13C Nuclear Magnetic Resonance Chemical Shifts Research Articles

Can Graph Machines Accurately Estimate 13C NMR Chemical Shifts of Benzenic Compounds?

In the organic laboratory, the 13C nuclear magnetic resonance (NMR) spectrum of a newly synthesized compound remains an essential step in elucidating its structure. For the chemist, the interpretation of such a spectrum, which is a set of chemical-shift values, is made easier if he/she has a tool capable of predicting with sufficient accuracy the carbon-shift values from the structure he/she intends to prepare. As there are few open-source methods for accurately estimating this property, we applied our graph-machine approach to build models capable of predicting the chemical shifts of carbons. For this study, we focused on benzene compounds, building an optimized model derived from training a database of 10,577 chemical shifts originating from 2026 structures that contain up to ten types of non-carbon atoms, namely H, O, N, S, P, Si, and halogens. It provides a training root-mean-squared relative error (RMSRE) of 0.5%, i.e., a root-mean-squared error (RMSE) of 0.6 ppm, and a mean absolute error (MAE) of 0.4 ppm for estimating the chemical shifts of the 10k carbons. The predictive capability of the graph-machine model is also compared with that of three commercial packages on a dataset of 171 original benzenic structures (1012 chemical shifts). The graph-machine model proves to be very efficient in predicting chemical shifts, with an RMSE of 0.9 ppm, and compares favorably with the RMSEs of 3.4, 1.8, and 1.9 ppm computed with the ChemDraw v. 23.1.1.3, ACD v. 11.01, and MestReNova v. 15.0.1-35756 packages respectively. Finally, a Docker-based tool is proposed to predict the carbon chemical shifts of benzenic compounds solely from their SMILES codes.

A Very Deep Graph Convolutional Network for 13C NMR Chemical Shift Calculations with Density Functional Theory Level Performance for Structure Assignment.

Nuclear magnetic resonance (NMR) chemical shift calculations are powerful tools for structure elucidation and have been extensively employed in both natural product and synthetic chemistry. However, density functional theory (DFT) NMR chemical shift calculations are usually time-consuming, while fast data-driven methods often lack reliability, making it challenging to apply them to computationally intensive tasks with a high requirement on quality. Herein, we have constructed a 54-layer-deep graph convolutional network for 13C NMR chemical shift calculations, which achieved high accuracy with low time-cost and performed competitively with DFT NMR chemical shift calculations on structure assignment benchmarks. Our model utilizes a semiempirical method, GFN2-xTB, and is compatible with a broad variety of organic systems, including those composed of hundreds of atoms or elements ranging from H to Rn. We used this model to resolve the controversial J/K ring junction problem of maitotoxin, which is the largest whole molecule assigned by NMR calculations to date. This model has been developed into user-friendly software, providing a useful tool for routine rapid structure validation and assignation as well as a new approach to elucidate the large structures that were previously unsuitable for NMR calculations.

COLMARppm: A Web Server Tool for the Accurate and Rapid Prediction of 1H and 13C NMR Chemical Shifts of Organic Molecules and Metabolites.

Despite rapid progress in metabolomics research, a major bottleneck is the large number of metabolites whose chemical structures are unknown or whose spectra have not been deposited in metabolomics databases. Nuclear magnetic resonance (NMR) spectroscopy has a long history of elucidating chemical structures from experimentally measured 1H and 13C chemical shifts. One approach to characterizing the chemical structures of an unknown metabolite is to predict the 1H and 13C chemical shifts of candidate compounds (e.g., metabolites from the Human Metabolome Database (HMDB)) and compare them with chemical shifts of the unknown. However, accurate prediction of NMR chemical shifts in aqueous solution is challenging due to limitations of experimental chemical shift libraries and the high computational cost of quantum chemical methods. To improve NMR prediction accuracy and applicability, an empirical prediction strategy is introduced here to provide an accurately predicted chemical shift for organic molecules and metabolites within seconds. Unique features of COLMARppm include (i) the training library exclusively consisting of high quality NMR spectra measured under standard conditions in aqueous solution, (ii) utilization of NMR motif information, and (iii) leveraging of the improved prediction accuracy for the automated assignment of experimental chemical shifts for candidate structures. COLMARppm is demonstrated in terms of accuracy and speed for a set of 20 compounds taken from the HMDB for chemical shift prediction and resonance assignment. COLMARppm is applicable to a wide range of small molecules and can be directly incorporated into metabolomics workflows.

