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  • 1.
    Bradley, Siobhan J.
    et al.
    Victoria University of Wellington, New Zealand.
    Kroon, Renee
    Chalmers University of Technology, Sweden.
    Laufersky, Geoffry
    Victoria University of Wellington, New Zealand.
    Röding, Magnus
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
    Goreham, Renee V.
    Victoria University of Wellington, New Zealand.
    Gschneidtner, Tina
    Chalmers University of Technology, Sweden.
    Schroeder, Kathryn
    Victoria University of Wellington, New Zealand.
    Moth-Poulsen, Kasper
    Chalmers University of Technology, Sweden.
    Andersson, Mats
    Chalmers University of Technology, Sweden ; University of South Australia, Australia.
    Nann, Thomas
    Victoria University of Wellington, New Zealand.
    Heterogeneity in the fluorescence of graphene and graphene oxide quantum dots2017In: Microchimica Acta, ISSN 0026-3672, E-ISSN 1436-5073, Vol. 184, no 3, p. 871-878Article in journal (Refereed)
    Abstract [en]

    Heterogeneity is an inherent property of a wealth of real-world nanomaterials and yet rarely in the reporting of new properties is its effect sufficiently addressed. Graphene quantum dots (GQDs) – fluorescent, nanoscale fragments of graphene - are an extreme example of a heterogeneous nanomaterial. Here, top-down approaches – by far the most predominant – produce batches of particles with a distribution of sizes, shapes, extent of oxidation, chemical impurities and more. This makes characterization of these materials using bulk techniques particularly complex and comparisons of properties across different synthetic methods uninformative. In particular, it hinders the understanding of the structural origin of their fluorescence properties. We present a simple synthetic method, which produces graphene quantum dots with very low oxygen content that can be suspended in organic solvents, suggesting a very pristine material. We use this material to illustrate the limitations of interpreting complex data sets generated by heterogeneous materials and we highlight how misleading this “pristine” interpretation is by comparison with graphene oxide quantum dots synthesized using an established protocol. In addition, we report on the solvatochromic properties of these particles, discuss common characterization techniques and their limitations in attributing properties to heterogeneous materials.

  • 2.
    Longfils, M.
    et al.
    Chalmers University of Technology, Sweden; University of Gothenburg, Sweden.
    Röding, Magnus
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
    Altskar, Annika
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
    Schuster, Erich
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
    Loren, Niklas
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
    Sarkka, A.
    Chalmers University of Technology, Sweden; University of Gothenburg, Sweden.
    Rudemo, M.
    Chalmers University of Technology, Sweden; University of Gothenburg, Sweden.
    Single particle raster image analysis of diffusion for particle mixtures2018In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 269, no 3, p. 269-281Article in journal (Refereed)
    Abstract [en]

    Recently we complemented the raster image correlation spectroscopy (RICS) method of analysing raster images via estimation of the image correlation function with the method single particle raster image analysis (SPRIA). In SPRIA, individual particles are identified and the diffusion coefficient of each particle is estimated by a maximum likelihood method. In this paper, we extend the SPRIA method to analyse mixtures of particles with a finite set of diffusion coefficients in a homogeneous medium. In examples with simulated and experimental data with two and three different diffusion coefficients, we show that SPRIA gives accurate estimates of the diffusion coefficients and their proportions. A simple technique for finding the number of different diffusion coefficients is also suggested. Further, we study the use of RICS for mixtures with two different diffusion coefficents and investigate, by plotting level curves of the correlation function, how large the quotient between diffusion coefficients needs to be in order to allow discrimination between models with one and two diffusion coefficients. We also describe a minor correction (compared to published papers) of the RICS autocorrelation function. Lay description Diffusion is a key mass transport mechanism for small particles. Efficient methods for estimating diffusion coefficients are crucial for analysis of microstructures, for example in soft biomaterials. The sample of interest may consist of a mixture of particles with different diffusion coefficients. Here, we extend a method called Single Particle Raster Image Analysis (SPRIA) to account for particle mixtures and estimation of the diffusion coefficients of the mixture components. SPRIA combines elements of classical single particle tracking methods with utilizing the raster scan with which images obtained by using a confocal laser scanning microscope. In particular, single particles are identified and their motion estimated by following their center of mass. Thus, an estimate of the diffusion coefficient will be obtained for each particle. Then, we analyse the distribution of the estimated diffusion coefficients of the population of particles, which allows us to extract information about the diffusion coefficients of the underlying components in the mixture. On both simulated and experimental data with mixtures consisting of two and three components with different diffusion coefficients, SPRIA provides accurate estimates and, with a simple criterion, the correct number of mixture components is selected in most cases.

