Nuclear Magnetic Resonance Shifts Research Articles

Tracking the Facet Transformation of CeO2 by 17O Solid-State Nuclear Magnetic Resonance.

CeO2 nanomaterials expose various crystal facets with distinct surface geometry, resulting in different surface reactivities and material behaviors that ultimately determine their performances and suitability for a wide range of applications. Here, we apply 17O solid-state nuclear magnetic resonance (NMR) to follow the facet transformation of CeO2 at increased temperatures, observing a transition from (100) to (110) and finally to the more stable (111), based on the characteristic NMR shifts associated with the unique surface structure of each facet. In addition, we explore the effects of Pt ions on the conversion of different facets, which are found to promote the formation of the thermally stable (111) facet. Furthermore, 17O solid-state NMR provides a semiquantitative method for measuring the fractions of exposed facets. This work offers new insights and a more comprehensive understanding of crystal facet structures, and the new approach can be readily extended to study the facets of other oxide-based materials.

Light-Induced Metallic and Paramagnetic Defects in Halide Perovskites from Magnetic Resonance.

Halide perovskites are promising next-generation solar cell materials, but their commercialization is hampered by their propensity to degrade under operating conditions, particularly under heat, humidity, and light. Identifying degradation products and linking them to the degradation mechanism at the atomic scale is necessary to design more stable perovskite materials. Here we use magnetic resonance methods to identify and characterize the formation of both metallic lead clusters and Pb3+ defects upon light-induced degradation of methylammonium lead halide perovskite using nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR) measurements. Paramagnetic relaxation enhancement (PRE) of the 1H NMR resonances demonstrates the presence of localized paramagnetic Pb3+ defects, a large Knight shift of the 207Pb NMR proves the presence of lead metal, and their relative proportions are determined by the differing temperature dependence in variable-temperature EPR. This work reconciles previous conflicting literature results, enabling the use of EPR spectroscopy to monitor photodegradation of perovskite devices.

EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models.

Nuclear magnetic resonance (NMR) crystallography is one of the main methods in structural biology for analyzing protein stereochemistry and structure. The chemical shift of the resonance frequency reflects the effect of the protons in a molecule producing distinct NMR signals in different chemical environments. Apprehending chemical shifts from NMR signals can be challenging since having an NMR structure does not necessarily provide all the required chemical shift information, making predictive models essential for accurately deducing chemical shifts, either from protein structures or, more ideally, directly from amino acid sequences. Here, we present EFG-CS, a web server that specializes in chemical shift prediction. EFG-CS employs a machine learning-based transfer prediction model for backbone atom chemical shift prediction, using ESMFold-predicted protein structures. Additionally, ESG-CS incorporates a graph neural network-based model to provide comprehensive side-chain atom chemical shift predictions. Our method demonstrated reliable performance in backbone atom prediction, achieving comparable accuracy levels with root mean square errors (RMSE) of 0.30 ppm for H, 0.22 ppm for Hα, 0.89 ppm for C, 0.89 ppm for Cα, 0.84 ppm for Cβ, and 1.69 ppm for N. Moreover, our approach also showed predictive capabilities in side-chain atom chemical shift prediction achieving RMSE values of 0.71 ppm for Hβ, 0.74-1.15 ppm for Hδ, and 0.58-0.94 ppm for Hγ, solely utilizing amino acid sequences without homology or feature curation. This work shows for the first time that generative AI protein models can predict NMR shifts nearly comparable to experimental models. This web server is freely available at https://biosig.lab.uq.edu.au/efg_cs, and the chemical shift prediction results can be downloaded in tabular format and visualized in 3D format.

Paramagnetic Effects in NMR Spectroscopy of Transition-Metal Complexes: Principles and Chemical Concepts.

