Pore geometry characterization-methods are important tools for understanding how pore structure influences properties such as transport through a porous material. Bottlenecks can have a large influence on transport and related properties. However, existing methods only catch certain types of bottleneck effects caused by variations in pore size. We here introduce a new measure, geodesic channel strength, which captures a different type of bottleneck effect caused by many paths coinciding in the same pore. We further develop new variants of pore size measures and propose a new way of visualizing 3-D characterization results using layered images. The new measures together with existing measures were used to characterize and visualize properties of 3-D FIB-SEM images of three leached ethyl-cellulose/hydroxypropyl-cellulose films. All films were shown to be anisotropic, and the strongest anisotropy was found in the film with lowest porosity. This film had very tortuous paths and strong geodesic channel-bottlenecks, while the paths through the other two films were relatively straight with well-connected pore networks. The geodesic channel strength was shown to give important new visual and quantitative insights about connectivity, and the new pore size measures provided useful information about anisotropies and inhomogeneities in the pore structures. The methods have been implemented in the freely available software MIST. 

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.

Porous phase-separated ethylcellulose/hydroxypropylcellulose (EC/HPC) films are used to control drug transport out of pharmaceutical pellets. Water-soluble HPC leaches out and forms a porous structure that controls the drug transport. Industrially, the pellets are coated using a fluidized bed spraying device, and a layered film exhibiting varying porosity and structure after leaching is obtained. A detailed understanding of the formation of the multilayered, phase-separated structure during production is lacking. Here, we have investigated multilayered EC/HPC films produced by sequential spin-coating, which was used to mimic the industrial process. The effects of EC/HPC ratio and spin speed on the multilayer film formation and structure were investigated using advanced microscopy techniques and image analysis. Cahn-Hilliard simulations were performed to analyze the mixing behavior. A gradient with larger structures close to the substrate surface and smaller structures close to the air surface was formed due to coarsening of the layers already coated during successive deposition cycles. The porosity of the multilayer film was found to vary with both EC/HPC ratio and spin speed. Simulation of the mixing behavior and in situ characterization of the structure evolution showed that the origin of the discontinuities and multilayer structure can be explained by the non-mixing of the layers.

Porous phase-separated films made of ethylcellulose (EC) and hydroxypropylcellulose (HPC) are commonly used for controlled drug release. The structure of these thin films is controlling the drug transport from the core to the surrounding liquids in the stomach or intestine. However, detailed understanding of the time evolution of these porous structures as they are formed remains elusive. In this work, spin-coating, a widely applied technique for making thin uniform polymer films, was used to mimic the industrial manufacturing process. The focus of this work was on understanding the structure evolution of phase-separated spin-coated EC/HPC films. The structure evolution was determined using confocal laser scanning microscopy (CLSM) and image analysis. In particular, we determined the influence of spin-coating parameters and EC : HPC ratio on the final phase-separated structure and the film thickness. The film thickness was determined by profilometry and it influences the ethanol solvent evaporation rate and thereby the phase separation kinetics. The spin speed was varied between 1000 and 10 000 rpm and the ratio of EC : HPC in the polymer blend was varied between 78 : 22 wt% and 40 : 60 wt%. The obtained CLSM micrographs showed phase separated structures, typical for the spinodal decomposition phase separation mechanism. By using confocal laser scanning microscopy combined with Fourier image analysis, we could extract the characteristic length scale of the phase-separated final structure. Varying spin speed and EC : HPC ratio gave us precise control over the characteristic length scale and the thickness of the film. The results showed that the characteristic length scale increases with decreasing spin speed and with increasing HPC ratio. The thickness of the spin-coated film decreases with increasing spin speed. It was found that the relation between film thickness and spin speed followed the Meyerhofer equation with an exponent close to 0.5. Furthermore, good correlations between thickness and spin speed were found for the compositions 22 wt% HPC, 30 wt% HPC and 45 wt% HPC. These findings give a good basis for understanding the mechanisms responsible for the morphology development and increase the possibilities to tailor thin EC/HPC film structures. 

