One-dimensional 1H NMR Research Articles

Spectrochemical Insights into Aromatic Hydrocarbons in 1,2-Disubstituted Naphthalenes: Investigations Using 1D and 2D NMR Spectroscopy and X-Ray Crystallography Analysis

Naphthalene and its derivatives are often determined as markers of environmental pollution, although they are also recognized as crucial building blocks in many beneficial compounds. In the course of this study, a range of 1,2-disubstituted naphthalenes bearing nitro, amine, sulfonate, chlorine, and hydroxyl substituents were synthesized and characterized using several spectroscopic techniques, including FT-IR,1H NMR,13C NMR, DEPT-90, DEPT-135, DEPTq, elemental analysis, and high-resolution mass (HRMs). The NMR analysis of the aromatic hydrocarbon moiety in these compounds emphasized the impact of substituent variations on the appearance of 1H and 13C NMR signals. To resolve the issue of overlapping one-dimensional (1D) 1H NMR signals and determine the connectivity of hydrogen and carbon atoms from the aromatic naphtayl ring, we employed a “two-dimensional” experiment, using homonuclear correlation spectroscopy (2D 1H-1H COSY) and heteronuclear single quantum correlation (2D 1H - 13C HSQC) and heteronuclear multiple‐bond correlation spectroscopy (2D 1H - 13C HMBC). This work also investigated the correlation between 1H NMR signals and thin-layer chromatography (TLC) data. X-ray crystallography data validates that the presence of a specific group, not aligned coplanarly with the naphthyl ring, exerts a spatial influence on the manifestation of 1H NMR signals, alongside other electronic effects. This study will offer NMR data, providing insights into the potential applications and analyses of naphthalene derivatives across various theoretical and practical domains.

Structure, Dynamics, and Reactivity of Encapsulated Molecules in Restricted Spaces: Arylazoisoxazoles within an Octa Acid Capsule.

In this study, a well-defined organic capsule assembled from two octa acid (OA) molecules acting as host and select arylazoisoxazoles (AAIO) acting as guests were employed to demonstrate that confined molecules have restricted freedom that translates into reaction selectivity in both ground and excited states. The behavior of these AAIO guests in confined capsules was found to be different from that found in both crystals, where there is very little freedom, and in isotropic solvents, where there is complete freedom. Through one-dimensional (1D) and two-dimensional (2D) 1H NMR spectroscopic experiments, we have established a relationship between structure, dynamics and reactivity of molecules confined in an OA capsule. Introduction of CF3 and CH3 substitution at the 4-position of the aryl group of AAIO reveals that in addition to space confinement, weak interactions between the guest and the OA capsule control the dynamics and reactivity of guest molecules. 1H NMR studies revealed that there is a temperature-dependence to guest molecules tumbling (180° rotation along the capsular short axis) within an OA capsule. While 1H NMR points to the occurrence of tumbling motion, MD simulations and simulation of the temperature-dependent NMR signals provide an insight into the mechanism of tumbling within OA capsules. Thermal and photochemical isomerization of AAIO were found to occur within an OA capsule just as in organic solvents. The observed selectivity noted during thermal and photo induced isomerization of OA encapsulated AAIOs can be qualitatively understood in terms of the well-known concepts due to Bell-Evans-Polanyi (BEP principle), Hammond and Zimmerman.

Simulated LC-MS Data Set for Assessing the Metabolomics Data Processing Pipeline Implemented into MVAPACK.