ConspectusMagnetic resonance techniques represent a fundamental class of spectroscopic methods used in physics, chemistry, biology, and medicine. Electron paramagnetic resonance (EPR) is an extremely powerful technique for characterizing systems with an open-shell electronic nature, whereas nuclear magnetic resonance (NMR) has traditionally been used to investigate diamagnetic (closed-shell) systems. However, these two techniques are tightly connected by the electron-nucleus hyperfine interaction operating in paramagnetic (open-shell) systems. Hyperfine interaction of the nuclear spin with unpaired electron(s) induces large temperature-dependent shifts of nuclear resonance frequencies that are designated as hyperfine NMR shifts (δHF).Three fundamental physical mechanisms shape the total hyperfine interaction: Fermi-contact, paramagnetic spin-orbit, and spin-dipolar. The corresponding hyperfine NMR contributions can be interpreted in terms of through-bond and through-space effects. In this Account, we provide an elemental theory behind the hyperfine interaction and NMR shifts and describe recent progress in understanding the structural and electronic principles underlying individual hyperfine terms.The Fermi-contact (FC) mechanism reflects the propagation of electron-spin density throughout the molecule and is proportional to the spin density at the nuclear position. As the imbalance in spin density can be thought of as originating at the paramagnetic metal center and being propagated to the observed nucleus via chemical bonds, FC is an excellent indicator of the bond character. The paramagnetic spin-orbit (PSO) mechanism originates in the orbital current density generated by the spin-orbit coupling interaction at the metal center. The PSO mechanism of the ligand NMR shift then reflects the transmission of the spin polarization through bonds, similar to the FC mechanism, but it also makes a substantial through-space contribution in long-range situations. In contrast, the spin-dipolar (SD) mechanism is relatively unimportant at short-range with significant spin polarization on the spectator atom. The PSO and SD mechanisms combine at long-range to form the so-called pseudocontact shift, traditionally used as a structural and dynamics probe in paramagnetic NMR (pNMR). Note that the PSO and SD terms both contribute to the isotropic NMR shift only at the relativistic spin-orbit level of theory.We demonstrate the advantages of calculating and analyzing the NMR shifts at relativistic two- and four-component levels of theory and present analytical tools and approaches based on perturbation theory. We show that paramagnetic NMR effects can be interpreted by spin-delocalization and spin-polarization mechanisms related to chemical bond concepts of electron conjugation in π-space and hyperconjugation in σ-space in the framework of the molecular orbital (MO) theory. Further, we discuss the effects of environment (supramolecular interactions, solvent, and crystal packing) and demonstrate applications of hyperfine shifts in determining the structure of paramagnetic Ru(III) compounds and their supramolecular host-guest complexes with macrocycles.In conclusion, we provide a short overview of possible pNMR applications in the analysis of spectra and electronic structure and perspectives in this field for a general chemical audience.

Benchmark Study on the Calculation of 207Pb NMR Chemical Shifts.

A benchmark set for the computation of 207Pb nuclear magnetic resonance (NMR) chemical shifts is presented. The PbS50 set includes conformer ensembles of 50 lead-containing molecular compounds and their experimentally measured 207Pb NMR chemical shifts. Various bonding motifs at the Pb center with up to seven bonding partners are included. Six different solvents were used in the measurements. The respective shifts lie in the range between +10745 and -5030 ppm. Several calculation settings are assessed by evaluating computed 207Pb NMR shifts for the use with different density functional approximations (DFAs), relativistic approaches, treatment of the conformational space, and levels for geometry optimization. Relativistic effects were included explicitly with the zeroth order regular approximation (ZORA), for which only the spin-orbit variant was able to yield reliable results. In total, seven GGAs and three hybrid DFAs were tested. Hybrid DFAs significantly outperform GGAs. The most accurate DFAs are mPW1PW with a mean absolute deviation (MAD) of 429 ppm and PBE0 with an MAD of 446 ppm. Conformational influences are small as most compounds are rigid, but more flexible structures still benefit from Boltzmann averaging. Including explicit relativistic treatments such as SO-ZORA in the geometry optimization does not show any significant improvement over the use of effective core potentials (ECPs).