Porous phase-separated ethylcellulose/hydroxypropylcellulose (EC/HPC) films are used to control drug transport from pharmaceutical pellets. The drug transport rate is determined by the structure of the porous films that are formed as water-soluble HPC leaches out. However, a detailed understanding of the evolution of the phase-separated structure in the films is lacking. In this work, we have investigated EC/HPC films produced by spin-coating, mimicking the industrial fluidized bed spraying. The aim was to investigate film structure evolution and coarsening kinetics during solvent evaporation. The structure evolution was characterized using confocal laser scanning microscopy and image analysis. The effect of the EC:HPC ratio (15 to 85 wt% HPC) on the structure evolution was determined. Bicontinuous structures were found for 30 to 40 wt% HPC. The growth of the characteristic length scale followed a power law, L(t) ∼ t(n), with n ∼ 1 for bicontinuous structures, and n ∼ 0.45-0.75 for discontinuous structures. The characteristic length scale after kinetic trapping ranged between 3.0 and 6.0 μm for bicontinuous and between 0.6 and 1.6 μm for discontinuous structures. Two main coarsening mechanisms could be identified: interfacial tension-driven hydrodynamic growth for bicontinuous structures and diffusion-driven coalescence for discontinuous structures. The 2D in-plane interface curvature analysis showed that the mean curvature decreased as a function of time for bicontinuous structures, confirming that interfacial tension is driving the growth. The findings of this work provide a good understanding of the mechanisms responsible for morphology development and open for further tailoring of thin EC/HPC film structures for controlled drug release. © 2022 The Royal Society of Chemistry

Porous phase-separated ethylcellulose/hydroxypropylcellulose (EC/HPC) films are used to control drug transport out of pharmaceutical pellets. The films are applied on the pellets using fluidized bed spraying. The drug transport rate is determined by the structure of the porous films that are formed as the water-soluble HPC leaches out. However, a detailed understanding of the evolution of the phase-separated structure during production is lacking. Here, we have investigated EC/HPC films produced by spin-coating, which mimics the industrial manufacturing process. This work aimed to understand the structure formation and film shrinkage during solvent evaporation. The cross-sectional structure evolution was characterized using confocal laser scanning microscopy (CLSM), profilometry and image analysis. The effect of the EC/HPC ratio on the cross-sectional structure evolution was investigated. During shrinkage of the film, the phase-separated structure undergoes a transition from 3D to nearly 2D structure evolution along the surface. This transition appears when the typical length scale of the phase-separated structure is on the order of the thickness of the film. This was particularly pronounced for the bicontinuous systems. The shrinkage rate was found to be independent of the EC/HPC ratio, while the initial and final film thickness increased with increasing HPC fraction. A new method to estimate part of the binodal curve in the ternary phase diagram for EC/HPC in ethanol has been developed. The findings of this work provide a good understanding of the mechanisms responsible for the morphology development and allow tailoring of thin EC/HPC films structure for controlled drug release. 

A porous network acts as transport paths for drugs through films for controlled drug release. The interconnectivity of the network strongly influences the transport properties. It is therefore important to quantify the interconnectivity and correlate it to transport properties for control and design of new films. This work presents a novel method for 3D visualisation and analysis of interconnectivity. High spatial resolution 3D data on porous polymer films for controlled drug release has been acquired using a focused ion beam (FIB) combined with a scanning electron microscope (SEM). The data analysis method enables visualisation of pore paths starting at a chosen inlet pore, dividing them into groups by length, enabling a more detailed quantification and visualisation. The method also enables identification of central features of the porous network by quantification of channels where pore paths coincide. The method was applied to FIB-SEM data of three leached ethyl cellulose (EC)/hydroxypropyl cellulose (HPC) films with different weight percentages. The results from the analysis were consistent with the experimentally measured release properties of the films. The interconnectivity and porosity increase with increasing amount of HPC. The bottleneck effect was strong in the leached film with lowest porosity. 

Tomography using a focused ion beam (FIB) combined with a scanning electron microscope (SEM) is well-established for a wide range of conducting materials. However, performing FIB-SEM tomography on ion- and electron-beam-sensitive materials as well as poorly conducting soft materials remains challenging. Some common challenges include cross-sectioning artifacts, shadowing effects, and charging. Fully dense materials provide a planar cross section, whereas pores also expose subsurface areas of the planar cross-section surface. The image intensity of the subsurface areas gives rise to overlap between the grayscale intensity levels of the solid and pore areas, which complicates image processing and segmentation for three-dimensional (3D) reconstruction. To avoid the introduction of artifacts, the goal is to examine porous and poorly conducting soft materials as close as possible to their original state. This work presents a protocol for the optimization of FIB-SEM tomography parameters for porous and poorly conducting soft materials. The protocol reduces cross-sectioning artifacts, charging, and eliminates shadowing effects. In addition, it handles the subsurface and grayscale intensity overlap problems in image segmentation. The protocol was evaluated on porous polymer films which have both poor conductivity and pores. 3D reconstructions, with automated data segmentation, from three films with different porosities were successfully obtained.

