Under-sampling and compressed sensing of 3D spatially-resolved displacement propagators in porous media using APGSTE-RARE MRI.

A method for under-sampling and compressed sensing of 3D spatially-resolved propagators is presented and demonstrated for flow in a packed bed and a heterogeneous carbonate rock. By sampling only 12.5% of q,k-space, the experimental acquisition time was reduced by almost an order of magnitude. In particular, for both systems studied, a 3D image was acquired at 1 mm isotropic spatial resolution such that 134,400 local propagators were obtained. Data were acquired in ~1 h and ~11 h for the packed bed and rock, respectively. It is shown that spatial resolution and under-sampling using this implementation retains the quantitative nature of the propagator measurement, and differences between implementation of this measurement in two and three dimensions are identified. The potential for 3D spatially-resolved propagators to provide new insights into transport processes in porous media by characterisation of the statistical moments of the propagators is discussed

Acquisition of spatially-resolved displacement propagators using compressed sensing APGSTE-RARE MRI.

A method is presented for accelerating the acquisition of spatially-resolved displacement propagators via under-sampling of an Alternating Pulsed Gradient Stimulated Echo - Rapid Acquisition with Relaxation Enhancement (APGSTE-RARE) data acquisition with compressed sensing image reconstruction. The method was demonstrated with respect to the acquisition of 2D spatially-resolved displacement propagators of water flowing through a packed bed of hollow cylinders. The q,k-space was under-sampled according to variable-density pseudo-random sampling patterns. The quality of compressed sensing reconstructions of spatially-resolved propagators at a range of sampling fractions was assessed using the peak signal-to-noise ratio (PSNR) as a quality metric. Propagators of good quality (PSNR 33.2 dB) were reconstructed from only 6.25% of all data points in q,k-space, resulting in a reduction in the data acquisition time from 4 h to 14 min. The spatially-resolved propagators were reconstructed using both the total variation and nuclear norm sparsifying transforms; use of total variation resulted in a slightly higher quality of the reconstructed image in most cases. To illustrate the power of this method to characterise heterogeneous flow in porous media, the method is applied to the characterisation of flow in a vuggy carbonate rock

NMR nanoparticle diffusometry in hydrogels:enhancing sensitivity and selectivity

\u3cp\u3eFrom the diffusional behavior of nanoparticles in heterogeneous hydrogels, quantitative information about submicron structural features of the polymer matrix can be derived. Pulsed-gradient spin?echo NMR is often the method of choice because it measures diffusion of the whole ensemble of nanoparticles. However, in \u3csup\u3e1\u3c/sup\u3eH diffusion-ordered spectroscopy (DOSY), low-intensity nanoparticle signals have to be separated from a highly protonated background. To circumvent this, we prepared \u3csup\u3e19\u3c/sup\u3eF labeled, PEGylated, water-soluble dendritic nanoparticles with a \u3csup\u3e19\u3c/sup\u3eF loading of ∼% to enable background free \u3csup\u3e19\u3c/sup\u3eF DOSY experiments. \u3csup\u3e19\u3c/sup\u3eF nanoparticle diffusometry was benchmarked against \u3csup\u3e1\u3c/sup\u3eH diffusion-T\u3csub\u3e2\u3c/sub\u3e correlation spectroscopy (DRCOSY), which has a stronger signal separation potential than the commonly used \u3csup\u3e1\u3c/sup\u3eH DOSY experiment. We used bootstrap data resampling to estimate confidence intervals and stabilize 2D-Laplace inversion of DRCOSY data with high noise levels and artifacts, allowing quantitative diffusometry even at low magnetic field strengths (30 MHz). The employed methods offer significant advantages in terms of sensitivity and selectivity.\u3c/p\u3

Yielding and flow of cellulose microfibril dispersions in the presence of a charged polymer

The shear flow of microfibrillated cellulose dispersions is still not wholly understood as a consequence of their multi-length-scale heterogeneity. We added carboxymethyl cellulose, a charged polymer, that makes cellulose microfibril dispersions more homogeneous at the submicron and macro scales. We then compared the yielding and flow behavior of these dispersions to that of typical thixotropic yield-stress fluids. Despite the apparent homogeneity of the dispersions, their flow velocity profiles in cone-plate geometry, as measured by rheo-MRI velocimetry, differ strongly from those observed for typical thixotropic model systems: the viscosity across the gap is not uniform, despite a flat stress field across the gap. We describe these velocity profiles with a nonlocal model, and attribute the non-locality to persistent micron-scale structural heterogeneity.</p

Scaling behavior of dendritic nanoparticle mobility in semidilute polymer solutions

In our studies on particle mobility in polymer solutions, we have investigated and determined self-diffusion coefficients of nanoparticles in semidilute solutions of poly(ethylene glycol) (PEG, Mw = 6, 20, 35, and 100 kDa). Specially designed PEGylated dendrimers with well-defined sizes (dh = 3.4-11.0 nm) and with internal 19F moieties allow for background-free 19F NMR diffusion measurements. This way, we were able to assess the self-diffusion coefficients as a function of particle diameter and length scales (correlation length, tube diameter, polymer radius of gyration) with high resolution. Scaling arguments allowed us to visualize a clear crossover between particles probing a lower apparent viscosity to near macroviscosity when the nanoparticle size is comparable to the PEG polymer coil size. The same arguments are shown to correctly predict particle diffusion coefficients as a function of polymer concentration when the particles are smaller than the polymer coils

Scaling behavior of dendritic nanoparticle mobility in semidilute polymer solutions

