low-energy conformational sets of an unexpected synthetic product, which were calculated using density functional theory (DFT). This analysis aimed to identify the correct molecular connection sites. Furthermore, the method was applied to determine the correct relative configuration between two possible diastereoisomers.

Synthetic and commercial carboxymethyl cellulose (CMC) sodium salts were analyzed by CP-MAS ¹³C-NMR and plasma emission analysis. CMC was synthesized in the lab from cotton alkali cellulose and sodium chloroacetate. The progressive changes in the chemical composition and morphology (and reflected in the degree of substitution, DS) were followed by solid-state NMR. Observed differences in solid-state NMR spectra are discussed in terms of DS, chemical composition, dynamics, and polymorphism. The effect of molecular weight in CMC morphology and dynamics can be assessed by small differences observed in peak shapes and peak positions in the CP-MAS spectra.