Nuclear Magnetic Resonance Chemical Shift As Highly Explainable Chemical Structure Fingerprints for Anion Exchange Membrane Polymers

Recent shift towards clean energy increased the demand for both fuel cells (used as clean power generator) and water electrolyzer (used as hydrogen supply) significantly. Anion exchange membrane (AEM) serves as a core component for these devices, though its low anion conductivity and durability inhibit their potential for commercialization. Many research and development (R&D) have been done seeking for improvements in AEM1, but current empirical-centric method consumes significant amount of resources, such as cost, labor, and time. To reduce resource consumptions, implementing materials informatics (MI) that allows high-speed screening of materials through a pre-trained AEM polymer machine learning (ML) model is important. However, AEMs are made up of polymers whose chemical structures are complex and hard to represent in a machine-understandable form. Fingerprints are often used to represent chemical structures in numerical forms generated through algorithm2. Majority of these fingerprints are designed for small molecules, not polymers, but they are usually unintuitive and difficult to understand due to their topological nature. In contrast, nuclear magnetic resonance (NMR) chemical shift have long been used in chemistry to identify the chemical structure of a particular sample3. In this study, we aim to utilize the high-resolution nature of NMR chemical shift to identify structural formula as chemical structure fingerprint for ML model, such that a polymer-suited and highly explainable fingerprint can be developed.First, an AEM database containing structural and experimental condition information was built using data extracted from 62 papers. Experimental conditions included were anion conductivity measuring temperature, alkaline stability test measuring condition (test temperature, length of test, and concentration of alkaline solution). Then, the 13C NMR chemical shift for the chemical structure contained in structural information was calculated using ChemDraw. The obtained chemical shifts were converted to numerical strings and is named as “NMR fingerprint”. A new AEM database containing both structural (molar ratio of each building blocks and NMR fingerprints) and experimental condition was used as the training database for ML models. Target variable was set to anion conductivity, and the rest were explanatory variables. ML model used was XGBoost. Cross-validation was used to evaluate the capability of ML models to predict anion conductivity of novel AEM polymers. Prediction logic was analyzed using Shapley additive explanations (SHAP) value.The database built contains data from 62 AEM papers, with 2,197 anion conductivity data points. Each AEM chemical structures present in the database was converted to NMR fingerprints using NMR chemical shifts, obtaining around 2,000 NMR fingerprints for each AEM polymer unit. Together with the experimental conditions and structural information included in the database, the data were used as train-validation dataset for XGBoost. The coefficient of determination (R2) obtained for cross-validated model was 0.9235, implying that the model learnt and determined the relationship between anion conductivity and AEM polymer structure with high accuracy, with the aid of experimental conditions. Then, the prediction logic of the ML model was explored using SHAP values, which are values computed from coalitional game theory, and is used to increase transparency and interpretability of ML models. Analyzing the plot of SHAP values for top 20 important variables used in XGBoost showed that measuring temperature for anion conductivity ranked highest, which is in coherence to the well-known behavior of AEM polymers. Besides, non-experimental condition variables such as 29.8_A ranked into the top 3 important variables. 29.8_A is the chemical shift for alkyl groups attached in between two imidazolium group of AEM polymer, suggesting that the presence or absence of more than one imidazolium group per side chain is important to determine the anion conductivity of an AEM polymer. SHAP values for 29.8_A show that higher feature value (pink color) gives higher impact (positive region of x-axis) to the target variable, inferring that having alkyl groups between imidazolium groups give beneficial effect to anion conductivity. Such ability to explain the prediction logic of ML model shows that using NMR chemical shifts as fingerprints for AEM polymer structures provide intuitive, human-understandable ML prediction logic explanation. Together with the high cross-validation accuracy, NMR chemical shifts hold the potential to not only be a gold standard in expressing polymer structures in machine-understandable form, but also to strongly push the adoption of ML in the AEM polymer field, creating a paradigm shift for AEM R&D.Reference S. Gottesfeld et al., J. Power Sources 2018, 375, 170-184.J. Bajorath, J. Chem. Inf. Comput. Sci. 2001, 41, 2, 233–245.P. Jezzard et al., Adv. Mater. 1992, 4, 2, 82-90. Figure 1

