Two-dimensional Correlation Spectra Research Articles

ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability of Polygonatum kingianum.

Medicinal plants have incredibly high economic value, and a practical evaluation of their quality is the key to promoting industry development. The deep learning model based on residual convolutional neural network (ResNet) has the advantage of automatic extraction and the recognition of Fourier transform near-infrared spectroscopy (FT-NIR) features. Models are difficult to understand and interpret because of unknown working mechanisms and decision-making processes. Therefore, in this study, artificial feature extraction methods combine traditional partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models to understand and compare deep learning models. The results show that the ResNet model has significant advantages over traditional models in feature extraction and recognition. Secondly, preprocessing has a great impact on the feature extraction and feature extraction, and is beneficial for improving model performance. Competitive adaptive reweighted sampling (CARS) and variable importance in projection (VIP) methods screen out more feature variables after preprocessing, but the number of potential variables (LVs) and successive projections algorithm (SPA) methods obtained is fewer. The SPA method only extracts two variables after preprocessing, causing vital information to be lost. The VIP feature of traditional modelling yields the best results among the four methods. After spectral preprocessing, the recognition rates of the PLS-DA and SVM models are up to 90.16% and 88.52%. For the ResNet model, preprocessing is beneficial for extracting and identifying spectral image features. The ResNet model based on synchronous two-dimensional correlation spectra has a recognition accuracy of 100%. This research is beneficial to the application development of the ResNet model in foods, spices, and medicinal plants.

Comprehensive modified approaches to reducing the interference of moisture from an FTIR spectrum and the corresponding second derivative spectrum

We proposed a modified and improved approach to removing the interference of moisture from an IR spectrum and the corresponding second derivative spectrum. The temperature fluctuation in the air of the optical path and baseline-drift lead to the small but persistent presence of the interference of moisture. The problem has been successfully addressed by adopting a double-matching strategy. Additionally, two-dimensional correlationspectra (2D-COS) are generated using the second derivative or third derivative spectrum of the negative base 10 logarithms of the single-beam spectra, thereby removing the linear slope or quadratic portion of baseline-drift. Using the newly adopted approach, the residual interferences of moisture are attenuated. We applied the new approach to the IR spectra and the second derivative spectra of neat hexadecanol and biaxially oriented polypropylene (BOPP) film, and some promising preliminary results are obtained. In hexadecanol, two highly overlapping peaks at 1464 and 1463 cm−1 are revealed. In BOPP, the envelope at 1458 cm−1 is found to be composed of a number of sub-peaks.

Quantitative analysis of near infrared spectroscopic data based on dual-band transformation and competitive adaptive reweighted sampling

Near infrared (NIR) spectroscopy has the characteristics of rapid processing, nondestructive analysis and on-line detection. This technique has been widely used in the fields of quantitative determination and substance content analysis. However, for complex NIR spectral data, most traditional machine learning models cannot carry out effective quantitative analyses (manifested as underfitting; that is, the training effect of the model is not good). Small amounts of available data limit the performance of deep learning-based infrared spectroscopy methods, while the traditional threshold-based feature selection methods require more prior knowledge. To address the above problems, this paper proposes a competitive adaptive reweighted sampling method based on dual band transformation (DWT-CARS). DWT-CARS includes four types in total: CARS based on integrated two-dimensional correlation spectrum (i2DCOS-CARS), CARS based on difference coefficient (DI-CARS), CARS based on ratio coefficient (RI-CARS) and CARS based on normalized difference coefficient (NDI-CARS). We conducted comparative experiments on three datasets; compared to traditional machine learning methods, our method achieved good results, demonstrating that this method has considerable prospects for the quantitative analysis of near-infrared spectroscopic data. To further improve the performance and stability of this method, we combined the idea of integrated modeling and constructed a partial least squares model based on Monte Carlo sampling for the samples obtained by CARS (DWT-CARS-MC-PLS). Through comparative experiments, we verified that the integrated model could further enhance the accuracy and stability of the results.