Metabolomics commonly relies on using one-dimensional (1D) 1H NMR spectroscopy or liquid chromatography-mass spectrometry (LC-MS) to derive scientific insights from large collections of biological samples. NMR and MS approaches to metabolomics require, among other issues, a data processing pipeline. Quantitative assessment of the performance of these software platforms is challenged by a lack of standardized data sets with "known" outcomes. To resolve this issue, we created a novel simulated LC-MS data set with known peak locations and intensities, defined metabolite differences between groups (i.e., fold change > 2, coefficient of variation ≤ 25%), and different amounts of added Gaussian noise (0, 5, or 10%) and missing features (0, 10, or 20%). This data set was developed to improve benchmarking of existing LC-MS metabolomics software and to validate the updated version of our MVAPACK software, which added gas chromatography-MS and LC-MS functionality to its existing 1D and two-dimensional NMR data processing capabilities. We also included two experimental LC-MS data sets acquired from a standard mixture andMycobacterium smegmatiscell lysates since a simulated data set alone may not capture all the unique characteristics and variability of real spectra needed to assess software performance properly. Our simulated and experimental LC-MS data sets were processed with the MS-DIAL and XCMSOnline software packages and our MVAPACK toolkit to showcase the utility of our data sets to benchmark MVAPACK against community standards. Our results demonstrate the enhanced objectivity and clarity of software assessment that can be achieved when both simulated and experimental data are employed since distinctly different software performances were observed with the simulated and experimental LC-MS data sets. We also demonstrate that the performance of MVAPACK is equivalent to or exceeds existing LC-MS software programs while providing a single platform for processing and analyzing both NMR and MS data sets.

Estimation of conformational entropy for ionic liquids from NMR spectroscopy

Abstract Conformational entropy (Sconf) plays a key role in the low melting points of ionic liquids. In this study, a nuclear magnetic resonance (NMR)-based approach was developed to experimentally estimate Sconf of 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)amide. J-coupling constants from the 1D 1H NMR and 2D spectra of the butyl group in the cation were analyzed using the Karplus equation optimized by density functional theory calculations. The obtained conformational population and entropy values coincided reasonably well with previously reported results, indicating the usefulness of the NMR approach.

Geographical discrimination of Asian red pepper powders using 1H NMR spectroscopy and deep learning-based convolution neural networks

This study investigated an innovative approach to discriminate the geographical origins of Asian red pepper powders by analyzing one-dimensional 1H NMR spectra through a deep learning-based convolution neural network (CNN). 1H NMR spectra were collected from 300 samples originating from China, Korea, and Vietnam and used as input data. Principal component analysis − linear discriminant analysis and support vector machine models were employed for comparison. Bayesian optimization was used for hyperparameter optimization, and cross-validation was performed to prevent overfitting. As a result, all three models discriminated the origins of the test samples with over 95 % accuracy. Specifically, the CNN models achieved a 100 % accuracy rate. Gradient-weighted class activation mapping analysis verified that the CNN models recognized the origins of the samples based on variations in metabolite distributions. This research demonstrated the potential of deep learning-based classification of 1H NMR spectra as an accurate and reliable approach for determining the geographical origins of various foods.

Compound-Specific 1D 1H NMR Pulse Sequence Selection for Metabolomics Analyses.

NMR-based metabolomics approaches have been used in a wide range of applications, for example, with medical, plant, and marine samples. One-dimensional (1D) 1H NMR is routinely used to find out biomarkers in biofluids such as urine, blood plasma, and serum. To mimic biological conditions, most NMR studies have been carried out in an aqueous solution where the high intensity of the water peak is a major problem in obtaining a meaningful spectrum. Different methods have been used to suppress the water signal, including 1D Carr-Purcell-Meiboom-Gill (CPMG) presat, consisting of a T2 filter to suppress macromolecule signals and reduce the humped curve in the spectrum. 1D nuclear Overhauser enhancement spectroscopy (NOESY) is another method for water suppression that is used routinely in plant samples with fewer macromolecules than in biofluid samples. Other common 1D 1H NMR methods such as 1D 1H presat and 1D 1H ES have simple pulse sequences; their acquisition parameters can be set easily. The proton with presat has just one pulse and the presat block causes water suppression, while other 1D 1H NMR methods including those mentioned above have more pulses. However, it is not well known in metabolomics studies because it is used only occasionally and in a few types of samples by metabolomics experts. Another effective method is excitation sculpting to suppress water. Herein, we evaluate the effect of method selection on signal intensities of commonly detected metabolites. Different classes of samples including biofluid, plant, and marine samples were investigated, and recommendations on the advantages and limitations of each method are presented.