  • 3.
    Röding, Magnus
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
    Effective diffusivity in lattices of impermeable superballs2018In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, ISSN 1063-651X, E-ISSN 1095-3787, Vol. 98, no 5, article id 052908Article in journal (Refereed)
    Abstract [en]

    Granular materials constitute a broad class of two-phase media with discrete, solid par-ticles i.e. granules surrounded by a continuous void phase. They have properties that arekey for e.g. separation and chromatography columns, cathode materials for batteries, andpharmaceutical coatings for controlled release. Controlling mass transport properties suchas effective diffusivity is crucial for these applications and the subject of targeted designand optimization. The prototypical granule is a sphere, but current manufacturingtechniques allow for more complicated shapes to be produced in a highly controlled manner,including ellipsoids, cubes, and cubes with rounded edges and corners. The impactof shape for self-assembly, phase transitions, crystallization, and random close packing hasalso been studied for these shapes

  • 4.
    Röding, Magnus
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
    Shape-dependent effective diffusivity in packings of hard cubes and cuboids compared with spheres and ellipsoids2017In: Soft Matter, ISSN 1744-683X, E-ISSN 1744-6848, Vol. 13, no 46, p. 8864-8870Article in journal (Refereed)
    Abstract [en]

    We performed computational screening of effective diffusivity in different configurations of cubes and cuboids compared with spheres and ellipsoids. In total, more than 1500 structures are generated and screened for effective diffusivity. We studied simple cubic and face-centered cubic lattices of spheres and cubes, random configurations of cubes and spheres as a function of volume fraction and polydispersity, and finally random configurations of ellipsoids and cuboids as a function of shape. We found some interesting shape-dependent differences in behavior, elucidating the impact of shape on the targeted design of granular materials.

  • 5.
    Röding, Magnus
    et al.
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
    Billeter, M.
    Chalmers University of Technology, Sweden.
    Massively parallel approximate Bayesian computation for estimating nanoparticle diffusion coefficients, sizes and concentrations using confocal laser scanning microscopy2018In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 271, no 2, p. 174-182Article in journal (Refereed)
    Abstract [en]

    We implement a massively parallel population Monte Carlo approximate Bayesian computation (PMC-ABC) method for estimating diffusion coefficients, sizes and concentrations of diffusing nanoparticles in liquid suspension using confocal laser scanning microscopy and particle tracking. The method is based on the joint probability distribution of diffusion coefficients and the time spent by a particle inside a detection region where particles are tracked. We present freely available central processing unit (CPU) and graphics processing unit (GPU) versions of the analysis software, and we apply the method to characterize mono- and bidisperse samples of fluorescent polystyrene beads.

  • 6.
    Röding, Magnus
    et al.
    RISE, SP – Sveriges Tekniska Forskningsinstitut, SP Food and Bioscience. University of South Australia, Australia; University College London, Australia.
    Bradley, Siobhan J.
    University of South Australia, Australia; Victoria University of Wellington, New Zeeland.
    Williamson, Nathan H.
    University of South Australia, Australia.
    Dewi, Melissa R.
    University of South Australia, Australia.
    Nann, Thomas
    University of South Australia, Australia; Victoria University of Wellington, New Zeeland.
    Nydén, Magnus
    University College London, Australia.
    The power of heterogeneity: Parameter relationships from distributions2016In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 5, article id e0155718Article in journal (Refereed)
    Abstract [en]