Estimating the synergistic formation of humus by abiotic and biotic pathways during composting

Promoting humus production by composting is meaningful for decreasing organic wastes pollution, and repairing contaminated environment. However, the interaction of multiple humus formation pathways has limited to understand how to improve humus formation by composting. Here, humus formation has been divided into biotic and abiotic pathways to quantify their synergy to humus formation. Monomolecular reducing sugars, amino acids and protocatechuic acid acted as precursors to simulate humus formation during composting in 20 days. PARAFAC analysis of humic acids structure presented that the humus formed by abiotic process was more complex and quicker. While two-dimensional correlation spectra illustrated that compounds derived from biotic process were steadier than the abiotic one. The core microorganisms which transform simple components into complex ones could explain it, which further illustrated that the microbial metabolism can effectively promote the complexity of humus either. Additionally, to analyze the contribution of biotic and abiotic pathways to humus formation, the synergy of the two pathways has been quantified. Abiotic synthesis, biotic degradation, and biotic synthesis were accounted for 30.68%, 43.58% and 25.74%. Therefore, promoting humus production by abiotic process may be an efficient way to reduce organic waste pollution with composting, especially for the protein-like and cellulose wastes . • Humus formation has been approximately quantitative analyzed. • Abiotic synthesis contributed much to humus formation during stimulate composting. • Humus structure formed by abiotic pathway was faster. • Biotic pathway promoted HA structural complexity with higher efficiency.

Solid-State NMR Reveals Asymmetric ATP Hydrolysis in the Multidrug ABC Transporter BmrA.

The detailed mechanism of ATP hydrolysis in ATP-binding cassette (ABC) transporters is still not fully understood. Here, we employed 31P solid-state NMR to probe the conformational changes and dynamics during the catalytic cycle by locking the multidrug ABC transporter BmrA in prehydrolytic, transition, and posthydrolytic states, using a combination of mutants and ATP analogues. The 31P spectra reveal that ATP binds strongly in the prehydrolytic state to both ATP-binding sites as inferred from the analysis of the nonhydrolytic E504A mutant. In the transition state of wild-type BmrA, the symmetry of the dimer is broken and only a single site is tightly bound to ADP:Mg2+:vanadate, while the second site is more ‘open’ allowing exchange with the nucleotides in the solvent. In the posthydrolytic state, weak binding, as characterized by chemical exchange with free ADP and by asymmetric 31P–31P two-dimensional (2D) correlation spectra, is observed for both sites. Revisiting the 13C spectra in light of these findings confirms the conformational nonequivalence of the two nucleotide-binding sites in the transition state. Our results show that following ATP binding, the symmetry of the ATP-binding sites of BmrA is lost in the ATP-hydrolysis step, but is then recovered in the posthydrolytic ADP-bound state.

Effect of leaching time on phytotoxicity of dissolved organic matter derived from black carbon based on spectroscopy

Black carbon (BC) exports huge amounts of its derived DOM from terrestrial ecosystems annually through a variety of ways (i.e., erosion or runoff migration). The pyrolytic feedstock type and temperature resulted in DOM derived from highly condensed aromatic and non-aromatic BC. However, the behaviors of low aromatic BC-derived DOM at diverse leaching time are poorly understood. In this work, low aromatic BCs were prepared by pyrolysis corn straws at 250 °C, 350 °C and 450 °C. Extraction experiments for four leaching time (6 h, 10 h, 15 h and 21 h) were set up to simulate BC-derived DOM generative process in nature. The phytotoxicity of BC-derived DOM was evaluated via germination index (GI). Spectral characteristics were discussed to analyze the phytotoxicity variations of fluorescence components composition at different time, including the excitation-emission matrix-parallel factor, two-dimensional correlation spectra and Fourier transform infrared spectroscopy. The results suggested that low aromatic BC-derived DOM might contain aromatic phenolic compounds. A longer time contributed to accumulate the complex, hard-to-use organic matters, leading to lower GI. These results would supplement the dynamic spectral characteristics of low aromatic BC-derived DOM and its environmental risks during the leaching process.