Effectiveness of mid-infrared spectroscopy for the prediction of cow milk metabolites

Proton nuclear magnetic resonance (1H NMR) spectroscopy is acknowledged as one of the most powerful analytical methods with cross-cutting applications in dairy foods. To date, the use of 1H NMR spectroscopy for the collection of milk metabolic profile is hindered by costly and time-consuming sample preparation and analysis. The present study aimed at evaluating the accuracy of mid-infrared spectroscopy (MIRS) as a rapid method for the prediction of cow milk metabolites determined through 1H NMR spectroscopy. Bulk milk (n = 72) and individual milk samples (n = 482) were analyzed through one-dimensional 1H NMR spectroscopy and MIRS. Nuclear magnetic resonance spectroscopy identified 35 milk metabolites, which were quantified in terms of relative abundance, and MIRS prediction models were developed on the same 35 milk metabolites, using partial least squares regression analysis. The best MIRS prediction models were developed for galactose-1-phosphate, glycerophosphocholine, orotate, choline, galactose, lecithin, glutamate, and lactose, with coefficient of determination in external validation from 0.58 to 0.85, and ratio of performance to deviation in external validation from 1.50 to 2.64. The remaining 27 metabolites were poorly predicted. This study represents a first attempt to predict milk metabolome. Further research is needed to specifically address whether developed prediction models may find practical application in the dairy sector, with particular regard to the screening of dairy cows' metabolic status, the quality control of dairy foods, and the identification of processed milk or incorrectly stored milk.

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H-13C HSQC-Based Metabolomics.

Two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with one-dimensional (1D) 1H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D 1H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D 1H-13C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδo) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδo for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D 1H-13C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.

Hafnium(IV) Chemistry with Imide-Dioxime and Catecholate-Oxime Ligands: Unique {Hf5} and Metalloaromatic {Hf6}-Oxo Clusters Exhibiting Fluorescence.

Hafnium(IV) molecular species have gained increasing attention due to their numerous applications ranging from high-resolution nanolithography, heterogeneous catalysis, and electronics to the design of molecule-based building blocks in metal-organic frameworks (MOFs), with applications in gas separation, sorption, luminescence sensing, and interim storage of radioactive waste. Despite great potential, their chemistry is relatively underdeveloped. Here, we use strong chelators (2Z-6Z)-piperidine-2,6-dione (H3pidiox) and 2,3-dihydroxybenzaldehyde oxime (H3dihybo) to synthesize the first ever reported pentanuclear {Hf5/H3pidiox} and hexanuclear {Hf6/H3dihybo} clusters (HfOCs). The {Hf6} clusters adopt unique core structures [Hf6IV(μ3-O)2(μ-O)3] with a trigonal-prismatic arrangement of the six hafnium atoms and have been characterized via single-crystal X-ray diffraction analysis, UV-vis spectroscopy in the solid state, NMR, fluorescence spectroscopy, and high-resolution mass spectrometry in solution. One-dimensional (1D) and two-dimensional (2D) 1H NMR and mass spectroscopies reveal the exceptional thermodynamic stability of the HfOCs in solution. Interestingly, the conjunction of the oxime group with the catechol resulted in the remarkable reduction of the clusters' band gap, below 2.51 eV. Another prominent feature is the occurrence of pronounced metalloaromaticity of the triangular {Hf3} metallic component revealed by its NICSzz scan curve calculated by means of density functional theory (DFT). The NICSzz(1) value of -44.6 ppm is considerably higher than the -29.7 ppm found at the same level of theory for the benzene ring. Finally, we investigated the luminescence properties of the clusters where 1 emits light in the violet region despite the lack of fluorescence of the free H3pidiox ligand, whereas the {Hf6} 3 shifts the violet-emitting light of the H3dihybo to lower energy. DFT calculations show that this fluorescence behavior stems from ligand-centered molecular orbital transitions and that HfIV coordination has a modulating effect on the photophysics of these HfOCs. This work not only represents a significant milestone in the construction of stable low-band-gap multinuclear HfIV clusters with unique structural features and metal-centered aromaticity but also reveals the potential of Hf(IV) molecule-based materials with applications in sensing, catalysis, and electronic devices.