    Complex scientific data is becoming the norm, many disciplines are growing immensely data-rich, and higher-dimensional measurements are performed to resolve complex relationships between parameters. Inherently multi-dimensional measurements can directly provide information on both the distributions of individual parameters and the relationships between them, such as in nuclear magnetic resonance and optical spectroscopy. However, when data originates from different measurements and comes in different forms, resolving parameter relationships is a matter of data analysis rather than experiment. We present a method for resolving relationships between parameters that are distributed individually and also correlated. In two case studies, we model the relationships between diameter and luminescence properties of quantum dots and the relationship between molecular weight and diffusion coefficient for polymers. Although it is expected that resolving complicated correlated relationships require inherently multi-dimensional measurements, our method constitutes a useful contribution to the modelling of quantitative relationships between correlated parameters and measurements. We emphasise the general applicability of the method in fields where heterogeneity and complex distributions of parameters are obstacles to scientific insight.

  • 7.
    Röding, Magnus
    et al.
    RISE, SP – Sveriges Tekniska Forskningsinstitut, SP Food and Bioscience, Structure Design. University of South Australia, Australia.
    Del Castillo, L. A.
    University of South Australia, Australia.
    Nydén, M.
    University College London, Australia.
    Follink, B.
    University of South Australia, Australia; Monash University, Australia.
    Microstructure of a granular amorphous silica ceramic synthesized by spark plasma sintering2016In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 264, no 3, p. 298-303Article in journal (Refereed)
    Abstract [en]

    We study the microstructure of a granular amorphous silica ceramic material synthesized by spark plasma sintering. Using monodisperse spherical silica particles as precursor, spark plasma sintering yields a dense granular material with distinct granule boundaries. We use selective etching to obtain nanoscopic pores along the granule borders. We interrogate this highly interesting material structure by combining scanning electron microscopy, X-ray computed nanotomography and simulations based on random close packed spherical particles. We determine the degree of anisotropy caused by the uni-axial force applied during sintering, and our analysis shows that our synthesis method provides a means to avoid significant granule growth and to fabricate a material with well-controlled microstructure.

  • 8.
    Röding, Magnus
    et al.
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
    Gaska, Karolina
    Chalmers University of Technology, Sweden .
    Kádár, Roland
    Chalmers University of Technology, Sweden.
    Loren, Niklas
    Chalmers University of Technology, Sweden.
    Computational Screening of Diffusive Transport in Nanoplatelet-Filled Composites: Use of Graphene To Enhance Polymer Barrier Properties2017In: ACS Applied Nano Materials, ISSN 2574-0970Article in journal (Refereed)
    Abstract [en]

    Motivated by the substantial interest in various fillers to enhance the barrier properties of polymeric films, especially graphene derivatives, we perform a computational screening of obstructed diffusion to explore the design parameter space of nanoplatelet-filled composites synthesized in silico. As a model for the nanoplatelets, we use circular and elliptical nonoverlapping and impermeable flat disks, and diffusion is stochastically simulated using a random-walk model, from which the effective diffusivity is calculated. On the basis of ∼1000 generated structures and diffusion simulations, we systematically investigate the impact of different nanoplatelet characteristics such as orientation, layering, size, polydispersity, shape, and amount. We conclude that the orientation, size, and amount of nanoplatelets are the most important parameters and show that using nanoplatelets oriented perpendicular to the diffusion direction, under reasonable assumptions, with approximately 0.2% (w/w) graphene, we can reach 90% reduction and, with approximately 1% (w/w) graphene, we can reach 99% reduction in diffusivity, purely because of geometrical effects, in a defect-free matrix with perfect compatibility. Additionally, our results suggest that the existing analytical models have some difficulty with extremely large aspect ratio (extremely flat) nanoplatelets, which calls for further development.