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.

Raster image correlation spectroscopy (RICS) is a fluorescence image analysis method for extracting the mobility, concentration, and stoichiometry of diffusing fluorescent molecules from confocal image stacks. The method works by calculating a spatial correlation function for each image and analyzing the average of those by model fitting. Rules of thumb exist for RICS image acquisitioning, yet a rigorous theoretical approach to predict the accuracy and precision of the recovered parameters has been lacking. We outline explicit expressions to reveal the dependence of RICS results on experimental parameters. In terms of imaging settings, we observed that a twofold decrease of the pixel size, e.g., from 100 to 50 nm, decreases the error on the translational diffusion constant (D) between three- and fivefold. For D = 1 μm2 s−1, a typical value for intracellular measurements, ∼25-fold lower mean-squared relative error was obtained when the optimal scan speed was used, although more drastic improvements were observed for other values of D. We proposed a slightly modified RICS calculation that allows correcting for the significant bias of the autocorrelation function at small (≪50 × 50 pixels) sizes of the region of interest. In terms of sample properties, at molecular brightness E = 100 kHz and higher, RICS data quality was sufficient using as little as 20 images, whereas the optimal number of frames for lower E scaled pro rata. RICS data quality was constant over the nM–μM concentration range. We developed a bootstrap-based confidence interval of D that outperformed the classical least-squares approach in terms of coverage probability of the true value of D. We validated the theory via in vitro experiments of enhanced green fluorescent protein at different buffer viscosities. Finally, we outline robust practical guidelines and provide free software to simulate the parameter effects on recovery of the diffusion coefficient. 

Sustainable food production and consumption are currently key issues. About one third of food produced for human consumption is wasted. In developed countries, consumers are responsible for the largest amount of food waste throughout the supply chain. The unwillingness to purchase and consume suboptimal food products is an important cause of food waste, however, the reasons behind this are still insufficiently studied. Our research addresses the question of how combinations of color, shape and damage of apples influence consumer liking and perceived sensory attributes. In a laboratory study based on factorial design of visual appearance (color, shape and damage varied from optimal to suboptimal) a total of 130 consumers evaluated sensory perception of flavor and texture attributes in apple samples. Liking was also evaluated. The results showed a significant difference in liking between an optimal apple and all apple categories with at least two out of three suboptimal properties. Further, it was a clear trend that the optimal apple was perceived as sweeter, crispier, less bitter, and less earthy than all the other apples by the participating consumers, however, the results were not statistically significant. A suboptimal appearance, therefore, had a negative effect on both perception and liking..

While molecular doping is ubiquitous in all branches of organic electronics, little is known about the spatial distribution of dopants, especially at molecular length scales. Moreover, a homogeneous distribution is often assumed when simulating transport properties of these materials, even though the distribution is expected to be inhomogeneous. In this study, electron tomography is used to determine the position of individual molybdenum dithiolene complexes and their three-dimensional distribution in a semiconducting polymer at the sub-nanometre scale. A heterogeneous distribution is observed, the characteristics of which depend on the dopant concentration. At 5 mol% of the molybdenum dithiolene complex, the majority of the dopant species are present as isolated molecules or small clusters up to five molecules. At 20 mol% dopant concentration and higher, the dopant species form larger nanoclusters with elongated shapes. Even in case of these larger clusters, each individual dopant species is still in contact with the surrounding polymer. The electrical conductivity first strongly increases with dopant concentration and then slightly decreases for the most highly doped samples, even though no large aggregates can be observed. The decreased conductivity is instead attributed to the increased energetic disorder and lower probability of electron transfer that originates from the increased size and size variation in dopant clusters. This study highlights the importance of detailed information concerning the dopant spatial distribution at the sub-nanometre scale in three dimensions within the organic semiconductor host. The information acquired using electron tomography may facilitate more accurate simulations of charge transport in doped organic semiconductors. 

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

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.

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.

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.

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.