Grazing affects metabolic pattern of individual cow milk.

Effective traceability tools able to characterize milk from pasture are important to safeguard low-input farming systems, niche dairy products, and local traditions. The aims of the present study were to investigate the ability of proton nuclear magnetic resonance (1H NMR) spectroscopy to discriminate between milk produced from cows before and after the beginning of the grazing season, and to assess the effects of grazing on milk metabolites. The research trial involved a single alpine holding with 72 lactating cows. Individual milks were repeatedly sampled from the same animals before (i.e., d -3 and -1) and after (i.e., d 2, 3, 7, 10, and 14) the onset of the grazing period. One-dimensional 1H NMR spectra of milk extracts were collected through a Bruker spectrometer. Random forest discriminant analysis was applied to 1H NMR spectra to predict the period of collection for each sample. Data concerning the relative abundance of milk metabolites were analyzed through a linear mixed model, which included the fixed effects of period of sampling, cow breed, stage of lactation, and parity, and the random effect of cow nested within breed. The random forest model exhibited great accuracy (93.1%) in discriminating between samples collected on d -3, -1, 2, and 3 and those collected on d 7, 10, and 14. Univariate analysis performed on the 40 detected metabolites highlighted that milk samples from pasture had lower levels of 14 compounds (with fumarate being the most depressed metabolite) and greater levels of 15 compounds (with methanol and hippurate being the most elevated metabolites). Results indicate that milk 1H NMR spectra are promising to identify milk produced in different conditions. Also, our study highlights that grazing is associated with significant changes of milk metabolic profile, suggesting the potential use of several metabolites as indicators of farm management.

Spatially encoded polarization transfer for improving the quantitative aspect of 1H–13C HSQC

Peak overlap hampers quantification in one-dimensional (1D) 1H NMR. 2D 1H -13C HSQC spectrum provides resolution superior to 1D 1H NMR. However, quantifying the components in a complex mixture with HSQC is not straightforward as in 1D 1H NMR. Quantification using HSQC could open up new avenues for studying metabolism. The variations in 1H–13C scalar couplings, T1, T2, and pulse imperfections contribute to this problem. Although T1 and T2 can be suitably chosen to minimize their deleterious effects, the differential polarization transfer for different resonances owing to large variations in 1H -13C couplings does not allow the cross-peak intensities to be directly correlated to the quantity of metabolites. Existing approaches are time-consuming. We show that spatial encoding of the polarization transfer delays in HSQC using sweep frequency pulses in the presence of a magnetic field gradient allows one to have a transfer of polarization from 1H to 13C insensitive to variations in 1H -13C couplings improving the quantitative aspect of HSQC. Comparisons to other QHSQC and perfected HSQC variants are also provided.

Thioflavin T fluorescence and NMR spectroscopy suggesting a non-G-quadruplex structure for a sodium binding aptamer embedded in DNAzymes

Recently, a Na+-binding aptamer was reported to be embedded in a few RNA-cleaving DNAzymes, including NaA43, Ce13d, and NaH1. The Na+ aptamer consists of multiple GG stretches, which is a prerequisite for the formation of G-quadruplex (G4) structures. These DNAzymes require Na+ for activity but show no activity in the presence of K+ or other metal ions. Given that DNA can selectively bind K+ by forming a G4 structure, this work aims to answer whether this Na+ aptamer also uses a G4 to bind Na+. Through comparative ThT fluorescence spectrometry studies, while a control G4 DNA exhibited notable fluorescence enhancement up to 5 mM K+ with a Kd of 0.28 ± 0.06 mM, the Ce13d DNAzyme fluorescence was negligibly perturbed with similar concentrations of K+. Opposed to this, Ce13d displayed specific remarkable fluorescence decrease with low millimolar concentrations of Na+. NMR experiments at two different pH values suggest that Ce13d adopts a significantly different conformation or equilibrium of conformations in the presence of Na+ versus K+ and has a more stable structure in the presence of Na+. Additionally, absence of characteristic G4 peaks in one-dimensional 1H NMR suggest that G4 is not responsible for the Na+ binding. This hypothesis is confirmed by the absence of characteristic peaks in the CD spectra of this sequence. Therefore, we concluded that the aptamer must be selective for Na+ and that it binds Na+ using a structural element that does not contain G4.

