Nuclear Magnetic Resonance Spectroscopy Signals Research Articles

Structural identification and antioxidant activity of trans-9, trans-11, cis-15-conjugated linolenic acid converted by probiotics

The study aimed to determine the chemical structures of octadecatrienoic acid isomers produced by probiotics through the bioconversion of α-linolenic acid and to assess their antioxidant capacities. The chemical structures were identified using nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS), while the antioxidant capacities were evaluated in vitro and in cellular. The NMR signals obtained allowed for definitive characterization, with the main ion fragments detected being m/z 58.0062, 59.0140, 71.0141, 113.0616, 127.0777, and 181.5833. Compounds at concentrations below 40 μM maintained the antioxidant capacity of HepG2 cells by protecting endogenous antioxidative enzymes and mitochondrial membrane potential. However, doses higher than 40 μM increase oxidative damage and mitochondrial dysfunction. These results confirmed the structure of the probiotic-derived compound as trans9, trans11, cis15-conjugated linolenic acid. Additionally, appropriate doses of CLNA can alleviate oxidative stress induced by AAPH, while high doses aggravate cellular damage. These findings provide foundational information for the further exploration of probiotic-derived edible lipids.

Geometry dependence of micron-scale NMR signals on NV-diamond chips

Small volume nuclear magnetic resonance spectroscopy (NMR) has recently made considerable progress due to rapid developments in the field of quantum sensing using nitrogen vacancy (NV) centers. These optically active defects in the diamond lattice have been used to probe unprecedented small volumes in the picoliter range with high spectral resolution. However, the NMR signal size depends strongly on both the diamond sensor’s and sample’s geometry. Using Monte-Carlo integration of sample spin dipole moments, the magnetic field projection along the orientation of the NV center for different geometries has been analyzed. We show that the NMR signal strongly depends on the NV-center's orientation with respect to the diamond surface. While the signal of currently used planar diamond sensors converges as a function of the sample volume, more optimal geometries lead to a logarithmically diverging signal. Finally, we simulate the expected signal for spherical, cylindrical and nearly-2D sample volumes, covering relevant geometries for interesting applications in NV-NMR such as single-cell biology or NV-based hyperpolarization. The results provide a guideline for NV-NMR spectroscopy of microscopic objects.

Rapid and accurate verification of drug identity, purity and quality by 1H-NMR using similarity calculations and differential NMR

The illegal trade in counterfeit and fake drugs is a worldwide multi-billion dollar industry, not only generating enormous economic losses, but health problems for the general population, through direct toxicity, treatment failure and the increased generation of antibiotic resistance. Techniques for high-throughput testing of suspect medicines are needed to face the challenges of the problem. In this study we show that with nuclear magnetic resonance spectroscopy (NMR) drug compliance can be verified in a few minutes, providing data on drug identity, purity and quality without the necessity to develop a specific methodology and using a direct extraction with deuterated solvent. The evaluation of the data is facilitated by similarity calculations and differential NMR spectroscopy. The viability and limitations of this method were assessed, with the application on five different drugs, namely sertraline hydrochloride, alprazolam, vitamin D3, enalapril maleate and paracetamol, in which the individual dosage quantity of the active ingredient ranged from 750 mg down to 0.25 mg. The appropriate sample weight, solvent and internal standard were determined for each drug and quantification was carried out by choosing the most adequate NMR signals for each compound and the internal standard. With the method the accuracy of the quantification is somewhat sacrificed for increased speed in the analysis, but the measurements offer excellent precision and offer the possibility of external calibration. Spectral similarity calculations and differential NMR spectroscopy were used to compare different generic brands and detect eventual undeclared constituents and contaminants. In one brand of alprazolam tablets the undeclared constituent tristearin was found, while in paracetamol tablets the contaminant para-aminophenol was found at a level above the allowed by the legislation. The applicability and limitations of the method are discussed.

