Magnetic Resonance in Chemistry最新文献

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.

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.

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.

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.

A recurring and significant finding across diverse biological and macromolecular systems is that the frequency dependence of the spin-lattice relaxation rate often cannot be well fitted with a single correlation time but rather follows a power-law function. This power-law dependence is attributed to the dynamics of rare, strongly bound water molecules trapped on rugged macromolecular surfaces, with a Pareto distribution of correlation times. Here, we show that power-law dependences naturally emerge from a broad distribution of correlation times with weighting factors proportional to 1/τ^(1-α). We derive analytical expressions for limiting cases and perform numerical simulations demonstrating that this distribution of correlation times generates power-law exponents closely matching α over wide frequency windows. We validate this framework by fitting Nuclear Magnetic Relaxation Dispersion (NMRD) profiles of sedimented proteins, biological tissues, cross-linked hydrogels, and protein solutions. This approach establishes a physical interpretation of power-law relaxation, enabling the extraction of dynamic information otherwise inaccessible.

A fast field-cycling NMR (FFC NMR) experiment measures the longitudinal (spin-lattice) relaxation rate as a function of applied magnetic field to yield a relaxation rate dispersion curve, $R_1\left(f\right),$ where $f$ is the proton Larmor frequency. The $R_1\left(f\right)$ dispersions, or NMRD profiles, are exquisitely sensitive to the relative dynamics of proton spins across timescales in the range 10^-9-10^-4 s. These timescales span the translational and rotational dynamics of proton-bearing fluids to dynamics at the surfaces of solids, soft material and macromolecules. FFC NMR is useful for studying fluid-filled rocks and soils, porous silica and cementitious material, polymer systems, foodstuffs, protein systems, biological tissues and biofluids. The NMRD profiles are rich with information, but interpretation is challenging. A parametrized relaxometry model must generate an NMRD profile $R_1\left(f\right)$ that can be fit to experimental data across three to four orders of magnitude of frequency. The 3-Tau model has emerged as a model capable of fitting NMRD profiles from a broad range of material types yielding physically meaningful parameters. This review article demonstrates power of the FFC NMR experiment interpreted using the 3-Tau model to reveal properties of hydrated hard and soft material.

Samples of a layered α-Sn (IV) phosphate were partially deuterated by soaking with D₂O to yield a mixture of two isotopomers Sn (HPO₄) (DPO₄).c-H₂O and Sn (DPO₄)₂.c-H₂O containing cavity water c-H₂O. They were characterized by the ¹H, ²H, ³¹P, and ¹¹⁹Sn MAS NMR experiments including relaxation time measurements. The formation of these isotopomers is proven by the kinetic proton-deuterium cross-polarization MAS NMR experiments giving the cross-polarization rate constant T_H-D of 3.2 ms. In agreement with their formulation the ²H MAS NMR spectra of Sn (HPO₄) (DPO₄).c-H₂O and Sn (DPO₄)₂.c-H₂O did not display the other signals besides the DPO₄ resonance. The DPO₄ groups observed in the temperature-independent ²H MAS NMR spectra show the DQCC value of 184 ± 6 kHz corresponding to hydrogen bonds formed with an O^… O distance estimated as ~2.7 Å. Because of reduced dipolar interactions in the deuterated samples, the ¹H MAS NMR spectra are well resolved providing signal assignments and the analysis. According to the solid-state NMR data collected for the partially deuterated samples of SnP, the cavity water accepts one hydrogen bond from the P-OH donor group and forms one hydrogen bond with the neighboring phosphate group, while the other water hydrogen is not involved in hydrogen bonding.

Phase change materials (PCMs) are useful for energy storage. Large latent heats make n-alkane waxes effective PCMs. Octadecane and eicosane were studied as both unencapsulated and microencapsulated with NMR relaxometry and diffusometry throughout melting. Each wax was stepped degree-wise through its melting point. At each temperature point, NMR $T_2$ relaxation, $T_1-T_2$ correlated relaxation, and PGStE measurements as a function of molecular migration time $\Delta$ were acquired. These measurements detected molecular-level disordering due to microencapsulation. NMR relaxometry measurements on the solid waxes measured a major intermediate mobility domain in the microencapsulated waxes. This pseudo interface domain in the solid microencapsulated waxes is consistent with the disruption of long-range order, increasing heterogeneity and extending the melting point range. NMR diffusometry measured diffusion coefficients $D$ in the unencapsulated wax melts, matching values established in the literature. $D$ as a function of $1/T$ was fit for Arrhenius activation energies $E_a$ as a function of $\Delta$ for each stage of melting. $D$ values are reported for each solid wax sample, which are not measurements of only molecular diffusion but are consistent with magnetization transport via spin diffusion.

Two compounds, including one new alkaloid, namely, aspergilloid A (1), together with one known compound (2), were isolated from the sponge-derived fungus Aspergillus sp. MF01. The structure of one undescribed compound was elucidated by NMR, HRESIMS, and electronic circular dichroism (ECD) diffraction spectroscopy. All the isolated compounds were evaluated for their antimicrobial activities. Compound 1 showed weak antibacterial activity against Gram-positive bacteria Staphylococcus aureus and Staphylococcus epidermidis at 1 mg/mL.