Spectroscopic characterization andthermal decomposition kinetics of 1,3-dibutyl-imidazolium bromide synthesized through a solvent-free and one-pot method

Imidazolium-based ionic liquids (ILs), as one of the most popular and investigated classes of ILs owing to their outstanding physicochemical features, find applications in different fields over the last decades. Their thermal stability is of paramount importance for their effective use at elevated temperatures. 1,3-dibutyl-imidazolium bromide ([BBIm][Br] is an interesting IL, which may be employed for different purposes. Nevertheless, its thermal stability and decomposition kinetic are scarce. In the present work, 1,3-dibutyl-imidazolium bromide ([BBIm][Br]) is successfully synthesized through a simple, clean, and efficient solvent-free and one-pot ultrasonic method. Its structure was assessed by DSC, FTIR, Raman spectroscopies, µXRF, one-dimensional (1-D) liquid state 1H, 13C, and 15N NMR. The 2-D NMR analyses thoroughly discussed herein corroborated the structure of the cation as well as the alkyl side chains. Its thermal stability and decomposition kinetic were performed with non-isothermal thermogravimetry analysis at different heating rates. The kinetic triplet was determined using isoconversional kinetic methods. It is revealed that [BBIm][Br] undergoes a single step mass loss in the temperature range of 180–380 °C for which the activation energy is 140 kJ/mol. Furthermore, it is proved that the decomposition mechanism that best represents the experimental data is the nth order (Fn) reaction mechanism.

Chemometric Discrimination of the Varietal Origin of Extra Virgin Olive Oils: Usefulness of 13C Distortionless Enhancement by Polarization Transfer Pulse Sequence and 1H Nuclear Magnetic Resonance Data and Effectiveness of Fusion with Mid-Infrared Spectroscopy Data.

The label authentication of monovarietal extra virgin olives is of great relevance from a socio-economical point of view. This work aims to gain insights into the prediction of the varietal origin of extra virgin olive oil (EVOO) samples obtained from single olive cultivars, French cultivars Olivière, Salonenque, and Tanche and Portuguese cultivars Blanqueta, Carrasquenha, and Galega Vulgar, collected in 2016-2017 and 2017-2018 harvest seasons. To pursue this study, spectroscopic approaches based on one-dimensional nuclear magnetic resonance (1D NMR) spectroscopy, namely, 1H and 13C NMR distortionless enhancement by polarization transfer (DEPT) 45 pulse sequence, and Fourier transform mid-infrared spectroscopy (FT-MIR) are used in combination with partial least square discriminant analysis (PLS1-DA). The results obtained by PLS1-DA models using 1H and 13C NMR DEPT 45 data are compared to those of PLS1-DA models using MIR data. The application of a control chart method allows for the optimization of the interpretation of the PLS1-DA results, and an efficient two-step strategy is proposed to improve the discrimination of the six studied cultivars. Then, NMR and MIR data are combined by either a mid- or high-level data fusion approach to further improve the discrimination. The models are also tested on samples from other cultivars to check their ability to reject varieties that were not considered in the calibration process.

Fast Identification of Food Thickeners by Nontargeted NMR-Spectroscopy.