An Automatic Denoising Method for NMR Spectroscopy Based on Low-Rank Hankel Model

Nuclear magnetic resonance (NMR) spectroscopy, whose time domain data is modeled as the sum of damped exponential signals, has become an indispensable tool in various scenarios, such as biomedicine, biology, and chemistry. NMR spectroscopy signals, however, are usually corrupted by Gaussian noise in practice, raising difficulties in sequential analysis and quantification. The low-rank Hankel property of exponential signals plays an important role in the denoising issue, but selecting an appropriate parameter still remains a problem. In this work, we explore the effect of the regularization parameter of a convex optimization denoising method based on low-rank Hankel matrices for exponential signals corrupted by Gaussian noise. An accurate estimate on the spectral norm of weighted Hankel matrices is provided as a guidance to set the regularization parameter. The bound can be efficiently calculated since it only depends on the standard deviation of the noise and a constant. Aided by the bound, one can easily obtain an auto-setting regularization parameter to produce promising denoised results. Our results on synthetic and realistic NMR spectroscopy data demonstrate a superior denoising performance of the proposed approach over typical Cadzow and the state-of-the-art QR decomposition methods, especially in the low signal-to-noise ratio regime.

Insights into the behavior of ethylene oxide-1,2-epoxybutane diblock copolymers in water as a function of temperature and the presence of colloidal silica

HypothesisNonionic surfactants have been widely used for many consumer products and industrial processes, and their applications often involve temperature-cycling across cloud point temperature (Tcloud). To explore the behavior of nonionic surfactants across Tcloud and when mixed with colloidal silica at a very dilute concentration around 0.1 wt%, a series of 1,2-epoxybutane-capped alcohol ethoxylates (BAEs) with various cloud points is used as a model system. ExperimentsBAEs with cloud points from 15 to 64 °C were successfully prepared by varying the lengths of 1,2-epoxybutane (BO) and ethylene oxide (EO) blocks and their phase behavior across Tcloud was studied using nuclear magnetic resonance spectroscopy (NMR), dynamic light scattering (DLS) and differential scanning calorimetry (DSC). FindingsIn the absence of silica, the NMR signals are not greatly affected by the cloud point transition, but both the water and surfactant exhibit a decrease in spin-spin relaxation time once the temperature reaches the Tcloud. In the presence of silica, the NMR spectra indicate significantly reduced mobility of the EO portion relative to the alkyl and BO segments. Furthermore, our results suggest that the BAE surfactants are not fractionally clouding out or precipitating with a portion of the compositional distribution during the cloud point transition.

Enhancing Compression Level for More Efficient Compressed Sensing and Other Lessons from NMR Spectroscopy.

Modern nuclear magnetic resonance spectroscopy (NMR) is based on two- and higher-dimensional experiments that allow the solving of molecular structures, i.e., determine the relative positions of single atoms very precisely. However, rich chemical information comes at the price of long data acquisition times (up to several days). This problem can be alleviated by compressed sensing (CS)—a method that revolutionized many fields of technology. It is known that CS performs the most efficiently when measured objects feature a high level of compressibility, which in the case of NMR signal means that its frequency domain representation (spectrum) has a low number of significant points. However, many NMR spectroscopists are not aware of the fact that various well-known signal acquisition procedures enhance compressibility and thus should be used prior to CS reconstruction. In this study, we discuss such procedures and show to what extent they are complementary to CS approaches. We believe that the survey will be useful not only for NMR spectroscopists but also to inspire the broader signal processing community.

Probing the Surface Structure of Semiconductor Nanoparticles by DNP SENS with Dielectric Support Materials.

Surface characterization is crucial for understanding how the atomic-level structure affects the chemical and photophysical properties of semiconducting nanoparticles (NPs). Solid-state nuclear magnetic resonance spectroscopy (NMR) is potentially a powerful technique for the characterization of the surface of NPs, but it is hindered by poor sensitivity. Dynamic nuclear polarization surface enhanced NMR spectroscopy (DNP SENS) has previously been demonstrated to enhance the sensitivity of surface-selective solid-state NMR experiments by 1-2 orders of magnitude. Established sample preparations for DNP SENS experiments on NPs require the dilution of the NPs on mesoporous silica. Using hexagonal boron nitride (h-BN) to disperse the NPs doubles DNP enhancements and absolute sensitivity in comparison to standard protocols with mesoporous silica. Alternatively, precipitating the NPs as powders, mixing them with h-BN, and then impregnating the powdered mixture with radical solution leads to further 4-fold sensitivity enhancements by increasing the concentration of NPs in the final sample. This modified procedure provides a factor of 9 improvement in NMR sensitivity in comparison to previously established DNP SENS procedures, enabling challenging homonuclear and heteronuclear 2D NMR experiments on CdS, Si, and Cd3P2 NPs. These experiments allow NMR signals from the surface, subsurface, and core sites to be observed and assigned. For example, we demonstrate the acquisition of DNP-enhanced 2D 113Cd-113Cd correlation NMR experiments on CdS NPs and natural isotropic abundance 2D 13C-29Si HETCOR of functionalized Si NPs. These experiments provide a critical understanding of NP surface structures.

