Predicting permeability via statistical learning on higher-order microstructural information

Quantitative structure-property relationships are crucial for the understanding and prediction of the physical properties of complex materials. For fluid flow in porous materials, characterizing the geometry of the pore microstructure facilitates prediction of permeability, a key property that has been extensively studied in material science, geophysics and chemical engineering. In this work, we study the predictability of different structural descriptors via both linear regressions and neural networks. A large data set of 30,000 virtual, porous microstructures of different types is created for this end. We compute permeabilities of these structures using the lattice Boltzmann method, and characterize the pore space geometry using one-point correlation functions (porosity, specific surface), two-point surface-surface, surface-void, and void-void correlation functions, as well as the geodesic tortuosity as an implicit descriptor. Then, we study the prediction of the permeability using different combinations of these descriptors. We obtain significant improvements of performance when compared to a Kozeny-Carman regression with only lowest-order descriptors (porosity and specific surface). We find that combining all three two-point correlation functions and tortuosity provides the best prediction of permeability, with the void-void correlation function being the most informative individual descriptor. Moreover, the combination of porosity, specific surface, and geodesic tortuosity provides very good predictive performance. This shows that higher-order correlation functions are extremely useful for forming a general model for predicting physical properties of complex materials. Additionally, our results suggest that neural networks are superior to the more conventional regression methods for establishing quantitative structure-property relationships

Estimation of mass thickness response of embedded aggregated silica nanospheres from high angle annular dark-field scanning transmission electron micrographs

In this study we investigate the functional behavior of the intensity in high-angle annular dark field (HAADF) scanning transmission electron micrograph (STEM) images. The model material is a silica particle (20 nm) gel at 5 wt%. By assuming that the intensity response is monotonically increasing with increasing mass thickness of silica, an estimate of the functional form is calculated using a maximum likelihood approach. We conclude that a linear functional form of the intensity provides a fair estimate but that a power function is significantly better for estimating the amount of silica in the z-direction. The work adds to the development of quantifying material properties from electron micrographs, especially in the field of tomography methods and three-dimensional quantitative structural characterization from a STEM micrograph. It also provides means for direct three-dimensional quantitative structural characterization from a STEM micrograph

Approximate Bayesian computation for estimating number concentrations of monodisperse nanoparticles in suspension by optical microscopy

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

On-chip light sheet illumination enables diagnostic size and concentration measurements of membrane vesicles in biofluids

Cell-derived membrane vesicles that are released in biofluids, like blood or saliva, are emerging as potential non-invasive biomarkers for diseases, such as cancer. Techniques capable of measuring the size and concentration of membrane vesicles directly in biofluids are urgently needed. Fluorescence single particle tracking microscopy has the potential of doing exactly that by labelling the membrane vesicles with a fluorescent label and analysing their Brownian motion in the biofluid. However, an unbound dye in the biofluid can cause high background intensity that strongly biases the fluorescence single particle tracking size and concentration measurements. While such background intensity can be avoided with light sheet illumination, current set-ups require specialty sample holders that are not compatible with high-throughput diagnostics. Here, a microfluidic chip with integrated light sheet illumination is reported, and accurate fluorescence single particle tracking size and concentration measurements of membrane vesicles in cell culture medium and in interstitial fluid collected from primary human breast tumours are demonstrated

Effective diffusivity in lattices of impermeable superballs

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 shape

Optimisation of applied harmonics in Fourier Transform Rheology to enablerapid acquisition of mechanical spectra of strain-sensitive,time dependent materials

Biological fluids such as food boluses are complex fluids which often are inhomogeneous, change over time and have a limited linear region. The rheological properties of a food bolus determine how easy it is to swallow which is crucial for those suffering from swallowing disorders. It is advantageous to use Fourier transform rheology to quickly obtain the mechanical spectrum of a bolus as it changes over time. Several harmonic strains are superimposed, and the resulting stress response is transformed into a mechanical spectrum. A novel optimisation algorithm was applied to minimise the maximal strain and strain rate applied to the sensitive bolus sample. The time to obtain a mechanical spectrum was reduced from 10 to 3.5 minutes

Optimisation of applied harmonics in Fourier Transform Rheology to enablerapid acquisition of mechanical spectra of strain-sensitive,time dependent materials

Biological fluids such as food boluses are complex fluids which often are inhomogeneous, change over time and have a limited linear region. The rheological properties of a food bolus determine how easy it is to swallow which is crucial for those suffering from swallowing disorders. It is advantageous to use Fourier transform rheology to quickly obtain the mechanical spectrum of a bolus as it changes over time. Several harmonic strains are superimposed, and the resulting stress response is transformed into a mechanical spectrum. A novel optimisation algorithm was applied to minimise the maximal strain and strain rate applied to the sensitive bolus sample. The time to obtain a mechanical spectrum was reduced from 10 to 3.5 minutes

Massively parallel approximate Bayesian computation for estimating nanoparticle diffusion coefficients, sizes and concentrations using confocal laser scanning microscopy

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

The lognormal and gamma distribution models for estimating molecular weight distributions of polymers using PGSE NMR

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

Measuring absolute nanoparticle number concentrations from particle count time series

Single-particle microscopy is important for characterization of nanoparticulate matter for which accurate concentration measurements are crucial. We introduce a method for estimating absolute number concentrations in nanoparticle dispersions based on a fluctuating time series of particle counts, known as a Smoluchowski process. Thus, unambiguous tracking of particles is not required and identification of single particles is sufficient. However, the diffusion coefficient of the particles must be estimated separately. The proposed method does not require precalibration of the detection region volume, as this can be estimated directly from the observations. We evaluate the method in a simulation study and on experimental data from a series of dilutions of 0.2- and 0.5-m polymer nanospheres in water, obtaining very good agreement with reference values