Food thickeners are carbohydrate additives that can only be determined by long-term, multistep analysis. Fast methods to directly determine thickeners in food matrixes are therefore welcome. In this study, a rapid procedure based on the direct 1H NMR analysis of food samples dissolved in deuterated water was developed. Individual thickeners were assigned due to specific marker signals gleaned from two-dimensional NMR analyses. The combination of one-dimensional 1H NMR and DOSY experiments enabled unequivocal assignments of thickeners even in complex matrixes. Using this approach, gum arabic, carrageenan, agar-agar, galactomannans, and pectin could be identified in pastille, glaze, and fruit spread. Because of low concentrations (<0.5%-1%, w/w), the same thickeners and others such as xanthan gum and alginate could not be determined directly by NMR in curry sauce, rice pudding, choco milk drink, and lemon peel flavor. Moreover, NMR analyses of the hydrolysate did not reveal the specific monomeric units of the thickeners under study, as shown for the hydrolysate of lemon peel flavor. Nevertheless, the NMR approach could provide welcome means in the future to directly determine intact thickeners in food.

Introducing Molecular Structural Analysis Using a Guided Systematic Approach Combined with an Interactive Multiplatform Web Application

Performing the identification of organic chemical compounds from a set of spectroscopic data gives the opportunity to students to develop critical thinking and problem-solving skills. In this context, we developed a student-centered methodology for teaching molecular structural analysis to first-year undergraduate students. This systematic approach was implemented during classroom-training sessions and complemented by a home-based training program. Home-based activities involved a multiplatform Web-based application (ULg Spectra) combined with guided inquiries. ULg Spectra offers fully interactive tutorial/drill materials relying on mass, infrared, and nuclear magnetic resonance spectra (i.e., one-dimensional 1H NMR and 13C NMR spectra and two-dimensional 1H–13C HSQC spectra). A survey indicates that the vast majority of students valued the ULg Spectra application combined with guided inquiries, especially in terms of usability and usefulness. This approach prompted them to actively engage in problem solving, and student autonomy was improved. Statistical data demonstrated that low-, medium-, and high-training students’ groups showed increasing performance in the final exam. Interestingly, a statistically significant increase in final grades and success rate was also observed compared to previous years.

Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based metabolome profiling of urine samples from kidney cancer patients

Kidney cancer is one of the most frequently diagnosed cancers of the urinary tract in the world. Despite significant advances in kidney cancer treatment, no urine specific biomarker is currently used to guide therapeutic interventions. In an effort to address this knowledge gap, metabolic profiling of urine samples from 50 patients with kidney cancer and 50 healthy volunteers was undertaken using high-resolution proton nuclear magnetic resonance spectroscopy (1H NMR) and silver-109 nanoparticle enhanced steel target laser desorption/ionization mass spectrometry (109AgNPET LDI MS). Twelve potential urine biomarkers of kidney cancer were identified and quantified using one-dimensional (1D) 1H NMR metabolomics. Seven mass spectral features which differed significantly in abundance (p < 0.05) between kidney cancer patients and healthy volunteers were also detected using 109AgNPET-based laser desorption/ionization mass spectrometry (LDI MS). This work provides a framework to expand biomarker discovery that could be used as useful diagnostic or prognostic of kidney cancer progression

Photochemistry after fire: Structural transformations of pyrogenic dissolved organic matter elucidated by advanced analytical techniques