Nonstationary Two-Dimensional Nuclear Magnetic Resonance: A Method for Studying Reaction Mechanisms in Situ.

Nuclear magnetic resonance spectroscopy (NMR) is a versatile tool of chemical analysis allowing one to determine structures of molecules with atomic resolution. Particularly informative are two-dimensional (2D) experiments that directly identify atoms coupled by chemical bonds or a through-space interaction. Thus, NMR could potentially be powerful tool to study reactions in situ and explain their mechanisms. Unfortunately, 2D NMR is very time-consuming and thus often cannot serve as a "snapshot" technique for in situ reaction monitoring. Particularly difficult is the case of spectra, in which resonance frequencies vary in the course of reaction. This leads to resolution and sensitivity loss, often hindering the detection of transient products. In this paper we introduce a novel approach to correct such nonstationary 2D NMR signals and raise the detection limits over 10 times. We demonstrate success of its application for studying the mechanism of the reaction of AgSO4-induced synthesis of diphenylmethane-type compounds. Several reactions occur in the studied mixture of benzene and toluene, all with rather low yield and leading to compounds with similar chemical shifts. Nevertheless, with the use of a proposed 2D NMR approach we were able to describe complex mechanisms of diphenylmethane formation involving AgSO4-induced toluene deprotonation and formation of benzyl carbocation, followed by nucleophilic attacks.

Soil Organic Matter Composition in Coastal and Continental Date Palm Systems: Insights from Tunisian Oases

In Tunisia, the coastal Chenini oasis is characterized by a lush vegetation cover, whereas more inland continental oases (e.g., the Guettaya oasis) have a very scarce vegetation cover. For sustaining date palm production in these areas, organic fertilizers are applied, either spread on the soil surface (in Chenini) or buried under a sand layer (in Guettaya). We examined at a molecular level how these management techniques affect soil organic matter composition in oasis systems. A dominance of fresh plant input for Guettaya was indicated by solid-state 13C nuclear magnetic resonance spectroscopy signals, which was most pronounced in the uppermost soil close to palms. Evidence for more degraded organic matter was found in deeper soil near the palms, as well as in the soil distant from the palms. Amino sugar contents were low in the uppermost Guettaya soil near the palms. The overall microbial amino sugar residue contents were similar in range as those found in other dryland environments. With increasing distance from trees, the amino sugar contents declined in Guettaya, where the palms grow on bare soil, but this was not the case for Chenini, which has multi-layer vegetation cover under palms. In agreement with the results from previous dryland studies, the soil microbial community in both oasis systems was dominated by fungi in topsoil, and a shift toward bacteria-derived residues in subsurface soil. This might be due to higher variability of temperature and moisture in topsoil and/or lower degradability of fungal remains; however, further research is required to confirm this hypothesis.

Biomarker Glycoprotein Acetyls Is Associated With the Risk of a Wide Spectrum of Incident Diseases and Stratifies Mortality Risk in Angiography Patients.

Integration of systems-level biomolecular information with electronic health records has led to recent interest in the glycoprotein acetyls (GlycA) biomarker-a serum- or plasma-derived nuclear magnetic resonance spectroscopy signal that represents the abundance of circulating glycated proteins. GlycA predicts risk of diverse outcomes, including cardiovascular disease, type 2 diabetes mellitus, and all-cause mortality; however, the underlying detailed associations of GlycA's morbidity and mortality risk are currently unknown. We used 2 population-based cohorts totaling 11 861 adults from the Finnish general population to test for an association with 468 common incident hospitalization and mortality outcomes during an 8-year follow-up. Further, we utilized 900 angiography patients to test for GlycA association with mortality risk and potential utility for mortality risk discrimination during 12-year follow-up. New associations with GlycA and incident alcoholic liver disease, chronic renal failure, glomerular diseases, chronic obstructive pulmonary disease, inflammatory polyarthropathies, and hypertension were uncovered, and known incident disease associations were replicated. GlycA associations for incident disease outcomes were in general not attenuated when adjusting for hsCRP (high-sensitivity C-reactive protein). Among 900 patients referred to angiography, GlycA had hazard ratios of 4.87 (95% CI, 2.45-9.65) and 5.00 (95% CI, 2.38-10.48) for 12-year risk of mortality in the fourth and fifth quintiles by GlycA levels, demonstrating its prognostic potential for identification of high-risk individuals. When modeled together, both hsCRP and GlycA were attenuated but remained significant. GlycA was predictive of myriad incident diseases across many major internal organs and stratified mortality risk in angiography patients. Both GlycA and hsCRP had shared and independent contributions to mortality risk, suggesting chronic inflammation as an etiological factor. GlycA may be useful in improving risk prediction in specific disease settings.