Accelerated Pure Shift NMR Spectroscopy with Deep Learning.

Pure shift nuclear magnetic resonance (NMR) spectroscopy presents a promising solution to provide sufficient spectral resolution and has been increasingly applied in various branches of chemistry, but the optimal resolution is generally accompanied by long experimental times. We present a proof of concept of deep learning for fast, high-quality, and reliable pure shift NMR reconstruction. The deep learning (DL) protocol allows one to eliminate undersampling artifacts, distinguish peaks with close chemical shifts, and reconstruct high-resolution pure shift NMR spectroscopy along with accelerated acquisition. More meaningfully, the lightweight neural network delivers satisfactory reconstruction performance on personal computers by several hundred simulated data learning, which somewhat lifts the prohibiting demand for a large volume of real training samples and advanced computing hardware generally required in DL projects. Additionally, an M-to-S strategy applicable to common DL cases is further exploited to boost the network generalization capability. As a result, this study takes a meaningful step toward deep learning protocols for broad chemical applications.

Paramagnetic Nuclear Magnetic Resonance Shifts for Triplet Systems and Beyond with Modern Relativistic Density Functional Methods.

An efficient framework for the calculation of paramagnetic NMR (pNMR) shifts within exact two-component (X2C) theory and (current-dependent) density functional theory (DFT) up to the class of local hybrid functionals (LHFs) is presented. Generally, pNMR shifts for systems with more than one unpaired electron depend on the orbital shielding contribution and a temperature-dependent term. The latter includes zero-field splitting (ZFS), hyperfine coupling (HFC), and the g-tensor. For consistency, we calculate these three tensors at the same level of theory, i.e., using scalar-relativistic X2C augmented with spin-orbit perturbation theory. Results for pNMR chemical shifts of transition-metal complexes reveal that this X2C-DFT framework can yield good results for both the shifts and the individual tensor contributions of metallocenes and related systems, especially if the HFC constant is large. For small HFC constants, the relative error is often large, and sometimes the sign may be off. 4d and 5d complexes with more complicated structures demonstrate the limitations of a fully DFT-based approach. Additionally, a Co-based complex with a very large ZFS and pronounced multireference character is not well described. Here, a hybrid DFT-multireference framework is necessary for accurate results. Our results show that X2C is sufficient to describe relativistic effects and computationally cheaper than a fully relativistic approach. Thus, it allows use of large basis sets for converged HFCs. Overall, current-dependent meta-generalized gradient approximations and LHFs show some potential; however, the currently available functionals leave a lot to be desired, and the predictive power is limited.

Prediction of 19F NMR chemical shift by machine learning

Fluorine-19 (19F) is a nucleus of great importance in the field of Nuclear Magnetic Resonance (NMR) spectroscopy due to its high receptivity and wide chemical shift dispersion. 19F NMR plays crucial roles in both organic synthesis and biomedicine. Herein, a machine learning-based comprehensive 19F NMR chemical shift prediction model was established based on the experimental 19F NMR dataset from the book by Dolbier and the open NMR database nmrshiftdb2. Fluorine radical SMILES (Fr-SMILES) that reflected the fluorine chemical equivalence, was designed as the representation of fluorine in the molecule. Model trained with the graph convolution network (GCN) algorithm gave a low mean absolute error (MAE) of 3.636 ppm on the testing set. This model exhibits broad applicability and can effectively predict 19F NMR shifts for a wide range of organic fluorine molecules. We believe that the current work will provide a powerful tool for not only predicting 19F NMR shifts but also aiding in the analysis and identification of these shifts in diverse organic fluorine compounds. An online prediction platform was constructed based on the current model, which can be found at https://fluobase.cstspace.cn/fnmr.