Spectroscopic properties (FT-IR, NMR and UV) and DFT studies of amodiaquine

Amodiaquine (AQ) was synthesized by a condensation reaction and characterized by experimental FT-IR, 1H and 13C nuclear magnetic resonance (NMR) and UV spectroscopies. In the present work, Density Functional Theory (DFT) calculations using B3LYP method with 6–311++G(d,p) and 6–311++G(2d, p) basis sets were performed, first, to confirm its structure, then to explain its reactive nature through its molecular properties such as natural charges, local and global reactivity descriptors or natural bond orbital (NBO). The 1H and 13C NMR chemical shifts were calculated by using the gauge-independent atomic orbital (GIAO) method, while the electronic UV–Vis spectrum is predicted using the time-dependent density functional theory (TD-DFT). Globally, the computerized results showed close similarity with the experimental values.The calculated energy band gap (ELUMO-EHOMO) value of AQ was found to be 4.09 eV suggesting that it could be considered as a hard molecule with high stability, supported by global reactivity descriptors. Molecular electrostatic potential (MEP) analysis revealed heteroatoms (oxygen and nitrogen) as the most putative nucleophilic sites when hydrogen atoms to which they are linked appear as electrophilic sites. The potential use of amodiaquine as non-linear optical (NLO) material and its thermodynamic indicators have also been assessed.

Exploring pyrrolizidine alkaloids in Crotalaria paulina: Isolation and identification of a promising compound

Studies involving the structural elucidation of organic compounds isolated from natural products, along with theoretical calculations based on density functional theory (DFT), have confirmed the structures of many complex molecules. This work demonstrates the use of Nuclear Magnetic Resonance (NMR) spectroscopy and theoretical calculations in the structural identification of a pyrrolizidine alkaloid (PA), a secondary metabolite isolated from the plant Crotalaria paulina. Secondary metabolites are found in a wide variety of plant species and have diverse biological applications. In this study, the structural elucidation of the alkaloid was performed using high resolution mass spectrometry and nuclear magnetic resonance spectroscopy. Eight candidate structures were subjected to theoretical calculations at DFT level of theory. The experimental and calculated results were compared using the probability distribution DP4+ and corrected mean absolute error (CMAE) methods. The DP4+ result was 99.9%, and the CMAE for 13C NMR chemical shifts was 2.1 ppm, indicating a strong agreement between the experimental and theoretical chemical shifts for the stereochemistry represented by structure CS3 ((3S,4R,5S,8aR,13aR)-3-ethyl-5‑hydroxy-3,4,5-trimethyl-4,5,8,8a,10,12,13,13a-octahydro-2H[1,6]dioxacycloundecino[2,3,4-gh]pyrrolizine-2,6(3H)‑dione). Ethylmethylcrotaline was coined as a nonsystematic nomenclature for structure CS3.

Methoxy 13C NMR Chemical Shift as a Molecular Descriptor in the Structural Analysis of Flavonoids and Other Phenolic Compounds

The interpretation of nuclear magnetic resonance (NMR) chemical shifts plays an increasingly important role in the structure elucidation of flavonoids. Despite the fact that the 3H signal of the methoxy (OMe) group appears as a singlet in the 1H NMR spectrum, its chemical shift is not reliably indicative of the OMe-substitution in the aromatic rings, unlike its 13C NMR chemical shift, which can be useful for the determination of the molecular structure of flavonoids. For example, the chemical shift of ortho-substitutions is at 56 ± 1 ppm for flavonoids having one OMe-substituent in either the ortho or orthó position and 61.5 ± 2 ppm for those having substituents in both ortho positions. The nuclear Overhauser effect spectroscopy ( δH-OMe→ ortho-aryl-H or adjacent substituent) and heteronuclear multiple-bond correlation ( δH-OMe→ ipso-aryl-C) data have been widely employed for the structure analysis of flavonoids and to identify the site of OMe-substitution. Thus, OMe chemical shifts can act as molecular descriptors. However, we have noticed that there are several examples that are not in accordance with this generalization, and a reinvestigation of such reported data seems to be relevant for reconsideration of assigned structures, and a few examples are cited herein.