  • 9.
    Röding, Magnus
    et al.
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
    Lacroix, Leander
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
    Krona, Annika
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
    Gebäck, Tobias
    Chalmers University of Technology, Sweden.
    Loren, Niklas
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience. Chalmers University of Technology, Sweden.
    A Highly Accurate Pixel-Based FRAP Model Based on Spectral-Domain Numerical Methods2019In: Biophysical Journal, ISSN 0006-3495, E-ISSN 1542-0086, Vol. 116, no 7, p. 1348-1361Article in journal (Refereed)
    Abstract [en]

    We introduce a new, to our knowledge, numerical model based on spectral methods for analysis of fluorescence recovery after photobleaching data. The model covers pure diffusion and diffusion and binding (reaction-diffusion) with immobile binding sites, as well as arbitrary bleach region shapes. Fitting of the model is supported using both conventional recovery-curve-based estimation and pixel-based estimation, in which all individual pixels in the data are utilized. The model explicitly accounts for multiple bleach frames, diffusion (and binding) during bleaching, and bleaching during imaging. To our knowledge, no other fluorescence recovery after photobleaching framework incorporates all these model features and estimation methods. We thoroughly validate the model by comparison to stochastic simulations of particle dynamics and find it to be highly accurate. We perform simulation studies to compare recovery-curve-based estimation and pixel-based estimation in realistic settings and show that pixel-based estimation is the better method for parameter estimation as well as for distinguishing pure diffusion from diffusion and binding. We show that accounting for multiple bleach frames is important and that the effect of neglecting this is qualitatively different for the two estimation methods. We perform a simple experimental validation showing that pixel-based estimation provides better agreement with literature values than recovery-curve-based estimation and that accounting for multiple bleach frames improves the result. Further, the software developed in this work is freely available online.

  • 10.
    Röding, Magnus
    et al.
    RISE, SP – Sveriges Tekniska Forskningsinstitut, SP Food and Bioscience, Structure Design. Chalmers University of Technology, Sweden; University College London, Australia.
    Schuster, Erich
    RISE, SP – Sveriges Tekniska Forskningsinstitut, SP Food and Bioscience, Structure Design. Chalmers University of Technology, Sweden.
    Logg, Katarina
    RISE, SP – Sveriges Tekniska Forskningsinstitut, SP Food and Bioscience, Structure Design. Chalmers University of Technology, Sweden.
    Lundman, Malin
    Chalmers University of Technology, Sweden; SCA Hygiene Products, Sweden.
    Bergström, Per
    Chalmers University of Technology, Sweden; SCA Hygiene Products, Sweden.
    Hanson, Charlotta
    Chalmers University of Technology, Sweden; SCA Hygiene Products, Sweden.
    Gebäck, Tobias
    Chalmers University of Technology, Sweden.
    Loren, Niklas
    RISE, SP – Sveriges Tekniska Forskningsinstitut, SP Food and Bioscience, Structure Design. Chalmers University of Technology, Sweden.
    Computational high-throughput screening of fluid permeability in heterogeneous fiber materials2016In: Soft Matter, ISSN 1744-683X, E-ISSN 1744-6848, Vol. 12, no 29, p. 6293-6299Article in journal (Refereed)
    Abstract [en]

    We explore computational high-throughput screening as a design strategy for heterogeneous, isotropic fiber materials. Fluid permeability, a key property in the design of soft porous materials, is systematically studied using a multi-scale lattice Boltzmann framework. After characterizing microscopic permeability as a function of solid volume fraction in the microstructure, we perform high-throughput computational screening of in excess of 35000 macrostructures consisting of a continuous bulk interrupted by spherical/elliptical domains with either lower or higher microscopic permeability (hence with two distinct microscopic solid volume fractions and therefore two distinct microscopic permeabilities) to assess which parameters determine macroscopic permeability for a fixed average solid volume fraction. We conclude that the fractions of bulk and domains and the distribution of solid volume fraction between them are the primary determinants of macroscopic permeability, and that a substantial increase in permeability compared to the corresponding homogenous material is attainable.