Ilm-NMR-P31: an open-access 31P nuclear magnetic resonance database and data-driven prediction of 31P NMR shifts

This publication introduces a novel open-access 31P Nuclear Magnetic Resonance (NMR) shift database. With 14,250 entries encompassing 13,730 distinct molecules from 3,648 references, this database offers a comprehensive repository of organic and inorganic compounds. Emphasizing single-phosphorus atom compounds, the database facilitates data mining and machine learning endeavors, particularly in signal prediction and Computer-Assisted Structure Elucidation (CASE) systems. Additionally, the article compares different models for 31P NMR shift prediction, showcasing the database’s potential utility. Hierarchically Ordered Spherical Environment (HOSE) code-based models and Graph Neural Networks (GNNs) perform exceptionally well with a mean squared error of 11.9 and 11.4 ppm respectively, achieving accuracy comparable to quantum chemical calculations.

Solvents Influence 1H NMR Chemical Shifts and Complete 1H and 13C NMR Spectral Assignments for Florfenicol

Florfenicol (FFC) is an important and widely used veterinary drug, and its structure has been characterized by nuclear magnetic resonance (NMR) spectroscopy. The study aimed to investigate the influences of solvent type, solvent concentration, and temperature on the chemical shifts of the 1H NMR of FFC. The results showed that different types of solvents significantly affected the chemical shifts, especially the chemical shifts of 2-H, 3-H, 5-H, and the active protons. When DMSO-d 6 is used as the solvent, there is no significant difference in the chemical shifts of FFC with a concentration ranging from 20 to 250 mmol/L; however, as the temperature increases, the chemical shifts of the active protons move to a higher field. Besides, the NMR spectroscopic data and structural analysis of FFC were refined by 1H, 13C, distortionless enhancement by polarization transfer-135 (DEPT-135), 1H–1H correlation spectroscopy (1H–1H COSY), phase-sensitive gradient heteronuclear singular quantum correlation (gHSQC), and heteronuclear multiple bond correlation (gHMBC) NMR spectroscopy using DMSO-d 6 as a solvent. The study will help with qualitative and quantitative analysis of FFC in the future.

Exact two-component theory becoming an efficient tool for NMR shieldings and shifts with spin–orbit coupling

We present a gauge-origin invariant exact two-component (X2C) approach within a modern density functional framework, supporting meta-generalized gradient approximations such as TPSS and range-separated hybrid functionals such as CAM-B3LYP. The complete exchange-correlation kernel is applied, including the direct contribution of the field-dependent basis functions and the reorthonormalization contribution from the perturbed overlap matrix. Additionally, the finite nucleus model is available for the electron-nucleus potential and the vector potential throughout. Efficiency is ensured by the diagonal local approximation to the unitary decoupling transformation in X2C as well as the (multipole-accelerated) resolution of the identity approximation for the Coulomb term (MARI-J, RI-J) and the seminumerical exchange approximation. Errors introduced by these approximations are assessed and found to be clearly negligible. The applicability of our implementation to large-scale calculations is demonstrated for a tin pincer-type system as well as low-valent tin and lead complexes. Here, the calculation of the Sn nuclear magnetic resonance shifts for the pincer-type ligand with about 2400 basis functions requires less than 1 h for hybrid density functionals. Further, the impact of spin–orbit coupling on the nucleus-independent chemical shifts and the corresponding ring currents of all-metal aromatic systems is studied.

