Magnetic Resonance in Chemistry最新文献

Highly dispersed Ni-TiO₂ catalyst has been studied in the process of preparation and under catalytic transfer hydrogenation reaction conditions in supercritical 2-propanol (250°C, 70 bar) using electron spin resonance in situ. Electron spin resonance in situ has been used to study the process of the catalyst passivation and subsequent reduction of the oxide layer in the gas flow. Reduction of the NiO layer on the surface of passivated Ni nanoparticles has been detected in supercritical 2-propanol, which is in agreement with kinetic modeling data. It has been found that the reduction of the nickel oxide layer in supercritical 2-propanol occurs at a lower temperature compared with the reduction in hydrogen flow, according to in situ electron spin resonance study.

One-dimensional (1D) proton-nuclear magnetic resonance (¹H-NMR) spectroscopy is an established technique for the deconvolution of complex biological sample types via the identification/quantification of small molecules. It is highly reproducible and could be easily automated for small to large-scale bioanalytical, epidemiological, and in general metabolomics studies. However, chemical shift variability is a serious issue that must still be solved in order to fully automate metabolite identification. Herein, we demonstrate a strategy to increase the confidence in assignments and effectively predict the chemical shifts of various NMR signals based upon the simplest form of statistical models (i.e., linear regression). To build these models, we were guided by chemical homology in serum/plasma metabolites classes (i.e., amino acids and carboxylic acids) and similarity between chemical groups such as methyl protons. Our models, built on 940 serum samples and validated in an independent cohort of 1,052 plasma-EDTA spectra, were able to successfully predict the ¹H NMR chemical shifts of 15 metabolites within ~1.5 linewidths (Δv_1/2) error range on average. This pilot study demonstrates the potential of developing an algorithm for the accurate assignment of ¹H NMR chemical shifts based solely on chemically defined constraints.

Prostate cancer (PCa) is one of the most prevalent cancers in men worldwide. For its detection, serum prostate-specific antigen (PSA) screening is commonly used, despite its lack of specificity, high false positive rate, and inability to discriminate indolent from aggressive PCa. Following increases in serum PSA levels, clinicians often conduct prostate biopsies with or without advanced imaging. Nuclear magnetic resonance (NMR)-based metabolomics has proven to be promising for advancing early-detection and elucidation of disease progression, through the discovery and characterization of novel biomarkers. This retrospective study of urine-NMR samples, from prostate biopsy patients with and without PCa, identified several metabolites involved in energy metabolism, amino acid metabolism, and the hippuric acid pathway. Of note, lactate and hippurate—key metabolites involved in cellular proliferation and microbiome effects, respectively—were significantly altered, unveiling widespread metabolomic modifications associated with PCa development. These findings support urine metabolomics profiling as a promising strategy to identify new clinical biomarkers for PCa detection and diagnosis.

Hydroxypropyl methylcellulose acetyl succinate (HPMCAS) is widely used as a pharmaceutical excipient, making a detailed understanding of its tunable structure important for formulation design. Several recently reported peak assignments in the solid-state ¹³C NMR spectrum of HPMCAS have been corrected here using peak integrals in quantitative spectra, spectral editing, empirical chemical-shift predictions based on solution NMR, and full spectrum simulation analogous to deconvolution. Unlike in cellulose, the strong peak at 84 ppm must be assigned to C2 and C3 methyl ethers, instead of regular C4 of cellulose. The proposed assignment of signals at <65 ppm to OCH sites, including C5 of cellulose, could not be confirmed. CH₂ spectral editing showed two resolved OCH₂ bands, a more intense one from O-CH₂ ethers of C6 at >69 ppm and a smaller one from its esters and possibly residual CH₂-OH groups, near 63 ppm. The strong intensities of resolved signals of acetyl, succinoyl, and oxypropyl substituents indicated the substitution of >85% of the OH groups in HPMCAS. The side-group concentrations in three different grades of HPMCAS were quantified.

Sulfur-33(³³S) stable-isotope labeled taurine, 2-aminoethanesulfonic acid, has been synthesized, and a series of solution and solid-state ³³S nuclear magnetic resonance (NMR) experiments at 14.1 and 18.8 T, respectively, have been carried out at room temperature. The single peak of a solution ³³S NMR spectrum in 0.1-mM [³³S]-taurine in D₂O can be observed with the signal-to-noise (S/N) ratio of 9 in 40,000 scans, which paves the way toward in vivo analysis of pharmacokinetics and metabolism of ³³S-labeled taurine. Undistorted magic-angle-spinning (MAS) and static ³³S NMR spectra of polycrystalline [³³S]-taurine are observed with sufficient S/N ratios for analysis, and the magnitudes of ³³S EFG and CS tensors can be obtained.

