Solid–Liquid Phase Transitions of Triglycerides in Griebenschmalz, Smalec, and Fedt Studied Using 13C Solid-State NMR with Dynamics-Based Spectral Filtering

The consumer satisfaction of lard-based bread spreads depends on a delicate balance between a liquid fat phase, allowing the spread to flow, and solid fat crystals, providing the product with substance sometimes further enhanced by crispy pork cracklings. Here we apply 13C solid-state NMR with dynamics-based spectral filtering to characterize and follow the temperature dependence of the co-existing solid and liquid triglyceride phases in commercial German Griebenschmalz and Polish smalec, both containing cracklings, as well as home-made Danish fedt and, as a chemically more pure reference, German Schweineschmalz intended for baking. The NMR method allows detection of carbon atoms representative of saturated, unsaturated, and polyunsaturated acyl chains in both solid and liquid states. The results show that the solid comprises multiple crystal forms with different melting temperatures, while the liquid is at low temperature enriched in triglycerides with shorter acyl chains and higher degree of unsaturation, which become diluted with long-chain saturated triglycerides as the solids are melting. The obtained deeper understanding of the concomitant aspects of the phase transitions may pave the way for future efforts of rational optimization of fat blend composition to extend the temperature range over which the product contains sufficient amounts of both solids and liquids to give texture properties appealing to consumers

Multi-Scale Characterization of Lyotropic Liquid Crystals Using 2H and Diffusion MRI with Spatial Resolution in Three Dimensions

The ability of lyotropic liquid crystals to form intricate structures on a range of length scales can be utilized for the synthesis of structurally complex inorganic materials, as well as in devices for controlled drug delivery. Here we employ magnetic resonance imaging (MRI) for non-invasive characterization of nano-, micro-, and millimeter scale structures in liquid crystals. The structure is mirrored in the translational and rotational motion of the water, which we assess by measuring spatially resolved self-diffusion tensors and spectra. Our approach differs from previous works in that the MRI parameters are mapped with spatial resolution in all three dimensions, thus allowing for detailed studies of liquid crystals with complex millimeter-scale morphologies that are stable on the measurement time-scale of 10 hours. The data conveys information on the nanometer-scale structure of the liquid crystalline phase, while the combination of diffusion and data permits an estimate of the orientational distribution of micrometer-scale anisotropic domains. We study lamellar phases consisting of the nonionic surfactant C10E3 in O, and follow their structural equilibration after a temperature jump and the cessation of shear. Our experimental approach may be useful for detailed characterization of liquid crystalline materials with structures on multiple length scales, as well as for studying the mechanisms of phase transitions

Segmental order parameters in a nonionic surfactant lamellar phase studied with H-1-C-13 solid-state NMR

A lyotropic nonionic lamellar system composed of pentaethyleneglycol mono n-dodecyl ether and D2O was studied using natural abundance C-13 NMR under magic-angle spinning. Applying a two-dimensional recoupling method proposed by Dvinskikh (R-PDLF), H-1-C-13 dipolar couplings were estimated over a range of temperatures (300-335 K), thus enabling analysis of structural changes in the liquid crystalline system. The results obtained are used to correlate the conformation and mobility of local sites in the surfactant molecule with overall changes in the lamellar structure

Towards non-parametric fiber-specific T1T_1 relaxometry in the human brain

Purpose: To estimate fiber-specific $T_1$ values, i.e. proxies for myelin content, in heterogeneous brain tissue. Methods: A diffusion-$T_1$ correlation experiment was carried out on an in vivo human brain using tensor-valued diffusion encoding and multiple repetition times. The acquired data was inverted using a Monte-Carlo inversion algorithm that retrieves non-parametric distributions $\mathcal{P}(\mathbf{D},R_1)$ of diffusion tensors and longitudinal relaxation rates $R_1 = 1/T_1$. Orientation distribution functions (ODFs) of the highly anisotropic components of $\mathcal{P}(\mathbf{D},R_1)$ were defined to visualize orientation-specific diffusion-relaxation properties. Finally, Monte-Carlo density-peak clustering (MC-DPC) was performed to quantify fiber-specific features and investigate microstructural differences between white-matter fiber bundles. Results: Parameter maps corresponding to $\mathcal{P}(\mathbf{D},R_1)$'s statistical descriptors were obtained, exhibiting the expected $R_1$ contrast between brain-tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated possible differences in $T_1$ relaxation between major fiber bundles. These differences, confirmed by MC-DPC, were in qualitative agreement with previous model-based works but seem biased by the limitations of our current experimental setup. Conclusions: Our Monte-Carlo framework enables the non-parametric estimation of fiber-specific diffusion-$T_1$ features, thereby showing potential for characterizing developmental or pathological changes in $T_1$ within a given fiber bundle, and for investigating inter-bundle $T_1$ differences.Comment: 11 pages, 6 figures, submitted to Magnetic Resonance in Medicine (MRM) on the 14th of June 202

