Magnetic Resonance in Chemistry

doi: 10.1002/mrc.70107pmid: N/A

No abstract is available for this article.

Jacob, D.; Falourd, X.; Deborde, C.; Lahaye, M.; Rondeau‐Mouro, C.

doi: 10.1002/mrc.70090pmid: 41802925

Quantitative analysis of solid‐state NMR data, based on magic‐angle spinning with cross‐polarization experiments (CP‐MAS), often requires extensive signal processing, from the transformation of raw time‐domain data (FIDs) to the extraction of quantitative data and the modelling of signal intensity kinetics. Many current workflows rely on semi‐manual peak fitting and heterogeneous tools across laboratories for intensity curve modelling, limiting reproducibility and throughput. In this work, we propose a fully reproducible and open workflow combining two key methodological approaches: (1) an adaptive bucketing approach, extraction of relevant variables for analysis (ERVA), implemented in NMRProcFlow application, to automatically segment 13C spectra into chemically relevant spectral regions; and (2) an online modelling platform that allows users to fit intensity curves over contact time with multiple models, guided by objective indicators including fit quality scores and parameter sensitivity metrics. This integrated approach provides a fast, user‐friendly and transparent path from FIDs to kinetic model parameters, opening new perspectives for reproducible quantitative solid‐state NMR.

Sterner, Patrick; Hach, Mery; Berg, Regina; Stock, Christoph; Holland, Daniel; Harbou, Erik

doi: 10.1002/mrc.70091pmid: 41805072

Heterogeneously catalyzed hydrogenations are pivotal in the chemical industry. Studying these reactions often demands significant experimental effort due to safety requirements, elevated pressures and temperatures, and the operational modes of traditional laboratory reactors. To address these challenges, we propose an automated, efficient, and cost‐effective method for characterizing such reactions within a kinetic laboratory setting. Utilizing benchtop NMR as a noninvasive, automatable analytical tool offers advantages in terms of space and cost over high‐frequency NMR, though its limited spectral resolution may restrict applicability to certain reaction systems. In this study, we investigate the hydrogenation of 2‐methyl‐3‐butyn‐2‐ol (MBY) to 2‐methyl‐3‐buten‐2‐ol (MBE) as a model reaction. While literature provides extensive data on the main components, the formation of side products remains inadequately explained. Conducting the reaction in a batch reactor, we assess the detection and quantification of side products. Samples withdrawn during hydrogenation are analyzed using benchtop NMR coupled with a quantum‐mechanical Bayesian quantitative NMR analysis, employing component knowledge to quantify mixtures through mathematical modeling. We collect kinetic data, gaining both qualitative and quantitative insights into the reaction network at temperatures up to 80°C and a pressure of 10 bar. Our findings demonstrate that the reaction mixture's composition can be quantitatively monitored in real‐time, facilitating the derivation of kinetic parameters. Despite the minor formation of various side products, we successfully quantify dimeric reaction products and evaluate process parameters influencing their formation. The integration of a reactor, online benchtop NMR, and advanced qNMR data analysis yields high‐quality results essential for process optimization.

Kohda, Daisuke; Hayashi, Seiichiro; Furuita, Kyoko; Kojima, Chojiro

doi: 10.1002/mrc.70095pmid: 41839778

Trandolapril, an angiotensin‐converting enzyme (ACE) inhibitor, undergoes two‐state exchange in organic solvents arising from cis–trans isomerization around a N–C bond. A previous NMR study reported different equilibrium constants depending on which 1H nuclei were used for analysis. Such variations have been attributed to experimental error but require experimental resolution. In this study, we developed a new method for measuring cross‐peak volumes based on a projection technique and applied the method to a series of two‐dimensional 1H–13C HSQC spectra of trandolapril, acquired using the time‐zero HSQC (HSQC0) scheme. The Proj‐Vol method yielded consistent equilibrium constant values across multiple 1H nuclei, demonstrating that trandolapril has a single equilibrium constant, consistent with its single exchange mechanism. The Proj‐Vol method is based on constructing 1D 13C projections of narrow rectangular regions around the cross‐peaks. The use of 1D projection provides several advantages, including fewer fitting parameters and the elimination of the need to consider peak splitting due to 1H homonuclear J‐couplings. It also offers other useful benefits, such as a narrower projection box size to reduce the contributions of other diagonally overlapping cross‐peaks in 2D HSQC spectra, the improved signal‐to‐noise ratio of projection spectra by slice summation, and the cancellation of dispersion components caused by spectral misphasing in the 1H dimension. These advantages and benefits increase the accuracy of cross‐peak volume determination in 2D HSQC spectra, compared with existing methods that directly fit 2D cross‐peak shapes.

Ok, Salim; Fazlyyyakhmatov, Marsel

doi: 10.1002/mrc.70096pmid: 41833342

Reliable characterization of crude oil properties remains a central task in petroleum research and industry. Conventional methods established by ASTM standards are time‐consuming and rely on toxic reagents, which have motivated the development of alternative approaches. Low‐field nuclear magnetic resonance (LF‐NMR) has emerged as one such method, offering low cost, simple operation, and minimal sample preparation. In this review, we summarize recent progress in applying LF‐NMR relaxometry to crude oils and their fractions. Particular attention is given to correlations between relaxation times, viscosity, and density, and to the use of machine learning techniques to improve the prediction of these parameters. Applications to crude oil emulsions are also considered, where LF‐NMR provides insights into droplet size distributions, phase composition, and stability. Finally, advances in SARA analysis are discussed, including new approaches that extend LF‐NMR characterization to complex water–oil systems. Together, these studies demonstrate that LF‐NMR relaxometry is a versatile tool with strong potential for rapid, nondestructive analysis, contributing to both laboratory characterization and practical flow assurance in petroleum production.

Costantino, Azzurra; Barbieri, Letizia; Giovannuzzi, Simone; Nocentini, Alessio; Supuran, Claudiu T.; Luchinat, Enrico

doi: 10.1002/mrc.70098pmid: 41866847

Drug development is a risky endeavour with a high failure rate, often caused by the limited ability to predict the efficacy and interactions of candidate drugs in a native cellular environment. In this context, in‐cell NMR spectroscopy is a promising tool for assessing drug‐target binding directly in living cells, thereby improving the screening and development of new molecules. In this study, we used real‐time in‐cell 19F NMR spectroscopy in a flow bioreactor to observe competitive binding of fluorinated benzenesulfonamide derivatives to three cytosolic isoforms of carbonic anhydrase. Quantitative measurement of the dissociation constants relative to a spy ligand allowed an accurate ranking of the compounds based on their intracellular affinities for each isoform. The use of two fluorinated ligands allowed simultaneous observation of spy ligand displacement and test ligand binding, as well as estimation of the effective ratio of free ligand concentrations under poor solubility conditions. We also show that signal saturation caused by short repetition times, which can significantly impact the analysis, can be easily corrected a posteriori. Overall, we show that real‐time in‐cell 19F NMR spectroscopy can reliably quantify drug–target binding in the cellular environment, paving the way for future applications in drug discovery.