Diffusion-Ordered Nuclear Magnetic Resonance Spectroscopy (DOSY-NMR): A Novel Tool for Identification of Phosphorus Compounds in Soil Extracts.

Liquid-state, one-dimension 31P nuclear magnetic resonance spectroscopy (NMR) has greatly advanced our understanding of the composition of organic phosphorus in the environment. However, the correct assignment of signals is complicated by overlapping and shifting signals in different types of soils. We applied therefore for the first time diffusion-ordered spectroscopy (DOSY) to soil extracts, allowing us to separate phosphorus components in the second domain based on their translational diffusion coefficients. After successful application to a mixture of 14 model compounds, diffusion rates correlated closely with the molecular weight of the individual compound in aqueous solution (R2 = 0.97). The method was then applied to NaOH/EDTA extracts of a grassland soil, of which paramagnetic contaminations were removed with sodium sulfide following high-velocity centrifugation (21 500g, 45 min) at 4 °C. Diffusion rates in soil extracts were again closely related to molecular weight (R2 = 0.98), varying from 163.9 to 923.8 Da. However, our DOSY application failed for a forest soil with low organic phosphorus content. Overall, DOSY did help to clearly identify specific NMR signals like myo- and scyllo-inositol hexakisphosphate. It thus provides a more confident signal assignment than 1D 31P NMR, although currently the ubiquitous use of this novel methodology is still limited to soils with high organic phosphorus content.

In Situ NMR Tracks Real-Time Li Ion Movement in Hybrid Supercapacitor–Battery Device

Lithium ion capacitors (LICs) are emerging as promising energy storage devices; thus, understanding their electrochemistry is of great interest. Here we report the study of a novel LIC by employing in situ nuclear magnetic resonance spectroscopy (NMR) as a nondestructive tool, revealing the sequence of electrochemical processes in it. We have performed in situ 7Li NMR experiments on the LIC by simultaneously cycling the LIC pouch cell in the voltage range 2.0–4.0 V. NMR spectra recorded for multiple cycles reveal the 7Li NMR signals arising from different parts of the capacitor. By employing a combination of in situ 7Li NMR, component isolation, and Gaussian–Lorentzian peak fitting, we investigate the resonances arising from the Li metal from stabilized lithium metal powder (SLMP), free ions in electrolyte, the solid electrolyte interface layer (SEI), intercalated lithium in carbon anode, and the Li ions in the electric double layer on cathode. The recorded in situ 7Li NMR spectra showed that the charge a...

Strategies for optimizing the phase correction algorithms in Nuclear Magnetic Resonance spectroscopy.

Nuclear Magnetic Resonance (NMR) spectroscopy is a popular medical diagnostic technique. NMR is also the favourite tool of chemists/biochemists to elucidate the molecular structure of small or big molecules; it is also a widely used tool in material science, in food science etc. In the case of medical diagnosis it allows for determining a metabolic composition of analysed tissue which may support the identification of tumour cells. Precession signal, that is a crucial part of MR phenomenon, contains distortions that must be filtered out before signal analysis. One of such distortions is phase error.Five popular algorithms: Automics, Shanon's entropy minimization, Ernst's method, Dispa and eDispa are presented and discussed. A novel adaptive tuning algorithm for Automics method was developed and numerically optimal solutions to automatic tuning of the other four algorithms were proposed. To validate the performance of the proposed techniques, two experiments were performed - the first one was done with the use of in silico generated data. For all presented methods, the fine tuning strategies significantly increased the correction accuracy. The highest improvement was observed for Automics algorithm, independently of noise level, with relative phase error dropping by average from 10.25% to 2.40% for low noise level and from 12.45% to 2.66% for high noise level. The second validation experiment, done with the use of phantom data, confirmed the in silico results. The obtained accuracy of the estimation of metabolite concentration was at 99.5%.ConclusionsThe proposed strategies for optimizing the phase correction algorithms significantly improve the accuracy of Nuclear Magnetic Resonance spectroscopy signal analysis.