  • 11.
    Röding, Magnus
    et al.
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience. Chalmers University of Technology, Sweden.
    Svensson, Peter
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
    Loren, Niklas
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience. Chalmers University of Technology, Sweden.
    Functional regression-based fluid permeability prediction in monodisperse sphere packings from isotropic two-point correlation functions2017In: Computational materials science, ISSN 0927-0256, E-ISSN 1879-0801, Vol. 134, p. 126-131Article in journal (Refereed)
    Abstract [en]

    We study fluid permeability in random sphere packings consisting of impermeable monodisperse hard spheres. Several different pseudo-potential models are used to obtain varying degrees of microstructural heterogeneity. Systematically varying solid volume fraction and degree of heterogeneity, virtual screening of more than 10,000 material structures is performed, simulating fluid flow using a lattice Boltzmann framework and computing the permeability. We develop a well-performing functional regression model for permeability prediction based on using isotropic two-point correlation functions as microstructural descriptors. The performance is good over a large range of solid volume fractions and degrees of heterogeneity, and to our knowledge this is the first attempt at using two-point correlation functions as functional predictors in a nonparametric statistics/machine learning context for permeability prediction.

  • 12.
    Röding, Magnus
    et al.
    RISE, SP – Sveriges Tekniska Forskningsinstitut. University College London, Australia.
    Zagato, Elisa
    Ghent University, Belgium.
    Remaut, Katrien
    Ghent University, Belgium.
    Braeckmans, Kevin
    Ghent University, Belgium.
    Approximate Bayesian computation for estimating number concentrations of monodisperse nanoparticles in suspension by optical microscopy2016In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 93, no 6, article id 063311Article in journal (Refereed)
    Abstract [en]

    We present an approximate Bayesian computation scheme for estimating number concentrations of monodisperse diffusing nanoparticles in suspension by optical particle tracking microscopy. The method is based on the probability distribution of the time spent by a particle inside a detection region. We validate the method on suspensions of well-controlled reference particles. We illustrate its usefulness with an application in gene therapy, applying the method to estimate number concentrations of plasmid DNA molecules and the average number of DNA molecules complexed with liposomal drug delivery particles.

  • 13.
    Shen, Zhiqiang
    et al.
    University of Connecticut, USA.
    Röding, Magnus
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience. University College London, Australia.
    Kröger, Martin
    ETH Zürich, Switzerland.
    Li, Ying
    University of Connecticut, USA .
    Carbon nanotube length governs the viscoelasticity and permeability of buckypaper2017In: Polymers, ISSN 2073-4360, E-ISSN 2073-4360, Vol. 9, no 4, article id 115Article in journal (Refereed)
  • 14.
    Williamson, Nathan H.
    et al.
    University of South Australia, Australia.
    Nydén, Magnus
    University of South Australia, Australia; University College London, Australia.
    Röding, Magnus
    RISE, SP – Sveriges Tekniska Forskningsinstitut, SP Food and Bioscience. University of South Australia, Australia; University College London, Australia.
    The lognormal and gamma distribution models for estimating molecular weight distributions of polymers using PGSE NMR2016In: Journal of magnetic resonance, ISSN 1090-7807, E-ISSN 1096-0856, Vol. 267, p. 54-62Article in journal (Refereed)
    Abstract [en]

    We present comprehensive derivations for the statistical models and methods for the use of pulsed gradient spin echo (PGSE) NMR to characterize the molecular weight distribution of polymers via the well-known scaling law relating diffusion coefficients and molecular weights. We cover the lognormal and gamma distribution models and linear combinations of these distributions. Although the focus is on methodology, we illustrate the use experimentally with three polystyrene samples, comparing the NMR results to gel permeation chromatography (GPC) measurements, test the accuracy and noise-sensitivity on simulated data, and provide code for implementation.