Structure of In Vitro-Synthesized Cellulose Fibrils Viewed by Cryo-Electron Tomography and 13C Natural-Abundance Dynamic Nuclear Polarization Solid-State NMR.

Cellulose, the most abundant biopolymer, is a central source for renewable energy and functionalized materials. In vitro synthesis of cellulose microfibrils (CMFs) has become possible using purified cellulose synthase (CESA) isoforms from Physcomitrium patens and hybrid aspen. The exact nature of these in vitro fibrils remains unknown. Here, we characterize in vitro-synthesized fibers made by CESAs present in membrane fractions of P. patens over-expressing CESA5 by cryo-electron tomography and dynamic nuclear polarization (DNP) solid-state NMR. DNP enabled measuring two-dimensional 13C–13C correlation spectra without isotope-labeling of the fibers. Results show structural similarity between in vitro fibrils and native CMF in plant cell walls. Intensity quantifications agree with the 18-chain structural model for plant CMF and indicate limited fibrillar bundling. The in vitro system thus reveals insights into cell wall synthesis and may contribute to novel cellulosic materials. The integrated DNP and cryo-electron tomography methods are also applicable to structural studies of other carbohydrate-based biomaterials.

Exploration of compressive sensing in the classification of frozen fish based on two-dimensional correlation spectrum

In order to classify imported frozen fish, effectively a spectral data compression method was presented based on two-dimensional correlation spectroscopy. In the experiment, the near-infrared spectral data of Oncorhynchus keta, Oncorhynchus nerka and Oncorhynchus gorbuscha of Salmonidae were collected. And two-dimensional correlation spectroscopy among the three fish samples was constructed. The study found that the auto-correlation peaks intensities at 650 nm, 1724 nm and 1908 nm were almost zero, which were taken as the separation point of the spectra. Therefore, each spectral data is divided into 4 segments and the integral of each segment is obtained. The original spectra of 201 points in each group were compressed into 4 points. Then, the compressed spectral data were input into the support vector machine to establish the discriminant model of three kinds of frozen fish. At the same time, the Competitive Adaptive Reweighted Sampling and the Successive Projections Algorithm were used to screen the original spectra. The classification results were compared with the result of the spectral data compression method of two-dimensional correlation spectroscopy. The result shows: the compression rate of the proposed method is 98.01%; the accuracy rate of support vector machine training set is 100%; the accuracy rate of validation set is up to 100%. The results shows that the proposed spectral data compression method based on two-dimensional correlation spectral technology has high compression rate and accurate classification.

Multi-spectroscopic investigation of the molecular weight distribution and copper binding ability of dissolved organic matter in Dongping Lake, China

The properties and metal-binding abilities of dissolved organic matter (DOM) rely on its molecular weight (MW) structure. In this study, the spatial differences of DOM in compositions, MW structures, and binding mechanisms with copper (Cu2+) in Dongping Lake were investigated by applying excitation-emission matrix combining parallel factor analysis (EEM-PARAFAC), synchronous fluorescence (SF) spectra, two-dimensional correlation spectra (2D-COS), and Fourier transform infrared (FTIR) spectra. The EDOM for the entrance of the Dawen River and PDOM for the macrophyte-dominated region were divided from DOM of Dongping Lake based on hierarchical clustering analysis (HCA) and principal component analysis (PCA) and were size-fractioned into MW < 500 kDa and <100 kDa fractions. According to EEM-PARAFAC, Dongping Lake was dominated by tryptophan-like substances with MW < 500 kDa. The concentration of PDOM was higher than that of EDOM (p < 0.05). 2D-COS showed that protein-like components preceded humic-like components binding to Cu2+ regardless of sample type (215 nm > 285 nm > 310–360 nm). The Cu2+ binding capacity of DOM exhibited specific differences in space, components, and molecular weights. The humic-like component 1 (C1) and tryptophan-like component 4 (C4) of PDOM showed stronger binding abilities than those of EDOM. Endogenous tryptophan-like component 4 (C4) had a higher binding affinity for Cu2+ than humic-like components (logKa: C4 > C1 > C2) in PDOM irrespective of MW. Humic-like components with MW < 500 kDa displayed higher binding potentials for Cu2+. FTIR spectra showed that the main participants of DOM-Cu complexation included aromatic hydrocarbons, aliphatic groups, amide Ⅰ bands, and carboxyl functional groups. This study provides spatial-scale insights into the molecular weight structure of DOM in influencing the behavior, fate, and bioavailability of heavy metals in lakes.