Cooperativity and topological hydrogen bonding in aromatic diol complexes

AbstractComplexes of three aromatic diols, catechol, naphthalene‐1,8‐diol, and fluorene‐4,5‐diol, with a series of hydrogen bond acceptors (HBAs) that have oxygen, nitrogen, and sulfur acceptor atoms, have been studied by density functional theory (DFT) at the B3LYP/6‐311+G(d,p) level. Binding energies, geometries, infrared spectroscopic (IR) frequencies, nuclear magnetic resonance (NMR) shifts, and measures of the electron density distribution from the Quantum Theory of Atoms in Molecules (QTAIM) are evaluated and compared with data for the corresponding monohydroxy compounds (monols), phenol, naphth‐1‐ol, and fluorene‐4‐ol, in order to assess the importance of cooperativity between intramolecular and intermolecular hydrogen bonding. All measures for all complexes show positive cooperativity whereby both the intermolecular and intramolecular hydrogen bonds are strengthened upon complexation. Cooperativity is weak for catechol and strong for the other two diols and, for all diols, increases with the hydrogen bond basicity of the acceptor. Correlations of IR and NMR metrics against binding energies for a single HBA and all six monols and diols are excellent, but attempts to correlate the same metrics for all HBAs and a single donor are frustrated by differences in intermolecular hydrogen bonding, depending notably on the identity of the acceptor atom in the HBA. Atom–atom interaction energies, calculated by the Interacting Quantum Atoms approach, are used to discuss the covalency of both types of hydrogen bond.

Review on NMR as a tool to analyse natural products extract directly: Molecular structure elucidation and biological activity analysis.

Natural products, the small organic molecules produced by plants, microbes and invertebrates, often present in the form of a mixture, this leads to the structural characterisation of natural extracts often requiring time-consuming multistep purification procedures. Nuclear magnetic resonance (NMR) technology is traditionally utilised as a tool for the structural elucidation of pure compounds. Contemporarily, an up-to-date trend in the application of NMR in natural product research is shifting to the direct NMR analysis of crude mixtures, to obtain molecular structure and biological activity information without performing cumbersome separation. To review works of literature on the evolution, principle and progress of NMR technologies for analysing mixtures, we focus on the successful application of NMR technologies in direct analysis of natural product extracts. Based on our research experience, academic tracking and extensive literature search, which involved, but not limited to, the use of various databases, like Web of Knowledge and PubMed. The keywords used, in various combinations, to retrieve recent literature on the successful application of NMR technologies to sheer natural product extracts, and excluded artificially natural product mixture and biofluid. NMR technologies for direct natural extracts analysis, including two-dimensional J-resolved spectroscopy (2D-JRES), pure shift NMR, diffusion-ordered NMR spectroscopy (DOSY), statistical correlation spectroscopy (STOCSY), concentration-ordered NMR spectroscopy (CORDY), saturation transfer difference (STD) and water-ligand observed via gradient spectroscopy (WaterLOGSY) were illustrated. By these methods, molecular structure and biological activity information will be directly obtained from NMR analysis of natural products extract, aiming to save experimental time and expenses.

Structural Evolution of Paramagnetic Lanthanide Compounds in Solution Compared to Time- and Ensemble-Average Structures.

Anisotropy in the magnetic susceptibility strongly influences the paramagnetic shifts seen in nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) experiments. A previous study on a series of C3-symmetric prototype MRI contrast agents showed that their magnetic anisotropy was highly sensitive to changes in molecular geometry and concluded that changes in the average angle between the lanthanide-oxygen (Ln-O) bonds and the molecular C3 axis due to solvent interactions had a significant impact on the magnetic anisotropy and, consequently, the paramagnetic shift. However, this study, like many others, was predicated on an idealized C3-symmetric structural model, which may not be representative of the dynamic structure in solution at the single-molecule level. Here, we address this by using ab initio molecular dynamics simulations to simulate how the molecular geometry, in particular the angles between the Ln-O bonds and the pseudo-C3 axis, evolves over time in the solution, mimicking typical experimental conditions. We observe large-amplitude oscillations in the O-Ln-C̃3 angles, and complete active space self-consistent field spin-orbit calculations show that this leads to similarly large oscillations in the pseudocontact (dipolar) paramagnetic NMR shifts. The time-averaged shifts show good agreement with experimental measurements, while the large fluctuations suggest that an idealized structure provides an incomplete description of the solution dynamics. Our observations have significant implications for modeling the electronic and nuclear relaxation times in this and other systems where the magnetic susceptibility is exquisitely sensitive to the molecular structure.