Combined focused ion beam and scanning electron microscope (FIB-SEM) tomography is a well-established technique for high resolution imaging and reconstruction of the microstructure of a wide range of materials. Segmentation of FIB-SEM data is complicated due to a number of factors; the most prominent is that for porous materials, the scanning electron microscope image slices contain information not only from the planar cross-section of the material but also from underlying, exposed subsurface pores. In this work, we develop a segmentation method for FIB-SEM data from ethyl cellulose porous films made from ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. These materials are used for coating pharmaceutical oral dosage forms (tablets or pellets) to control drug release. We study three samples of ethyl cellulose and hydroxypropyl cellulose with different volume fractions where the hydroxypropyl cellulose phase has been leached out, resulting in a porous material. The data are segmented using scale-space features and a random forest classifier. We demonstrate good agreement with manual segmentations. The method enables quantitative characterization and subsequent optimization of material structure for controlled release applications. Although the methodology is demonstrated on porous polymer films, it is applicable to other soft porous materials imaged by FIB-SEM. We make the data and software used publicly available to facilitate further development of FIB-SEM segmentation methods. Lay Description: For imaging of very fine structures in materials, the resolution limits of, e.g. X-ray computed tomography quickly become a bottleneck. Scanning electron microscopy (SEM) provides a way out, but it is essentially a two-dimensional imaging technique. One manner in which to extend it to three dimensions is to use a focused ion beam (FIB) combined with a scanning electron microscopy and acquire tomography data. In FIB-SEM tomography, ions are used to perform serial sectioning and the electron beam is used to image the cross section surface. This is a well-established method for a wide range of materials. However, image analysis of FIB-SEM data is complicated for a variety of reasons, in particular for porous media. In this work, we analyse FIB-SEM data from ethyl cellulose porous films made from ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. These films are used as coatings for controlled drug release. The aim is to perform image segmentation, i.e. to identify which parts of the image data constitute the pores and the solid, respectively. Manual segmentation, i.e. when a trained operator manually identifies areas constituting pores and solid, is too time-consuming to do in full for our very large data sets. However, by performing manual segmentation on a set of small, random regions of the data, we can train a machine learning algorithm to perform automatic segmentation on the entire data sets. The method yields good agreement with the manual segmentations and yields porosities of the entire data sets in very good agreement with expected values. The method facilitates understanding and quantitative characterization of the geometrical structure of the materials, and ultimately understanding of how to tailor the drug release. © 2020 The Authors.

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.

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.

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, including both granular and continuous solid phases, 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 artificial neural networks are superior to the more conventional regression methods for establishing quantitative structure-property relationships. We make the data and code used publicly available to facilitate further development of permeability prediction methods.

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.

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.

Small-angle X-ray scattering (SAXS) is a useful technique for nanoscale structural characterization of materials. In SAXS, structural and spatial information is indirectly obtained from the scattering intensity in the spectral domain, known as the reciprocal space. Therefore, characterizing the structure requires solving the inverse problem of finding a plausible structure model that corresponds to the measured scattering intensity. Both the choice of structure model and the computational workload of parameter estimation are bottlenecks in this process. In this work, we develop a framework for analysis of SAXS data from disordered materials. The materials are modeled using Gaussian Random Fields (GRFs). We study the case of two phases, pore and solid, and three phases, where a third phase is added at the interface between the two other phases. Further, we develop very fast GPU-accelerated, Fourier transform-based numerical methods for both structure generation and SAXS simulation. We demonstrate that length scales and volume fractions can be predicted with good accuracy using our machine learning-based framework. The parameter prediction executes virtually instantaneously and hence the computational burden of conventional model fitting can be avoided. Copyright © 2022 Röding, Tomaszewski, Yu, Borg and Rönnols.

The three-dimensional microstructure of functional materials determines its effective properties, like the mass transport properties of a porous material. Hence, it is desirable to be able to tune the properties by tuning the microstructure accordingly. In this work, we study a class of spinodoid i.e. spinodal decomposition-like structures with tunable anisotropy, based on Gaussian random fields. These are realistic yet computationally efficient models for bicontinuous porous materials. We use a convolutional neural network for predicting effective diffusivity in all three directions. We demonstrate that by incorporating the predictions of the neural network in an approximate Bayesian computation framework for inverse problems, we can in a computationally efficient manner design microstructures with prescribed diffusivity in all three directions. © 2022, The Author(s).

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.