The quick identification of known organic low molecular weight compounds, also known as structural dereplication, is a highly important task in the chemical profiling of natural resource extracts. To that end, a method that relies on carbon-13 nuclear magnetic resonance (NMR) spectroscopy, elaborated in earlier works of the author's research group, requires the availability of a dedicated database that establishes relationships between chemical structures, biological and chemical taxonomy, and spectroscopy. The construction of such a database, called acd_lotus, was reported earlier, and its usefulness was illustrated by only three examples. This article presents the results of structure searches carried out starting from 58 carbon-13 NMR data sets recorded on compounds selected in the metabolomics section of the biological magnetic resonance bank (BMRB). Two compound retrieval methods were employed. The first one involves searching in the acd_lotus database using commercial software. The second one operates through the freely accessible web interface of the nmrshiftdb2 database, which includes the compounds present in acd_lotus and many others. The two structural dereplication methods have proved to be efficient and can be used together in a complementary way.

¹³C nuclear magnetic resonance (NMR) is traditionally considered an insensitive technique, requiring long acquisition times to measure dilute functionalities on large polymers. With the introduction of cryoprobes and better electronics, sensitivity has improved in a way that allows measurements to take less than 1/20th the time that they previously did. Unfortunately, a high Q-factor with cryoprobes creates baseline curvature related to acoustic ringing that affects quantitative NMR analyses. Manual baseline correction is commonly used to compensate for the baseline roll, but it is a time-intensive process. The outcome of manual baseline correction can vary depending on processing parameters, especially for complicated spectra. Additionally, it can be challenging to distinguish between broad peaks and baseline rolls. A new anti-ring pulse sequence (zgig_pisp) was previously reported to improve on the incumbent single pulse experiment (zgig). The original report presented limited comparison data with ¹³C NMR, but a thorough validation is needed before broader implementation can be considered. In this work, we report the round-robin testing and comparison of zgig_pisp and zgig pulse sequences. During the testing phase, we found that zgig_pisp is practically equivalent to zgig to ±2% for the majority of integrals examined. Additionally, a short broadband inversion pulse (BIP) was demonstrated as an alternative to the originally reported adiabatic CHIRP shaped pulse. The zgig_pisp pulse sequence code for Bruker spectrometers is also simplified.

High-temperature superconducting (HTS) materials have recently been incorporated into the construction of HTS cryogen-free magnets for nuclear magnetic resonance (NMR) spectroscopy. These HTS NMR spectrometers do not require liquid cryogens, thereby providing significant cost savings and facilitating easy integration into chemistry laboratories. However, the optimal performance of these HTS magnets against standard cryogen NMR magnets must be evaluated, especially with demanding modern NMR applications such as NMR in anisotropic media. The stability of the HTS magnets over time and their performance with complex pulse sequence experiments are the main unknown factors of this new technology. In this study, we evaluate the utility of our prototype 400 MHz cryogen-free power-driven HTS NMR spectrometer, installed in the fumehood of a chemistry laboratory, for stereochemical analysis of three commercial natural products (artemisinin, artemether, and dihydroartemisinin) via measurement of anisotropic NMR data, in particular, residual dipolar couplings. The accuracy of measurement of the anisotropic NMR data with the HTS magnet spectrometer is evaluated through the CASE-3D fitting protocol, as implemented in the Mestrenova-StereoFitter software program.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for qualitative and quantitative analysis. However, for complex mixtures, determining the speciation from NMR spectra can be tedious and sometimes even unfeasible. On the other hand, identifying and quantifying structural groups in a mixture from NMR spectra is much easier than doing the same for components. We call this group-based approach “NMR fingerprinting.” In this work, we show that NMR fingerprinting can even be performed in an automated way, without expert knowledge, based only on standard NMR spectra, namely, ¹³C, ¹H, and ¹³C DEPT NMR spectra. Our approach is based on the machine-learning method of support vector classification (SVC), which was trained here on thousands of labeled pure-component NMR spectra from open-source data banks. We demonstrate the applicability of the automated NMR fingerprinting using test mixtures, of which spectra were taken using a simple benchtop NMR spectrometer. The results from the NMR fingerprinting agree remarkably well with the ground truth, which was known from the gravimetric preparation of the samples. To facilitate the application of the method, we provide an interactive website (https://nmr-fingerprinting.de), where spectral information can be uploaded and which returns the NMR fingerprint. The NMR fingerprinting can be used in many ways, for example, for process monitoring or thermodynamic modeling using group-contribution methods—or simply as a first step in species analysis.