Optimal Experimental Design for Biophysical Modelling in Multidimensional Diffusion MRI

Computational models of biophysical tissue properties have been widely used in diffusion MRI (dMRI) research to elucidate the link between microstructural properties and MR signal formation. For brain tissue, the research community has developed the so-called Standard Model (SM) that has been widely used. However, in clinically applicable acquisition protocols, the inverse problem that recovers the SM parameters from a set of MR diffusion measurements using pairs of short pulsed field gradients was shown to be ill-posed. Multidimensional dMRI was shown to solve this problem by combining linear and planar tensor encoding data. Given sufficient measurements, multiple choices of b-tensor sets provide enough information to estimate all SM parameters. However, in the presence of noise, some sets will provide better results. In this work, we develop a framework for optimal experimental design of multidimensional dMRI sequences applicable to the SM. This framework is based on maximising the determinant of the Fisher information matrix, which is averaged over the full SM parameter space. This averaging provides a fairly objective information metric tailored for the expected signal but that only depends on the acquisition configuration. The optimisation of this metric can be further restricted to any subclass of desirable design constraints like, for instance, hardware-specific constraints. In this work, we compute the optimal acquisitions over the set of all b-tensors with fixed eigenvectors

Molecular velocity auto-correlation of simple liquids observed by NMR MGSE method

The velocity auto-correlation spectra of simple liquids obtained by the NMR method of modulated gradient spin echo show features in the low frequency range up to a few kHz, which can be explained reasonably well by a $t^{-3/2}$ long time tail decay only for non-polar liquid toluene, while the spectra of polar liquids, such as ethanol, water and glycerol, are more congruent with the model of diffusion of particles temporarily trapped in potential wells created by their neighbors. As the method provides the spectrum averaged over ensemble of particle trajectories, the initial non-exponential decay of spin echoes is attributed to a spatial heterogeneity of molecular motion in a bulk of liquid, reflected in distribution of the echo decays for short trajectories. While at longer time intervals, and thus with longer trajectories, heterogeneity is averaged out, giving rise to a spectrum which is explained as a combination of molecular self-diffusion and eddy diffusion within the vortexes of hydrodynamic fluctuations.Comment: 8 pages, 6 figur

Water-repellent cellulose fiber networks with multifunctional properties.

We demonstrate a simple but highly efficient technique to introduce multifunctional properties to cellulose fiber networks by wetting them with ethyl-cyanoacrylate monomer solutions containing various suspended organic submicrometer particles or inorganic nanoparticles. Solutions can be applied on cellulosic surfaces by simple solution casting techniques or by dip coating, both being suitable for large area applications. Immediately after solvent evaporation, ethyl-cyanoacrylate starts cross-linking around cellulose fibers under ambient conditions because of naturally occurring surface hydroxyl groups and adsorbed moisture, encapsulating them with a hydrophobic polymer shell. Furthermore, by dispersing various functional particles in the monomer solutions, hydrophobic ethyl-cyanoacrylate nanocomposites with desired functionalities can be formed around the cellulose fibers. To exhibit the versatility of the method, cellulose sheets were functionalized with different ethyl-cyanoacrylate nanocomposite shells..

Translational Motion of Water in Biological Tissues-A Brief Primer

As an introduction to the rigorous treatment of diffusion encoding in the rest of the book, this chapter gives simple non-mathematical explanations of the relations between the properties of biological tissues and the translational motion of the tissue water with the aim of conveying an intuitive feel of the relevant time and length scales and the level of detail of the information that can be obtained. The influence of biomembranes and macromolecules on water dynamics is reviewed from both molecular and cellular scale perspectives. The potentially complex motion patterns are decomposed into short-time diffusivity, restriction, anisotropy, flow, and exchange, which can be independently assessed and correlated with advanced diffusion encoding methods in MRI