A new concept has been developed for characterizing the real-time evolution of the three-dimensional pore and lamella microstructure of bread during baking using synchrotron X-ray microtomography (SRµCT). A commercial, combined microwave-convective oven was modified and installed at the TOMCAT synchrotron tomography beamline at the Swiss Light Source (SLS), to capture the 3D dough-to-bread structural development in-situ at the micrometer scale with an acquisition time of 400 ms. This allowed characterization and quantitative comparison of three baking technologies: (1) convective heating, (2) microwave heating, and (3) a combination of convective and microwave heating. A workflow for automatic batchwise image processing and analysis of 3D bread structures (1530 analyzed volumes in total) was established for porosity, individual pore volume, elongation, coordination number and local wall thickness, which allowed for evaluation of the impact of baking technology on the bread structure evolution. The results showed that the porosity, mean pore volume and mean coordination number increase with time and that the mean local cell wall thickness decreases with time. Small and more isolated pores are connecting with larger and already more connected pores as function of time. Clear dependencies are established during the whole baking process between the mean pore volume and porosity, and between the mean local wall thickness and the mean coordination number. This technique opens new opportunities for understanding the mechanisms governing the structural changes during baking and discern the parameters controlling the final bread quality. © 2023 The Author(s)

Phase-separated polymer films are commonly used as coatings around pharmaceutical oral dosage forms (tablets or pellets) to facilitate controlled drug release. A typical choice is to use ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. When an EC/HPC film is in contact with water, the leaching out of the water-soluble HPC phase produces an EC film with a porous network through which the drug is transported. The drug release can be tailored by controlling the structure of this porous network. Imaging and characterization of such EC porous films facilitates understanding of how to control and tailor film formation and ultimately drug release. Combined focused ion beam and scanning electron microscope (FIB-SEM) tomography is a well-established technique for high-resolution imaging, and suitable for this application. However, for segmenting image data, in this case to correctly identify the porous network, FIB-SEM is a challenging technique to work with. In this work, we implement convolutional neural networks for segmentation of FIB-SEM image data. The data are acquired from three EC porous films where the HPC phases have been leached out. The three data sets have varying porosities in a range of interest for controlled drug release applications. We demonstrate very good agreement with manual segmentations. In particular, we demonstrate an improvement in comparison to previous work on the same data sets that utilized a random forest classifier trained on Gaussian scale-space features. Finally, we facilitate further development of FIB-SEM segmentation methods by making the data and software used open access. © 2021 The Authors.

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.

Manufacturing pasta is a rather well known process, but it is still challenging to tailor pasta products with new raw materials. In this study, we evaluated the effects of raw materials on the microstructure and water distribution in cooked pasta using 1H magnetic resonance imaging (MRI) as well as bright field and polarized light microscopy. The MRI parameters initial intensity (I0) and transverse dephasing time (T2 *) serve as indicators of the local water concentration and water-macromolecule interactions through chemical exchange, respectively. These parameters were mapped throughout the whole pasta volume with a spatial resolution of 78?m in all three dimensions. MRI was combined with light microscopy to link I0 and T2 * to microstructure components such as fiber particles and the extent of starch gelatinization. Four commercial spaghetti samples were analyzed which were made of durum wheat flour, both plain and enriched with wheat fiber, as well as with wholegrain and soft wheat flour. Although all pasta samples showed similar macroscopic water absorption as measured by weight increase, the sample structures differed at the microscopic scale. Compared to durum wheat spaghetti, the presence of fiber particles decreased T2 *, while spaghetti enriched with soft wheat flour increased T2 *. In addition, light microscopy showed that large fiber particles partly acted as barriers against water migration and protected starch granules from swelling. Smaller wheat fiber particles did not affect local starch swelling. Thus, the combination of light microscopy and MRI is a powerful tool to study the microstructure and water distribution in pasta.

Many products incorporate into their design fibrous material with particular levels of permeability as a way to control the retention and flow of liquid. The production and experimental testing of these materials can be expensive and time consuming, particularly if it needs to be optimised to a desired level of absorbency. We consider a parametric virtual fiber model as a replacement for the real material to facilitate studying the relationship between structure and properties in a cheaper and more convenient manner. 3D image data sets of a sample fibrous material are obtained using X-ray microtomography and the individual fibers isolated. The segmented fibers are used to estimate the parameters of a 3D stochastic model for generating softcore virtual fiber structures. We use several spatial measures to show the consistency between the real and virtual structures, and demonstrate with lattice Boltzmann simulations that our virtual structure has good agreement with respect to the permeability of the physical material. © 2021 The Author(s)