Pb intoxicated biomolecular changes in Cladonia convoluta studied using 2DCOS infrared spectroscopy coupled with chemometric analysis

Lead (Pb) is used in many industrial applications and is a toxic heavy metal that causes health hazards. Lichens are symbiotic associations that are environment friendly in removing toxic elements from the polluted environment. They can accumulate heavy metals and are therefore considered in biomonitoring of metal pollution. In this study, biochemical markers due to lead toxicity on Cladonia convoluta were revealed using two-dimensional correlation (2DCOS) spectra analysis. The 2DCOS enhances the resolution of the spectrum and gives more information on the interaction of functional groups within and between molecules which cannot be revealed from conventional spectra. Thus, 2DCOS is an efficient tool in monitoring even minute spectral changes in complex spectra. The results of conventional IR analysis showed changes in the band area. It follows in the order polysaccharides > proteins > lipids due to lead toxicity. The results of 2D correlation spectra show predominant changes at the pyranose ring. The hetero asynchronous map show correlation changes of pyranose ring with lipid and proteins. It reveals a change in carbohydrate synthesis due to toxicity. The highest positive and negative PCA loading values show deformation pyranose ring to glucan band. The results of HCA show samples are well separated into two major clusters with high separation values. ROC analysis gives good reliability of the test. Hence, 2DCOS coupled with chemometric analysis helps in understanding the biomolecular changes in C. convoluta effectively.

Dynamic nuclear polarization-enhanced, double-quantum filtered 13C-13C dipolar correlation spectroscopy of natural 13C abundant bone-tissue biomaterial

Here, we describe a method for obtaining a dynamic nuclear polarization (DNP)-enhanced double-quantum filtered (DQF) two-dimensional (2D) dipolar 13C-13C correlation spectra of bone-tissue material at natural 13C abundance. DNP-enhanced DQF 2D dipolar 13C-13C spectra were obtained using a few different mixing times of the dipolar-assisted rotational resonance (DARR) scheme and these spectra were compared to a conventional 2D through-space double-quantum (DQ)-single-quantum (SQ) correlation spectrum. While this scheme can only be used for an assignment purpose to reveal the carbon-carbon connectivity within a residue, the DQF 13C-13C dipolar correlation scheme introduced here can be used to obtain longer distance carbon-carbon constraints. A DQF pulse block is placed before the DARR mixing scheme for removing dominant 13C single-quantum (SQ) signals because these SQ 13C signals are overwhelmingly large compared to those 13C-13C dipolar cross-peaks generated and therefore saturate the dynamic range of the NMR detection. This approach exhibits strong enough 2D cross-peaks in a dipolar 13C-13C correlation spectrum and potentially provides pairwise 13C-13C dipolar constraints because the dipolar truncation effect as well as multi-step signal propagations involving a spin cluster that contains more than two spins can be ignored probabilistically. To obtain fast signal averaging, AsymPolPOK was used to provide a short 1H DNP signal build-up time (1.3 s) and to expedite our MAS DNP NMR acquisitions while still maintaining a satisfactory DNP enhancement factor (ε = 50). Under long DARR mixing, a t1-noise-like artifact was observed at a site that possesses a large chemical shift anisotropy (CSA) and a few different strategies to address this problem were discussed.

Discrimination of Adulterated Ginkgo Biloba Products Based on 2T2D Correlation Spectroscopy in UV-Vis Range.