Flipping hosts in hyperfine fields of paramagnetic guests

Nuclear magnetic resonance (NMR) spectroscopy of paramagnetic systems is an advancing field that contributes to characterization of the structure and magnetism of molecules, molecular assemblies, biomolecules, and materials. In this account, we utilize the hyperfine field intrinsic to ruthenium(III) coordination compounds to uncover details of their supramolecular host-guest binding with cyclodextrins. We interpret perturbations of the 1H NMR shifts of the host to suggest the binding mode of the interaction. We demonstrate how the size of the cyclodextrin cavity and the structural modification of the guest affect whether the binding occurs through a head or tail portal of the cyclodextrin and attempt to explain the observed differences in terms of molecular shapes and the charge-distribution complementarity between the host and guest. Paramagnetic NMR spectroscopy of host-guest systems is shown to be a useful tool for supramolecular and structural chemists investigating modes of molecular encapsulation.

Deep Learning Methodology for Obtaining Ultraclean Pure Shift Proton Nuclear Magnetic Resonance Spectra

Nuclear magnetic resonance (NMR) is one of the most powerful analytical techniques. In order to obtain high-quality NMR spectra, a real-time Zangger-Sterk (ZS) pulse sequence is employed to collect low-quality pure shift NMR data with high efficiency. Then, a neural network named AC-ResNet and a loss function named SM-CDMANE are developed to train a network model. The model with excellent abilities of suppressing noise, reducing line widths, discerning peaks, and removing artifacts is utilized to process the acquired NMR data. The processed spectra with noise and artifact suppression and small line widths are ultraclean and high-resolution. Peaks overlapped heavily can be resolved. Weak peaks, even hidden in the noise, can be discerned from noise. Artifacts, even as high as spectral peaks, can be removed completely while not suppressing peaks. Eliminating perfectly noise and artifacts and smoothing baseline make spectra ultraclean. The proposed methodology would greatly promote various NMR applications.

Unsupervised Analysis of Small Molecule Mixtures by Wavelet-Based Super-Resolved NMR.

Resolving small molecule mixtures by nuclear magnetic resonance (NMR) spectroscopy has been of great interest for a long time for its precision, reproducibility, and efficiency. However, spectral analyses for such mixtures are often highly challenging due to overlapping resonance lines and limited chemical shift windows. The existing experimental and theoretical methods to produce shift NMR spectra in dealing with the problem have limited applicability owing to sensitivity issues, inconsistency, and/or the requirement of prior knowledge. Recently, we resolved the problem by decoupling multiplet structures in NMR spectra by the wavelet packet transform (WPT) technique. In this work, we developed a scheme for deploying the method in generating highly resolved WPT NMR spectra and predicting the composition of the corresponding molecular mixtures from their 1H NMR spectra in an automated fashion. The four-step spectral analysis scheme consists of calculating the WPT spectrum, peak matching with a WPT shift NMR library, followed by two optimization steps in producing the predicted molecular composition of a mixture. The robustness of the method was tested on an augmented dataset of 1000 molecular mixtures, each containing 3 to 7 molecules. The method successfully predicted the constituent molecules with a median true positive rate of 1.0 against the varying compositions, while a median false positive rate of 0.04 was obtained. The approach can be scaled easily for much larger datasets.