To facilitate printing, coatings are typically applied to paperboard used for packaging to provide a good surface for application. To optimise the performance of the coating, it is important to understand the relationship between the microstructure of the material and its mass transport properties. In this work, three samples of paperboard coating are imaged using combined focused ion beam and scanning electron microscope (FIB-SEM) tomography data appropriately segmented to characterise the internal microstructure. These images are used to inform a parametric, tessellation-based stochastic three-dimensional model intended to mimic the irregular geometry of the particles that can be seen in the coating. Parameters for the model are estimated from the FIB-SEM image data, and we demonstrate good agreement between the real and virtual structures both in terms of geometrical measures and mass transport properties. The development of this model facilitates exploration of the relationship between the structure and its properties. © 2021 The Author(s)

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.

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.

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.

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.

Conventional analysis of fluorescence recovery after photobleaching (FRAP) data for diffusion coefficient estimation typically involves fitting an analytical or numerical FRAP model to the recovery curve data using non-linear least squares. Depending on the model, this can be time consuming, especially for batch analysis of large numbers of data sets and if multiple initial guesses for the parameter vector are used to ensure convergence. In this work, we develop a completely new approach, DeepFRAP, utilizing machine learning for parameter estimation in FRAP. From a numerical FRAP model developed in previous work, we generate a very large set of simulated recovery curve data with realistic noise levels. The data are used for training different deep neural network regression models for prediction of several parameters, most importantly the diffusion coefficient. The neural networks are extremely fast and can estimate the parameters orders of magnitude faster than least squares. The performance of the neural network estimation framework is compared to conventional least squares estimation on simulated data, and found to be strikingly similar. Also, a simple experimental validation is performed, demonstrating excellent agreement between the two methods. We make the data and code used publicly available to facilitate further development of machine learning-based estimation in FRAP. Lay description: Fluorescence recovery after photobleaching (FRAP) is one of the most frequently used methods for microscopy-based diffusion measurements and broadly used in materials science, pharmaceutics, food science and cell biology. In a FRAP experiment, a laser is used to photobleach fluorescent particles in a region. By analysing the recovery of the fluorescence intensity due to the diffusion of still fluorescent particles, the diffusion coefficient and other parameters can be estimated. Typically, a confocal laser scanning microscope (CLSM) is used to image the time evolution of the recovery, and a model is fit using least squares to obtain parameter estimates. In this work, we introduce a new, fast and accurate method for analysis of data from FRAP. The new method is based on using artificial neural networks to predict parameter values, such as the diffusion coefficient, effectively circumventing classical least squares fitting. This leads to a dramatic speed-up, especially noticeable when analysing large numbers of FRAP data sets, while still producing results in excellent agreement with least squares. Further, the neural network estimates can be used as very good initial guesses for least squares estimation in order to make the least squares optimization convergence much faster than it otherwise would. This provides for obtaining, for example, diffusion coefficients as soon as possible, spending minimal time on data analysis. In this fashion, the proposed method facilitates efficient use of the experimentalist's time which is the main motivation to our approach. The concept is demonstrated on pure diffusion. However, the concept can easily be extended to the diffusion and binding case. The concept is likely to be useful in all application areas of FRAP, including diffusion in cells, gels and solutions. © 2020 The Authors. 

Liposomes can efficiently deliver messenger RNA (mRNA) into cells. When mRNA cocktails encoding different proteins are needed, a considerable challenge is to efficiently deliver all mRNAs into the cytosol of each individual cell. In this work, two methods are explored to co-deliver varying ratiometric doses of mRNA encoding red (R) or green (G) fluorescent proteins and it is found that packaging mRNAs into the same lipoplexes (mingle-lipoplexes) is crucial to efficiently deliver multiple mRNA types into the cytosol of individual cells according to the pre-defined ratio. A mixture of lipoplexes containing only one mRNA type (single-lipoplexes), however, seem to follow the “first come – first serve” principle, resulting in a large variation of R/G uptake and expression levels for individual cells leading to ratiometric dosing only on the population level, but rarely on the single-cell level. These experimental observations are quantitatively explained by a theoretical framework based on the stochasticity of mRNA uptake in cells and endosomal escape of mingle- and single-lipoplexes, respectively. Furthermore, the findings are confirmed in 3D retinal organoids and zebrafish embryos, where mingle-lipoplexes outperformed single-lipoplexes to reliably bring both mRNA types into single cells. This benefits applications that require a strict control of protein expression in individual cells. © 2021 The Authors.