\u3cp\u3eIn our studies on particle mobility in polymer solutions, we have investigated and determined self-diffusion coefficients of nanoparticles in semidilute solutions of poly(ethylene glycol) (PEG, M\u3csub\u3ew\u3c/sub\u3e = 6, 20, 35, and 100 kDa). Specially designed PEGylated dendrimers with well-defined sizes (d\u3csub\u3eh\u3c/sub\u3e = 3.4-11.0 nm) and with internal \u3csup\u3e19\u3c/sup\u3eF moieties allow for background-free \u3csup\u3e19\u3c/sup\u3eF NMR diffusion measurements. This way, we were able to assess the self-diffusion coefficients as a function of particle diameter and length scales (correlation length, tube diameter, polymer radius of gyration) with high resolution. Scaling arguments allowed us to visualize a clear crossover between particles probing a lower apparent viscosity to near macroviscosity when the nanoparticle size is comparable to the PEG polymer coil size. The same arguments are shown to correctly predict particle diffusion coefficients as a function of polymer concentration when the particles are smaller than the polymer coils.\u3c/p\u3

A combined rheology and time domain NMR approach for determining water distributions in protein blends

We present a combined time domain NMR and rheology approach to quantify the water distribution in a phase separated protein blend. The approach forms the basis for a new tool to assess the microstructural properties of phase separated biopolymer blends, making it highly relevant for many food and non-food related applications. First, we determine the relaxation rate of absorbed water, and the viscoelastic properties of the separated phases as function of the water content. Next, the same properties are measured for the protein blends. Finally, predictions for water distribution obtained from rheological experiments are made via the polymer blending law, and compared to a more direct assessment of the water distribution with time-domain NMR relaxometry (TD-NMR). In this study, the protein blend consists of soy protein isolate (SPI) and vital wheat gluten (WG). We demonstrate that predictions for water distribution are similar for both TD-NMR and rheological measurements. It turns out that water does not distribute homogenously over the phases. Independent of the SPI and WG ratio, more water is absorbed by the SPI phase relative to the WG phase, which largely determines the resulting rheological properties of the blends

Temporal evolution and predictors of subjective cognitive complaints up to 4 years after stroke

OBJECTIVE: To examine the temporal evolution of subjective cognitive complaints in the long-term after stroke, and to identify predictors of long-term subjective cognitive complaints. METHODS: Prospective cohort study including 395 stroke patients. Subjective cognitive complaints were assessed at 2 months, 6 months and 4 years post-stroke, using the Checklist for Cognitive and Emotional consequences following stroke (CLCE-24). The temporal evolution of subjective cognitive complaints was described using multilevel growth modelling. Associations between CLCE-24 cognition score at 4 years post-stroke and baseline characteristics, depression, anxiety, cognitive test performance, and adaptive and maladaptive psychological factors were examined. Significant predictors were entered in a multivariate multilevel model. RESULTS: A significant increase in subjective cognitive complaints from 2 months up to 4 years (mean 3.7 years, standard deviation (SD) 0.6 years) post-stroke was observed (p≤0.001). Two months post-stroke, 76% of patients reported at least one cognitive complaint, 72% at 6 months, and 89% at 4 years post-stroke. A higher level of subjective cognitive complaints at 2 months and lower scores on adaptive and maladaptive psychological factors were significant independent predictors of a higher level of subjective cognitive complaints at 4 years post-stroke. CONCLUSION: Post-stroke subjective cognitive complaints increase over time and can be predicted by the extent of subjective cognitive complaints and the presence of adaptive and maladaptive psychological factors in the early phases after stroke

Complex coacervate core micelles with spectroscopic labels for diffusometric probing of biopolymer networks

\u3cp\u3eWe present the design, preparation, and characterization of two types of complex coacervate core micelles (C3Ms) with cross-linked cores and spectroscopic labels and demonstrate their use as diffusional probes to investigate the microstructure of percolating biopolymer networks. The first type consists of poly(allylamine hydrochloride) (PAH) and poly(ethylene oxide)-poly(methacrylic acid) (PEO-b-PMAA), labeled with ATTO 488 fluorescent dyes. We show that the size of these probes can be tuned by choosing the length of the PEO-PMAA chains. ATTO 488-labeled PEO\u3csub\u3e113\u3c/sub\u3e-PMAA\u3csub\u3e15\u3c/sub\u3e micelles are very bright with 18 dye molecules incorporated into their cores. The second type is a \u3csup\u3e19\u3c/sup\u3eF-labeled micelle, for which we used PAH and a \u3csup\u3e19\u3c/sup\u3eF-labeled diblock copolymer tailor-made from poly(ethylene oxide)-poly(acrylic acid) (mPEO\u3csub\u3e79\u3c/sub\u3e-b-PAA\u3csub\u3e14\u3c/sub\u3e). These micelles contain approximately 4 wt % of \u3csup\u3e19\u3c/sup\u3eF and can be detected by \u3csup\u3e19\u3c/sup\u3eF NMR. The \u3csup\u3e19\u3c/sup\u3eF labels are placed at the end of a small spacer to allow for the necessary rotational mobility. We used these ATTO- and \u3csup\u3e19\u3c/sup\u3eF-labeled micelles to probe the microstructures of a transient gel (xanthan gum) and a cross-linked, heterogeneous gel (κ-carrageenan). For the transient gel, sensitive optical diffusometry methods, including fluorescence correlation spectroscopy, fluorescence recovery after photobleaching, and super-resolution single nanoparticle tracking, allowed us to measure the diffusion coefficient in networks with increasing density. From these measurements, we determined the diameters of the constituent xanthan fibers. In the heterogeneous κ-carrageenan gels, bimodal nanoparticle diffusion was observed, which is a signpost of microstructural heterogeneity of the network.\u3c/p\u3