Pyrogenic organic matter (pyOM) is the solid residue left after pyrolysis of organic matter, a process occurring in nature during forest fires. Upon rain events, pyrogenic dissolved organic matter (pyDOM) leaches into the rain water and is transferred to the aquatic environment where it has been identified as an important component of the global carbon cycle. Overall, pyDOM consists of highly condensed aromatic compounds (ConAC) and poorly characterized aliphatic molecules. While the ConAC are thought to be persistent in the environment, a large ConAC accumulation that would match the calculated fluxes of pyDOM formation and export has not been observed. Photo-degradation has been identified as a major pyDOM sink, and it can potentially account for the discrepancy in the global mass balance of pyDOM.Photochemistry of pyDOM has been studied to a certain extent, but neither the structural transformation pathways of pyDOM nor how they relate to pyrolysis temperature and parent feedstocks are well understood. In response to this need, we have examined the products after photo-irradiation of pyDOM derived from a temperature series (250, 400, 525, 650 °C) of oak biochars made under controlled laboratory conditions, which can be considered model pyrogenic substances. Ultrahigh resolution mass spectrometry (FT-ICR-MS), one-dimensional 1H NMR, and two-dimensional 1H-1H total correlation spectroscopy (TOCSY) NMR were used to evaluate the compositional changes that occurred with photo-irradiation.In agreement with previous studies, pyDOM aryl groups were the most photo-labile, and alkyl and O-alkyl groups were photo-produced. A new finding is that polysubstituted olefinic moieties of pyDOM were also photo-degraded. Using TOCSY we identified several small molecular weight compounds whose abundances changed non-linearly across the combustion continuum which suggests that different radical processes occur in the different char leachates. Using mass spectrometric data we observed that pyDOM from oak chars made at lower temperatures (250 and 400 °C), as well as pyDOM from a grass char prepared at 650 °C, photo-transformed to a more hydrogenated and oxygenated composition. In contrast, pyDOM from higher temperature char leachates (525 and 650 °C) transformed predominantly into smaller aliphatic compounds. To explain these differences, we propose a photo-transformation mechanism for ConAC which includes oxygenation of ConAC, ring-opening reactions, and sequential decarboxylation yielding small aliphatic N-rich molecules (marine-like composition). These structures could be further polymerized by reactive oxygen species to form straight-chain alkanes if not consumed by microorganisms. Results from these controlled experiments reveal that the extent of photo-transformation appears to be a function of production temperature and parent feedstock. This study enhances our understanding of ConAC and pyDOM, their terrestrial-to-marine transfers, geochemical stability, and eventual fate in sunlit environments. Here we also show the value of structural characterization of pyDOM, and reveal the many unknowns left to be answered for this complex and analytically challenging type of organic matter.

Determination of the effective degree of cross-linking of porous cellulose membranes cross-linked with bifunctional epoxides

The cross-linking efficiency of a continuous process for the production of porous cellulose membranes cross-linked with 1,4-butanediol diglycidyl ether (BDDE) under variation of four different process parameters (concentration of cross-linker, catalyst concentration, reaction temperature and reaction time) was studied. The analysis of the resulting network properties is based on gravimetric and titrimetric methods complemented by one-dimensional 1H NMR analysis after degradation of the cross-linked cellulose membranes in aqueous zinc chloride solution. NMR results indicate that around 50 % of BDDE molecules are effectively cross-linking, the number being almost independent of the cross-linking conditions. Furthermore, thermogravimetric analysis of the cross-linked membranes showed a direct correlation between the degree of cross-linking and the decomposition temperature of the cellulose membranes.

Evaluation of Non-Uniform Sampling 2D 1H-13C HSQC Spectra for Semi-Quantitative Metabolomics.

Metabolomics is the comprehensive study of metabolism, the biochemical processes that sustain life. By comparing metabolites between healthy and disease states, new insights into disease mechanisms can be uncovered. NMR is a powerful analytical method to detect and quantify metabolites. Standard one-dimensional (1D) 1H-NMR metabolite profiling is informative but challenged by significant chemical shift overlap. Multi-dimensional NMR can increase resolution, but the required long acquisition times lead to limited throughput. Non-uniform sampling (NUS) is a well-accepted mode of acquiring multi-dimensional NMR data, enabling either reduced acquisition times or increased sensitivity in equivalent time. Despite these advantages, the technique is not widely applied to metabolomics. In this study, we evaluated the utility of NUS 1H–13C heteronuclear single quantum coherence (HSQC) for semi-quantitative metabolomics. We demonstrated that NUS improved sensitivity compared to uniform sampling (US). We verified that the NUS measurement maintains linearity, making it possible to detect metabolite changes across samples and studies. Furthermore, we calculated the lower limit of detection and quantification (LOD/LOQ) of common metabolites. Finally, we demonstrate that the measurements are repeatable on the same system and across different systems. In conclusion, our results detail the analytical capability of NUS and, in doing so, empower the future use of NUS 1H–13C HSQC in metabolomic studies.