Elucidation of Metal-Sugar Complexes: When Tungstate Combines with d-Mannose.

The control of metal-sugar complexes speciation in solution is crucial in an energy transition context. Herein, the formation of tungstate-mannose complexes is unraveled in aqueous solution using a multitechnique experimental and theoretical approach. 13C nuclear magnetic resonance (NMR), as well as 13C-1H and 1H-1H correlation spectra, analyzed in the light of coordination-induced shift method and conformation analysis, were employed to characterize the structure of the sugar involved in the complexes. X-ray absorption near edge structure spectroscopy was performed to provide relevant information about the metal electronic and coordination environment. The calculation of 13C NMR chemical shifts for a series of tungstate-mannose complexes using density functional theory (DFT) is a key to identify the appropriate structure among several candidates. Furthermore, a parametric study based on several relevant parameters, namely, pH and tungstate concentration, was carried out to look over the change of the nature and concentrations of the complexes. Two series of complexes were detected, in which the metallic core is either in a ditungstate or a monotungstate form. With respect to previous proposals, we identify two new species. Dinuclear complexes involve both α- and β-furanose forms chelating the metallic center in a tetradentate fashion. A hydrate form chelating a ditungstate core is also revealed. One monotungstate complex appears at high pH, in which a tetrahedral tungstate center is bound to α-mannofuranose through a monodentate site at the second deprotonated hydroxyl group. This unequalled level of knowledge opens the door to structure-reactivity relationships.

NMR Chemical Shifts of Emerging Green Solvents, Acids, and Bases for Facile Trace Impurity Analysis

The straightforward identification of impurity signals in nuclear magnetic resonance (NMR) spectra is imperative for the structure elucidation and signal assignment of synthetic products and intermediates. To keep pace with the emergence of novel green solvents and auxiliary compounds (e.g., acids and bases), NMR impurity tables and databases must be regularly updated. This study reports the residual 1H and 13C NMR chemical shifts of 42 green solvents, acids, and bases in eight NMR solvents, namely, dimethylsulfoxide-d6, chloroform-d, D2O, CD3OD, CD3CN, acetone-d6, tetrahydrofuran-d8, and toluene-d8. The multiplicities and coupling constants of 1H signals are also determined herein. The analysis of the recorded NMR spectra provides important information regarding the reactivity or multicomponent nature of the green solvents, acids, and bases. Herein, the results of this study are combined with earlier reports on residual NMR impurities to form a comprehensive database. This database forms the basis of an online interface (http://www.nmrimpurities.com) through which users can browse solvent spectra and search for signals of unknown origins to easily identify residual impurities in NMR spectra.

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted 13C and 1H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted 13C and 1H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of 1H and 13C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

Theoretical study of keto-enol tautomerism in 7-epi-clusianone by quantum chemical calculations of NMR chemical shifts.

Plants from the Garcinia genus have been used worldwide due to their therapeutic properties. Among the various metabolites isolated from this genus, 7-epi-clusianone, a tetraprenylated benzophenone, stands out for its wide range of identified biological activities. This benzophenone can exist in five tautomeric forms, although the benzene-d6 and chloroform-d3 solution nuclear magnetic resonance (NMR) spectra revealed only two tautomeric forms (B and C) in equilibrium, with concentration ratio depending on the solvent in which the spectrum was obtained. Calculated energy values suggested that tautomeric forms B and E would be prevalent in benzene-d6 solution, in contrast to the experimental data. Considering this conflicting result, we employed the statistical DP4 + method based on 13C and 1H NMR chemical shift calculations, in the gas phase and in benzene-d6 solution, to confirm that the B and C tautomeric forms of 7-epi-clusianone are the most prevalent in the experimental conditions.