Ginkgo biloba is a popular medicinal plant widely used in numerous herbal products, including food supplements. Due to its popularity and growing economic value, G. biloba leaf extract has become the target of economically motivated adulterations. There are many reports about the poor quality of ginkgo products and their adulteration, mainly by adding flavonols, flavonol glycosides, or extracts from other plants. In this work, we developed an approach using two-trace two-dimensional correlation spectroscopy (2T2D COS) in UV-Vis range combined with multilinear principal component analysis (MPCA) to detect potential adulteration of twenty G. biloba food supplements. UV-Vis spectral data are obtained for 80% methanol and aqueous extracts in the range of 245–410 nm. Three series of two-dimensional correlation spectra were interpreted by visual inspection and using MPCA. The proposed relatively quick and straightforward approach successfully differentiated supplements adulterated with rutin or those lacking ginkgo leaf extract. Supporting information about adulteration was obtained from the difference between the DPPH radical scavenging capacity of both extracts and from chromatographic (HPLC-DAD) fingerprints of methanolic samples.

Differentiation of ethanol–water clusters in Fenjiu by two-dimensional correlation fluorescence and Raman spectra

The taste of different flavor liquor is multifarious, but the same brand liquor with different quality is truth. The essence of supramolecular ethanol–water clusters and their intrinsic structural differences in three kinds of Fenjiu are studied by two-dimensional correlation spectra (2D-COS) of fluorescence and Raman. The 2D-COS of fluorescence reveals the prominent emission peaks of three Fenjiu are apparently different. The central fluorescence peak of Fenjiu (a) is located at 330 nm, corresponding to the cluster of (H2O)m(EtOH)n. In Fenjiu (b), the emission peak appears near 310 nm, while those of Fenjiu (c) appear mainly near 310 and 373 nm, corresponding to the clusters of (H2O)(EtOH)n and (H2O)m(EtOH), respectively. Based on 2D-COS of Raman, the peak of Fenjiu (b) at 3440 cm−1 changes initially, indicating its disorder degree is getting higher with continuous dilution with water. However, along with the dilution of Fenjiu (a) and Fenjiu (c), the peak located near 3200 cm−1 changes in priority, indicating that the degree of association between ethanol and water is high, and the clusters formed there are stable. Therefore, this work provides the combined methods to distinguish different supramolecular sets in Fenjiu, applying liquor differentiation in the future.

Interaction of heavy metals and biocide/herbicide from stormwater runoff of buildings with dissolved organic matter

Stormwater runoff from roofs and façades can be contaminated by heavy metals and biocides/herbicides. High efficiency on-site treatment methods are now urgently needed to safeguard the ecosystem. The basis for developing such treatment facilities is an in-depth understanding of their interactions with dissolved organic matter (DOM), as this affects their migration in the environment. Hence, the interactions between copper (Cu), zinc (Zn), benzyl-dimethyl-tetradecylammonium chloride dihydrate (BAC), mecoprop-P (MCPP) and DOM at pH 5 to 9 were investigated separately in this study. The evaluation of the interaction processes was achieved by applying excitation emission matrix and parallel factor analysis (EEM-PARAFAC) to titration samples; obtained data were fitted by two different models. Mechanisms involved in BAC/MCPP-DOM interactions were revealed by Fourier-transform infrared spectroscopy (FTIR) and two-dimensional correlation spectrum (2D-COS) analysis. Results showed that the applied DOM was composed of the two different fluorescent components C1 and C2. More interaction with C1 than with C2 was observed for both Cu/Zn and BAC/MCPP. Increasing the pH enhanced the interactions between Cu/Zn and DOM. At pH 5 with a maximum quencher addition, the remaining fluorescence of CuC1 and ZnC1 were 15.7% and 87.1%, respectively. Corresponding data at pH 9 decreased to 3% and 69.5%. Contrarily, interactions between BAC/MCPP and DOM were impaired by high pH conditions. The increase of pH from 5 to 9 with maximum BAC and MCPP added raised the remaining fluorescence of BAC-C1 and MCPP-C1 by 15.9% and 21.3% separately. The fitting outcomes from the Ryan-Weber equation (Cu/Zn titration) and the Stern-Volmer equation (BAC/MCPP titration) corresponded well with the titration studies. FTIR coupled with 2D-COS analysis revealed that mechanisms involved in BAC/MCPP titration include hydrogen bonding, π-π interaction, and electrostatic effect. The order of mechanisms taking effect during the interaction with DOM is affected by the molecular structure of BAC and MCPP.