Static and dynamic magnetic properties of the spin- triangle lattice antiferromagnet Na3Fe(PO4)2 studied by 31P NMR

31P nuclear magnetic resonance (NMR) measurements have been carried out to investigate the magnetic properties and spin dynamics of Fe3+ (S = 5/2) spins in the two-dimensional triangular lattice (TL) compound Na3Fe(PO4)2. The temperature (T) dependence of nuclear spin-lattice relaxation rates () shows a clear peak around Néel temperature, K, corresponding to an antiferromagnetic (AFM) transition. From the temperature dependence of NMR shift (K) above , an exchange coupling between Fe3+ spins was estimated to be K using the spin-5/2 Heisenberg isotropic-TL model. The temperature dependence of divided by the magnetic susceptibility (χ), , above proves the AFM nature of spin fluctuations below in the paramagnetic state. In the magnetically ordered state below , the characteristic rectangular shape of the NMR spectra is observed, indicative of a commensurate AFM state in its ground state. The strong temperature dependence of 1/T 1 in the AFM state is well explained by the two-magnon (Raman) process of the spin waves in a 3D antiferromagnet with a spin-anisotropy energy gap of 5.7 K. The temperature dependence of sublattice magnetization is also well reproduced by the spin waves. Those results indicate that the magnetically ordered state of Na3Fe(PO4)2 is a conventional 3D AFM state, and no obvious spin frustration effects were detected in its ground state.

Multi-spectroscopic characterization of bitumen and its polarity-based fractions

• Insights on H/C-ratio, aromatic systems and average molecular sizes are obtained. • NMR reveals varying chain lengths of aliphatic substituents throughout the fractions. • Characteristic NMR shifts in the resins suggest presence of some esters or amides. • 31 P NMR after derivatization, shows minor amounts of alcohols and carboxylic acids. Bitumen contains a complex mixture of molecules, ranging from simple linear alkanes to complex polyaromatic systems. Used in road construction, those structures are permanently influenced by atmospheric processes, which result in oxidation, degradation, loss of volatiles and structural rearrangements. In order to understand those molecular changes, a profound description of the unaltered system is needed. In this study, structural and chemical peculiarities of bitumen and its polarity-based fractions were analysed using nuclear magnetic resonance (NMR) techniques in combination with infrared/fluorescence spectroscopy and elemental analysis. The bitumen sample was separated into saturates, aromatics, resins and asphaltenes. Elemental analysis provided insights into variations of heteroatom content and hydrogen-carbon ratio among the fractions. Fluorescence spectroscopy showed an increase in the aromatic condensation degree with rising polarity. The combination of heteronuclear single quantum coherence (HSQC) and elemental analysis facilitated the assessment of length and branching extent of aliphatic side chains, throughout the fractions. Another 2D NMR technique, heteronuclear multiple bond coherence (HMBC) found evidence for minor contributions of carboxylic esters or amides within the resins. Hydroxyl and amino groups were analysed by 31 P NMR after derivatization, revealing minor amounts of alcohols, amides and carboxylic acids in the most polar fractions. Additionally, diffusion ordered spectroscopy was carried out to obtain estimations on average molecular sizes and weights. Applying in-depth NMR analysis to bitumen and combining it with complementary analytical techniques, this study provides a unique basis for future ageing studies and demonstrates how multi-spectroscopy can give new insights into bitumen structure and chemistry.

125Te NMR study of the bulk of topological insulators Bi2Te3 and Sb2Te3

AbstractThe narrow band gap semiconductors Bi2Te3 and Sb2Te3 are well known for their room temperature thermoelectric performance. Recently, they were shown to serve as model systems of three‐dimensional topological insulators with a bulk band gap and very robust, spin‐resolved surface states due to a special band inversion in their periodic bulk. Evidently, it is of interest to investigate the special surface states with a local probe like nuclear magnetic resonance (NMR). However, especially the bulk NMR of these materials shows peculiar and rather unexpected phenomena, e. g., a magnetic field induced deformation of the local charge symmetry and excessive line broadening due to a special internuclear coupling. Here we report a comprehensive account of the room temperature NMR properties of the spin‐1/2 nucleus 125Te of single crystalline Bi2Te3 and Sb2Te3. This includes a very unusual spin‐lattice relaxation that seems not to be reflected in the NMR shift, as well as an exchange enhanced NMR line broadening.