Automated fragmentation quantum mechanical calculation of 13C and 1H chemical shifts in molecular crystals.

In this work, the automated fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) approach was applied to calculate the 13C and 1H nuclear magnetic resonance (NMR) chemical shifts in molecular crystals. Two benchmark sets of molecular crystals were selected to calculate the NMR chemical shifts. Systematic investigation was conducted to examine the convergence of AF-QM/MM calculations and the impact of various density functionals with different basis sets on the NMR chemical shift prediction. The result demonstrates that the calculated NMR chemical shifts are close to convergence when the distance threshold for the QM region is larger than 3.5 Å. For 13C chemical shift calculations, the mPW1PW91 functional is the best density functional among the functionals chosen in this study (namely, B3LYP, B3PW91, M06-2X, M06-L, mPW1PW91, OB98, and OPBE), while the OB98 functional is more suitable for the 1H NMR chemical shift prediction of molecular crystals. Moreover, with the B3LYP functional, at least a triple-ζ basis set should be utilized to accurately reproduce the experimental 13C and 1H chemical shifts. The employment of diffuse basis functions will further improve the accuracy for 13C chemical shift calculations, but not for the 1H chemical shift prediction. We further proposed a fragmentation scheme of dividing the central molecule into smaller fragments. By comparing with the results of the fragmentation scheme using the entire central molecule as the core region, the AF-QM/MM calculations with the fragmented central molecule can not only achieve accurate results but also reduce the computational cost. Therefore, the AF-QM/MM approach is capable of predicting the 13C and 1H NMR chemical shifts for molecular crystals accurately and effectively, and could be utilized for dealing with more complex periodic systems such as macromolecular polymers and biomacromolecules. The AF-QM/MM program for molecular crystals is available at https://github.com/shiman1995/NMR.

13C NMR chemical shifts in substituted benzenes: analysis using natural perturbation orbitals and substitution effects

The natural perturbation orbital (NPO) method, combined with the gauge-including atomic orbital (GIAO), was applied to nuclear magnetic resonance (NMR) chemical shifts. The substituent effects of electron-donating (NH2) and electron-withdrawing (NO2) groups on the 13C NMR chemical shifts of monosubstituted benzenes were analysed using NPOs. 13C chemical shifts at the ortho-, meta- and para-positions were efficiently decomposed into contributions from two NPO pairs (, ). These contributions were further divided into two factors, namely electron density in the vicinity of resonance nuclei and orbital energy gaps. Trends in the electron density matched with the ortho-, meta- and para-directions of the substituent effects. Specifically, NH2 shielded the ortho- and para-carbons, while NO2 deshielded the same carbons. The orbital energy gaps of ortho-carbons in NH2- and NO2-benzenes were significantly decreased, showing that these substituents shielded the ortho-carbons.

Structure and NMR properties of the dinuclear complex di-μ-azido-κ4 N 1:N 1-bis-[(azido-κN)(pyridine-2-carboxamide-κ2 N 1,O)zinc(II)

The new diamagnetic complex, [Zn2(N3)4(C6H6N2O)2] or [Zn2(pca)2(μ1,1-N3)2(N3)2] was synthesized using pyridine-2-carboxamide (pca) and azido ligands, and characterized using various techniques: IR spectroscopy and single-crystal X-ray diffraction in the solid state, and nuclear magnetic resonance (NMR) in solution. The mol-ecule is placed on an inversion centre in space group P . The pca ligand chelates the metal centre via the pyridine N atom and the carbonyl O atom. One azido ligand bridges the two symmetry-related Zn2+ cations in the end-on coordination mode, while the other independent azido anion occupies the fifth coordination site, as a terminal ligand. The resulting five-coordinate Zn centres have a coordination geometry inter-mediate between trigonal bipyramidal and square pyramidal. The behaviour of the title complex in DMSO solution suggests that it is a suitable NMR probe for similar or isostructural complexes including other transition-metal ions. The diamagnetic nature of the complex is reflected in similar 1H and 13C NMR chemical shifts for the free ligand pca as for the Zn complex.