Aging of polypropylene probed by near infrared spectroscopy

This paper describes the first in-depth attempt to characterize thermally induced aging of polypropylene (PP) samples by near infrared (NIR) spectroscopy. Significant levels of variation in NIR bands associated with short (amorphous-dominated) and long (crystalline-dominated) helices was readily captured when PP samples were subjected to thermal aging treatment. Partial least squares (PLS) regression models derived from the NIR spectra indicated significant level of correlation between the actual and predicted elongations of the samples. Analysis of PLS scores and two-dimensional (2D) correlation spectra derived from the aged PP samples revealed inner working mechanism of the regression model. Namely, the aging treatment essentially induces compositional change in crystalline and amorphous structures of the PP samples, which eventually affect the variation of the PLS scores. Thus, by utilizing the scores, it becomes possible to predict the change in the elongation property of the aged PP sample.

Evaluating the phytotoxicity of dissolved organic matter derived from black carbon.

Dissolved organic matter (DOM) derived from black carbon (BC) can migrate from soil to river by rainfall or snow melting in nature. Because of the incomplete biomass combustion, BC produced at various temperatures is mixed, which is hard to divide the DOM at single temperature. Then it is difficult to explore the properties and risks of DOM in detail. Therefore, corn straws were selected to prepare BC under different heating temperature (200°C, 250°C, 300°C, 350°C, 400°C and 450°C). Germination index combined the excitation-emission matrix-parallel factor (PARAFAC) and two-dimensional correlation spectra was employed to clarify the phytotoxicity and the PARAFAC components of DOM derived from BC at single temperature. Results showed that BC was hard to dissolve in water, but most of its DOM were toxic. Heating temperature promoted the formation of simple and complex fluorescent components. Combined with volume integration, it is the complex peaks of fluorescent components to determine the phytotoxicity of DOM derived from BC. These results would help to build a deep understanding of the fluorescence characteristics and toxicity of BC at different temperatures and emphasize the importance of reducing straw by burning.

Classification of Lingwu long jujube internal bruise over time based on visible near-infrared hyperspectral imaging combined with partial least squares-discriminant analysis

Early detection of internal bruise is one of the major challenges in postharvest quality sorting processes in Lingwu long jujube. In this study, the visible/near infrared (VIS/NIR) hyperspectral imaging system (400–1000 nm) was used to rapidly detect the intact and damaged jujube at five time points after mechanical damage (2 h, 4 h, 8 h, 12 h and 24 h). The region of interest of samples was selected by ENVI software, and the average spectrum was calculated for modelling. Different preprocessing methods were used to transform and enhance the spectral signal. Partial least squares-discriminant analysis (PLS-DA) classification models of the original and preprocessed spectra were established. The de-trending-PLS-DA model had the best effect, the accuracy of the calibration set and prediction set were 85.56% and 92.22%, respectively. The spectra were pre-processed by the de-trending algorithm, and the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), interval variable iterative space shrinkage approach (iVISSA), cluster analysis (CA), two-dimensional correlation spectra (2D-COS), UVE-SPA, CARS-SPA, iVISSA-SPA, CA-SPA and 2D-COS-SPA method were used to select characteristic variables. The PLS-DA model based on feature variables was established. The de-trending-CARS-PLS-DA model with 63 variables was found to be the optimal model. In the de-trending-CARS-PLS-DA model, the accuracy of calibration set and prediction set were 86.67% and 91.11%, respectively. It was found that the model accurately detected bruising in jujube at 8 h after bruising, and the accuracy of calibration set and prediction set were 100%. The results showed that VIS/NIR hyperspectral imaging technology combined with PLS-DA could discriminate different stages of bruising in Lingwu long jujube. This study may help develop an online detection system of bruising in Lingwu long jujube.