Monitoring hydration in lime-metakaolin composites using electrochemical impedance spectroscopy and nuclear magnetic resonance spectroscopy

AbstractThis paper describes a study of the hydraulic reactions between metakaolin (MK) and air lime using electrochemical impedance spectroscopy (EIS) and nuclear magnetic resonance spectroscopy (NMR). Tests were carried out at 20, 25 and 30°C on lime-MK pastes with 10:1 w/w ratio. Tests over 28 days allowed identification of relevant changes in the EIS signals and characterization of pastes using thermal analysis (TGA/DSC), scanning electron microscopy (SEM), mercury intrusion porosimetry (MIP) and uni-axial compressive tests. Tests over shorter periods of time (up to 42 h) allowed more detailed studies of the hydraulic phases formed at the very beginning of the reactions.Results of thermal analyses demonstrate formation of hydraulic compounds such as CSH, C4AH13 and C3ASH6 and show their evolution over time. MIP analysis demonstrates changes in pore size distribution related to the formation and trasformation of hydraulic phases. Variations of impedance response with time are shown to be associated with reaction kinetics. Changes in the NMR signal within the first 42 h of reaction are shown to be associated with the dissolution of calcium hydroxide in the pore solution. Overall, this paper demonstrates the importance of NMR in the study of hydraulic reactions in lime based materials and the ability of EIS to detect the formation of hydraulic compounds and the end of the calcium hydroxide dissolution process.

Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma

Nuclear magnetic resonance spectroscopy (NMR) provides robust readouts of many metabolic parameters in one experiment. However, identification of clinically relevant markers in 1H NMR spectra is a major challenge. Association of NMR-derived quantities with genetic variants can uncover biologically relevant metabolic traits. Using NMR data of plasma samples from 1,757 individuals from the KORA study together with 655,658 genetic variants, we show that ratios between NMR intensities at two chemical shift positions can provide informative and robust biomarkers. We report seven loci of genetic association with NMR-derived traits (APOA1, CETP, CPS1, GCKR, FADS1, LIPC, PYROXD2) and characterize these traits biochemically using mass spectrometry. These ratios may now be used in clinical studies.

The lipid composition of isolated cytoplasmic lipid droplets from a human cancer cell line, BE(2)M17

(1)H nuclear magnetic resonance spectroscopy (NMR) resonances from lipids in tumours are associated with tumour grade and treatment response. The origin of these NMR signals is mainly considered to be cytoplasmic lipid droplets (LDs). Techniques exist for isolating LDs but little is known about their composition and its relationship to NMR signals. In this work, density-gradient ultracentrifugation was performed on homogenised human cancer cells to isolate LDs. (1)H NMR was performed on whole cells, isolated LDs and their extracts. Heteronuclear single quantum coherence spectroscopy (HSQC) and liquid chromatography mass spectroscopy (LC-MS) were performed on lipid extracts of LDs. Staining and microscopy were used to characterize isolated LDs. An excellent agreement in chemical shift and relative signal intensity was observed between lipid resonances in cells and isolated LD spectra supporting that NMR-visible lipids originate primarily from LDs. Isolated LDs showed high concentrations of unsaturated lipids, a oleic-to-linoleic acid ratio greater than two and a cholesteryl ester (ChE)-to-cholesterol (Ch) ratio close to unity. These ratios were several-fold greater than respective ratios in whole cells, demonstrating isolation is important to characterize LD composition. LDs contain a specific group of lipid species that are likely to contribute to the (1)H NMR spectrum of cells.