Estimation of the enthalpy of formation for monosubstituted alkanes by 13C NMR chemical shift

AbstractThe quantitative relationship between the 13C nuclear magnetic resonance (NMR) chemical shift and the enthalpy of formation of monosubstituted alkanes was studied, and a new approach for calculating enthalpy of formation was proposed. The experimental values of the enthalpy of formation of 64 monosubstituted alkanes were correlated with the parameters SCS (the sum of chemical shift), P3 (path counts of length three bonds), n (the number of C atoms), h[X] (the contributions of the substituent X to the ΔfH°(RX)), and the product of IPI(X) (the interaction potential index) and PEI(R) (the polarizability effect index). The regression results show a good correlation (R = 0.9998, S = 4.5 kJ/mol). Its stability and prediction ability were confirmed by the results of the random sampling method and the leave‐one‐out method. In this paper, the 13C NMR chemical shift and the enthalpy of formation of monosubstituted alkanes were associated quantitatively. The significance is not only to propose a new method to estimate the enthalpy of formation of monosubstituted alkanes, but also to establish a new method to understand the relationship between different properties based on a same molecular structure. Moreover, it provides a new approach for calculating a molecular difficult‐to‐test property parameter by means of another easy‐to‐test one.

A combined NMR, MD and DFT conformational analysis of 9-O-acetyl sialic acid-containing GM3 ganglioside glycan and its 9-N-acetyl mimic.

O-Acetylation of carbohydrates such as sialic acids is common in nature, but its role is not clearly understood due to the lability of O-acetyl groups. We demonstrated previously that 9-acetamido-9-deoxy-N-acetylneuraminic acid (Neu5Ac9NAc) is a chemically and biologically stable mimic of the 9-O-acetyl-N-acetylneuraminic acid (Neu5,9Ac2) of the corresponding sialoglycans. Here, a systematic nuclear magnetic resonance (NMR) spectroscopic and molecular dynamics (MD) simulation study was undertaken for Neu5,9Ac2-containing GM3 ganglioside glycan (GM3-glycan) and its Neu5Ac9NAc analog. GM3-glycan with Neu5Ac as the non-O-acetyl form of Neu5,9Ac2 was used as a control. Complete 1H and 13C NMR chemical shift assignments, three-bond 1H-13C trans-glycosidic coupling constants (3JCH), accurate 1H-1H coupling constants (3JHH), nuclear Overhauser effects and hydrogen bonding detection were carried out. Results show that structural modification (O- or N-acetylation) on the C-9 of Neu5Ac in GM3 glycan does not cause significant conformational changes on either its glycosidic dihedral angles or its secondary structure. All structural differences are confined to the Neu5Ac glycerol chain, and minor temperature-dependent changes are seen in the aglycone portion. We also used Density Functional Theory (DFT) quantum mechanical calculations to improve currently used 3JHH Karplus relations. Furthermore, OH chemical shifts were assigned at -10°C and no evidence of an intramolecular hydrogen bond was observed. The results provide additional evidence regarding structural similarities between sialosides containing 9-N-acetylated and 9-O-acetylated Neu5Ac and support the opportunity of using 9-N-acetylated Neu5Ac as a stable mimic to study the biochemical role of 9-O-acetylated Neu5Ac.

Absolute Configuration of (−)-Cubebin, a Classical Lignan with Pharmacological Potential, Defined by Means of Chiroptical Spectroscopy

This work describes the first determination of the absolute configuration (AC) of (−)-cubebin by means of electronic circular dichroism (ECD), supported by quantum chemical calculations. The comparison of experimental ECD with the corresponding quantum chemical prediction for the proper diastereoisomer resulted in the definitive assignment of the AC of the naturally occurring (−)-cubebin as (8R,8aR,9S). The challenging determination of the relative configuration (RC) of cubebin based only on experimental nuclear magnetic resonance (NMR) methods is stressed. Computation of the 13C and 1H NMR chemical shifts for all the possible diastereoisomers leads to values of mean absolute error and root mean square deviation that do not allow distinguishing among them. Thus, errors in stereochemical determination can easily occur even when using two-dimensional methods, which clearly demonstrate the complexity of this special case. To determine the RC of this bioactive natural compound with high level of confidence, it was necessary to combine the DP4+ method with X-ray crystallography. Therefore, employing the commonly used empirical methods to determine the AC of (−)-cubebin can easily lead to misassignment of its stereochemistry.