Direct Observation of Cholesterol Dimers and Tetramers in Lipid Bilayers.

Cholesterol is a ubiquitous component of mammalian cell membranes and affects membrane protein function. Although cholesterol-mediated formation of ordered membrane domains has been extensively studied, molecular-level structural information about cholesterol self-association has been absent. Here, we combine solid-state nuclear magnetic resonance (NMR) spectroscopy with all-atom molecular dynamics simulations to determine the oligomeric structure of cholesterol in phospholipid bilayers. Two-dimensional 13C-13C correlation spectra of differentially labeled cholesterol indicate that cholesterol self-associates in a face-to-face fashion at membrane concentrations from 17 to 44 mol %. 2D 13C and 19F spin-counting experiments allowed us to measure the average oligomeric number of these cholesterol clusters. At low cholesterol concentrations of ∼20%, the average cluster size is centered on dimers. At a high cholesterol concentration of 44%, which is representative of virus lipid envelopes and liquid-ordered domains of cell membranes, both dimers and tetramers are observed. The cholesterol dimers are found in both phase-separated membranes that contain sphingomyelin and in disordered and miscible membranes that are free of sphingomyelin. Molecular dynamics simulations support these experimental observations and moreover provide the lifetimes, stabilities, distributions, and structures of these nanoscopic cholesterol clusters. Taken together, these NMR and MD data strongly suggest that dimers are the basic structural unit of cholesterol in phospholipid bilayers. The direct observation of cholesterol dimers and tetramers provides a revised framework for studying cholesterol interactions with membrane proteins to regulate protein functions and for understanding the pathogenic role of cholesterol in diseases.

Effect of temperature on the interaction of cellulose/1-allyl-3-methyl imidazolium chloride solution

Ionic liquids have provided an efficient way for dissolving cellulose, but the study of its dissolution mechanism is still a major challenge. In this paper, in-situ FTIR spectroscopy, two-dimensional correlation spectrum (2Dcos) and in-situ 1H NMR spectroscopy were used to investigate the interaction between cellulose and 1-allyl-3-methyl imidazolium chloride ([Amim][Cl]) during heating. The results reveal that as the temperature increases, the water in [Amim][Cl] is gradually evaporated, and the hydrogen-bonding interaction between the hydroxyl groups in cellulose and [Amim][Cl] is destroyed. Combined with the in-situ infrared results, it can be inferred from the 2Dcos spectrum that the hydroxyl groups of cellulose not only form hydrogen bonds with C4,5-H and C2H on the [Amim][Cl] cations, but also form hydrogen bonds with chloride ions in [Amim][Cl], thus cellulose can form hydrogen bonds with both the cations and anions of [Amim][Cl]. This is the main reason why [Amim][Cl] has good cellulose solubility.

Excited state geometry of β-carotene influenced by environments: the nature and decisive role of solvent revealing by two-dimensional resonance Raman correlation spectroscopy.

Resonance Raman scattering can be used to investigate the ground and excited state information of carotenoid. It is known that the Dushinsky rotation can significantly influence the resonant Raman intensity of β-carotene (β-car). The excited state geometry revealed by the double components feature of the C = C stretching vibrational modes and the environmental dependence of the Raman intensity for each component remain unknown. We explore the influence of environmental factors on the relative intensity of these two C = C stretching vibration modes and perform two-dimensional resonance Raman correlation analysis to reveal the changes on β-car excited state geometry. The results show that the relative wavelength difference between the 0-0 absorption and the excitation is the key factor that decides the intensity ratio of the two components and that the intensity of each mode is modulated by environmental factors. This modulation is closely related to the excited state geometry and dynamics, effective conjugation length, and electron-phonon coupling constant. It also shows that the asynchronous cross-peaks in the two-dimensional resonance Raman correlation spectrum (2DRRCOS) can effectively characterize the degree of the varied electron-phonon coupling with the changing conditions. These results are not only complementary to the research on the excited states of carotenoids but also applicable to investigate the environmental dependence of Raman intensity for a lot of π-conjugated molecules.