Instantaneous frequency based spectral analysis of nuclear magnetic resonance spectroscopy data

Nuclear magnetic resonance spectroscopy signals are modelled as a sum of decaying complex exponentials in noise. The spectral analysis of these signals allowing for their decomposition and the estimation of the parameters of the components is crucial to the study of biochemical samples. This paper presents a novel Gabor filterbank/notch filtering instantaneous frequency (IF) estimator, that enables the extraction of weaker and shorter lived exponentials. This new approach is an iterative procedure where a Gabor filterbank is first employed to obtain a reliable estimate of the IF of the strongest component present. The estimated strongest component is then notch filtered, which un-masks weaker components, and the procedure repeated. The performance of this method was evaluated using an artificial signal and compared to the short time Fourier transform, reassigned STFT, and the original Gabor filterbank approach. The results clearly demonstrate its superiority in uncovering weaker signals and resolving components that are very close to one another in frequency. Furthermore, the new method is shown to be more robust than the ITCMP technique at low signal to noise ratios.

Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications

The analysis of metabolic processes is becoming increasingly important to our understanding of complex biological systems and disease states. Nuclear magnetic resonance spectroscopy (NMR) is a particularly relevant technology in this respect, since the NMR signals provide a quantitative measure of the metabolite concentrations. However, due to the complexity of the spectra typical of biological samples, the demands of clinical and high-throughput analysis will only be fully met by a system capable of reliable, automatic processing of the spectra. An initial step in this direction has been taken by Targeted Profiling (TP), employing a set of known and predicted metabolite signatures fitted against the signal. However, an accurate fitting procedure for (1)H NMR data is complicated by shift uncertainties in the peak systems caused by measurement imperfections. These uncertainties have a large impact on the accuracy of identification and quantification and currently require compensation by very time consuming manual interactions. Here, we present an approach, termed Extended Targeted Profiling (ETP), that estimates shift uncertainties based on a genetic algorithm (GA) combined with a least squares optimization (LSQO). The estimated shifts are used to correct the known metabolite signatures leading to significantly improved identification and quantification. In this way, use of the automated system significantly reduces the effort normally associated with manual processing and paves the way for reliable, high-throughput analysis of complex NMR spectra. The results indicate that using simultaneous shift uncertainty correction and least squares fitting significantly improves the identification and quantification results for (1)H NMR data in comparison to the standard targeted profiling approach and compares favorably with the results obtained by manual expert analysis. Preservation of the functional structure of the NMR spectra makes this approach more realistic than simple binning strategies.

Hyphenation of Gas Chromatography to Microcoil 1H Nuclear Magnetic Resonance Spectroscopy

Whereas the hyphenation of gas chromatography (GC) with mass spectrometry is of great importance, little is known about the coupling to nuclear magnetic resonance spectroscopy (NMR). The investigation of this technique is an attractive proposition because of the valuable information given by NMR on molecular structure. The experiments shown here are to our knowledge the first hyphenating capillary GC to microcoil NMR. In contrast to liquids, gases have rarely been investigated by NMR, mainly due to the experimental difficulties in handling gases and the low signal-to-noise-ratio (SNR) of the NMR signal obtained at atmospheric pressure. With advances in NMR sensitivity (higher magnetic fields and solenoidal microprobes), this limitation can be largely overcome. In this paper, we describe the use of a custom-built solenoidal NMR microprobe with an active volume of 2 microL for the NMR detection of several compounds at 400 MHz, first in a mixture, and then with full coupling to capillary GC to identify them separately. The injected amounts of each analyte in the hyphenated experiments are in the range of 15-50 micromol, resulting in reasonable SNR for sample masses of 1-2 microg.

Nuclear magnetic resonance spectroscopy studies of 2-(2,4-dinitrobenzyl) pyridine and long-lived photoinduced products

A study of the photo-induced and thermally activated tautomerization of 2-(2,4-dinitrobenzyl) pyridine (-DNBP) by solid state nuclear magnetic resonance spectroscopy (NMR) is presented. Whereas similar high-resolution 1H and 13C spectra were obtained from polycrystalline powders before and after ultraviolet light irradiation, the corresponding spectral data obtained from PVC films doped with -DNBP show significant changes. The -DNBP phototautomer lifetimes in PVC films (2.4 h or 6 d) were determined from the proton NMR signal recovery. Scanning electron microscopy (SEM) and differential scanning calorimetry of the doped films show that, in addition to matrix isolated molecules, -DNBP is incorporated into the polymer matrix in the form of nanocrystalline aggregates. At high guest concentrations, the crystallites can be modified by controlled solvent evaporation, as monitored by NMR and SEM. The variation of the 1H spectrum with temperature shows the presence of at least two stable conformational isomers in the solid samples.