Thermal property and structural molecular dynamics of organic–inorganic hybrid perovskite 1,4-butanediammonium tetrachlorocuprate

We investigate the thermal behaviour and physical properties of the crystals of the organic inorganic hybrid perovskite [(NH3)(CH2)4(NH3)]CuCl4. The compound's thermal stability curve as per thermogravimetric analysis exhibits a stable state up to ∼495 K, while the weight loss observed near 538 K corresponds to partial thermal decomposition. The 1H nuclear magnetic resonance (NMR) chemical shifts for NH3 change more significantly with temperature than those for CH2, because the organic cation motion is enhanced at both ends of the organic chain. The 13C NMR chemical shifts for the ‘CH2-1’ units of the chain show an anomalous change, and those for ‘CH2-2’ (units closer to NH3) are shifted sharply. Additionally, the 14N NMR spectra reflect the changes of local symmetry near TC (=323 K). Moreover, the 13C T1ρ values for CH2-2 are smaller than those for CH2-1, and the 13C T1ρ data curve for CH2-1 exhibits an anomalous behaviour between 260 and 310 K. These smaller T1ρ values at lower temperatures indicate that 1H and 13C in the organic chains are more flexible at these temperatures. The NH3 group is attached to both ends of the organic chain, and NH3 forms a N–H⋯Cl hydrogen bond with the Cl ion of inorganic CuCl4. When H and C are located close to the paramagnetic Cu2+ ion, the T1ρ value is smaller than when these are located far from the paramagnetic ion.

Structure Identification of Novel Compounds Using Simple IR, 1H, and 13C NMR Spectroscopy and Computational Tools

A simple method is suggested to identify the structure of novel compounds with basic IR, 1H, and 13C nuclear magnetic resonance (NMR) spectroscopy and computational tools. With the molecular formula obtained from high‐resolution mass spectrometry, all possible isomers are calculated. The absence or presence of particular functional groups which were inferred from IR, 1H, and 13C NMR and DEPT spectra are used to sort all the possible isomers using SMARTS code and RDKit. Then, the final structure(s) are identified by comparing the correlation between Quantum mechanically calculated 13C NMR chemical shielding constants and experimental 13C chemical shifts of molecules in consideration. We have applied this protocol to five natural compounds, the number of heavy atoms ranging from 11 to 15, and correctly identified the structures of all test cases. The limitations and further consideration are discussed.

Impact of intramolecular hydrogen bonding of gallic acid conformers on chemical shift through NMR spectroscopy

Intramolecular hydrogen bonding of gallic acid conformers was probed as a function of their dihedral angles using 13C nuclear magnetic resonance (NMR) chemical shift (δC). The quantum mechanically calculated 13C NMR chemical shift based on the most stable conformer (GA-I) in dimethylsulfoxide (DMSO) solution agrees to available measurement in the same solvent (RMSD = 0.95 ppm), better than to the measurement in solid phase (RMSD = 1.93 ppm). The accuracy of the calculated NMR chemical shift of the nominal but non-equivalent phenyl carbons C(3)/C(7) and C(4)/C(6) (ortho and meta to the acid –C(1)OOH group) of GA may not be evaluated using the experimental measurements at room temperature. The splitting in chemical shift of the nominal phenyl carbons is able to be experimentally measured only in low temperature NMR and using quantum mechanical calculations. We further recognised that the C NMR chemical shifts of the nominal phenyl carbons (C(3)/C(7) and C(4)/C(6)) encode information for intramolecular hydrogen bonding network formed by GA conformers. The ability to obtain accurate splitting of NMR chemical shifts for nominal carbons, therefore, determines the usefulness of the NMR technique as a probe for conformation of GA.