  • 15.
    Williamson, Nathan H.
    et al.
    University of South Australia, Australia.
    Röding, Magnus
    RISE, SP – Sveriges Tekniska Forskningsinstitut. University College London, Australia.
    Galvosas, Petrik
    Victoria University of Wellington, New Zealand.
    Miklavcic, Stanley J.
    University of South Australia, Australia.
    Nydén, Magnus
    University College London, Australia.
    Obtaining T1-T2 distribution functions from 1-dimensional T1 and T2 measurements: The pseudo 2-D relaxation model2016In: Journal of magnetic resonance, ISSN 1090-7807, E-ISSN 1096-0856, Vol. 269, p. 186-195Article in journal (Refereed)
    Abstract [en]

    We present the pseudo 2-D relaxation model (P2DRM), a method to estimate multidimensional probability distributions of material parameters from independent 1-D measurements. We illustrate its use on 1-D T1 and T2 relaxation measurements of saturated rock and evaluate it on both simulated and experimental T1-T2 correlation measurement data sets. Results were in excellent agreement with the actual, known 2-D distribution in the case of the simulated data set. In both the simulated and experimental case, the functional relationships between T1 and T2 were in good agreement with the T1-T2 correlation maps from the 2-D inverse Laplace transform of the full 2-D data sets. When a 1-D CPMG experiment is combined with a rapid T1 measurement, the P2DRM provides a double-shot method for obtaining a T1-T2 relationship, with significantly decreased experimental time in comparison to the full T1-T2 correlation measurement.

  • 16.
    Williamson, Nathan H.
    et al.
    University of South Australia, Australia.
    Röding, Magnus
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience. University College London, Australia.
    Liu, Huabing
    Victoria University of Wellington, New Zealand; Limecho Technology Limited Company, China.
    Galvosas, Patrick
    Victoria University of Wellington, New Zealand.
    Miklavcic, Stanley J.
    University of South Australia, Australia.
    Nydén, Magnus
    University of South Australia, Australia; University College London, Australia.
    The pseudo 2-D relaxation model for obtaining T1-T2 relationships from 1-D T1 and T2 measurements of fluid in porous media2018In: Microporous and Mesoporous Materials, ISSN 1387-1811, E-ISSN 1873-3093, Vol. 269, p. 191-194Article in journal (Refereed)
    Abstract [en]

    NMR spin-lattice (T1) and spin-spin (T2) relaxation times and their inter-relation possess information on fluid behaviour in porous media. To elicit this information we utilize the pseudo 2-D relaxation model (P2DRM), which deduces the T1-T2 functional relationship from independent 1-D T1 and T2 measurements. Through model simulations we show empirically that the P2DRM accurately estimates T1-T2 relationships even when the marginal distributions of the true joint T1-T2 distribution are unknown or cannot be modeled. Estimates of the T1:. T2 ratio for fluid interacting with pore surfaces remain robust when the P2DRM is applied to simulations of rapidly acquired data. Therefore, the P2DRM can be useful in situations where experimental time is limited.

  • 17.
    Williamson, Nathan H.
    et al.
    University of South Australia, Australia.
    Röding, Magnus
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience. University College London, Australia.
    Miklavcic, Stanley J.
    University of South Australia, Australia.
    Nydén, Magnus
    RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience. University of South Australia, Australia.
    Scaling exponent and dispersity of polymers in solution by diffusion NMR2017In: Journal of Colloid and Interface Science, ISSN 0021-9797, E-ISSN 1095-7103, Vol. 493, p. 393-397Article in journal (Refereed)
    Abstract [en]

    Molecular mass distribution measurements by pulsed gradient spin echo nuclear magnetic resonance (PGSE NMR) spectroscopy currently require prior knowledge of scaling parameters to convert from polymer self-diffusion coefficient to molecular mass. Reversing the problem, we utilize the scaling relation as prior knowledge to uncover the scaling exponent from within the PGSE data. Thus, the scaling exponent—a measure of polymer conformation and solvent quality—and the dispersity (Mw/Mn) are obtainable from one simple PGSE experiment. The method utilizes constraints and parametric distribution models in a two-step fitting routine involving first the mass-weighted signal and second the number-weighted signal. The method is developed using lognormal and gamma distribution models and tested on experimental PGSE attenuation of the terminal methylene signal and on the sum of all methylene signals of polyethylene glycol in D2O. Scaling exponent and dispersity estimates agree with known values in the majority of instances, leading to the potential application of the method to polymers for which characterization is not possible with alternative techniques.

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