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The gas flow through sheet-like porous materials such as paper can show marked lateral variations due to a heterogeneous, locally varying microstructure. Hence, reliable predictions of such lateral flux variations require an appropriate consideration of local variations in the microstructure. The flow through such sheet-like materials is commonly described with Darcy’s law in which permeances are formulated in terms of microstructure properties, such as porosities, tortuosities, or hydraulic radii. This work proposes an extension of existing permeance models that directly considers the variation and the cross-dependence between local microstructure properties. The extended model is applied to local air fluxes through a paper sheet to exemplarily reveal the joint impact of local porosities and local tortuosities on the air flux. The key extension is to consider a joint distribution of porosity and tortuosity. The latter is constructed from the univariate property distributions using a copula approach and yields local tortuosities including their variation for any encountered local porosity. These values jointly enter any permeance model that qualitatively captures the dependence of the air flux on the porosity. To assess the merit of the model, variations in the air flux and in the pore space properties are independently determined from the same measured microstructure of paper. Air flux variations are provided by computational fluid dynamics simulations on multiple, nonoverlapping segments taken from the microstructure. A statistical analysis of the entire microstructure provides the distribution of local porosity, tortuosity, and thicknesses. Our model quantitatively explains that porosity-dependent variations in the tortuosity, in particular the ones associated with high-volume pathways, decisively determine air flux variations.

The famous Pioneer camp ‘Artek’ was used by the Soviet Union to showcase socialism to upcoming generations. It was an iconic space where each summer 5,000 children from over 60 nations met to promote transnational cooperation and Soviet-led world peace. The article reconstructs American children’s experiences who dared to break through the iron curtain to participate. It will examine how, and with what success, the participants’ encounters challenged and shaped their perceptions of East and West, and how their actions fed into the upsurgence of citizen diplomacy that played a crucial role in ending the Cold War.

We present a novel, practical method to determine the Frattini subgroup of a polycyclic group. This method is based on new theoretical investigations about complements and module structure of elementary abelian sections in polycyclic groups. We have implemented our method in GAP and include a discussion of this implementation.

A law of large numbers for the empirical distribution of parameters of a one-layer artificial neural networks with sparse connectivity is derived for a simultaneously increasing number of both, neurons and training iterations of the stochastic gradient descent., 12 pages, 4 figures

This work uses an open-source, data-driven, image-based modelling framework; OpenImpala. Lithium Iron Phosphate (LFP) electrode samples were imaged using micro-CT. Two scans were performed using the Zeiss 160 kVp Versa 510 at the University of Southampton. The two scans were both carried out on the same sample and using the same focal point, this is so they could be used as a direct comparison to see how porosity and tortuosity changed with spatial resolution. It is found that the Bruggeman correlation significantly underestimates the tortuosity compared to the OpenImpala calculated results. It is also found that there is a larger statistical variability in the 801 nm results, whereas the 400 nm results have a much smaller standard deviation. These results were compared to computer-generated images based on FIB/SEM tomographic data of a single Nickel Manganese Cobalt Oxide (NMC) porous active particle and found to have the same trends.

Colchicum autumnale are toxic autumn-blooming flowering plants, which often grow on extensive meadows and pastures. Thus, they pose a threat to farm animals especially in hay and silage. Intensive grassland management or the use of herbicides could reduce these weeds but environment protection requirements often prohibit these measures. For this reason, a non-chemical site- or plant-specific weed control is sought, which aims only at a small area around the C. autumnale and with low impact on the surrounding flora and fauna. For this purpose, however, the exact locations of the plants must be known. In the present paper, a procedure to locate blooming C. autumnale in high-resolution drone images in the visible light range is presented. This approach relies on convolutional neural networks to detect the flower positions. The training data, which is based on hand-labeled images, is further enhanced through image augmentation. The quality of the detection was evaluated in particular for grassland sites which were not included in the training to get an estimate for how well the detector works on previously unseen sites. In this case, 88.6% of the flowers in the test dataset were detected, which makes it suitable, e.g., for applications where the training is performed by the manufacturer of an automatic treatment tool and where the practitioners apply it to their previously unseen grassland sites.

Bayesian optimization is proposed for automatic learning of optimal controller parameters from experimental data. A probabilistic description (a Gaussian process) is used to model the unknown function from controller parameters to a user-defined cost. The probabilistic model is updated with data, which is obtained by testing a set of parameters on the physical system and evaluating the cost. In order to learn fast, the Bayesian optimization algorithm selects the next parameters to evaluate in a systematic way, for example, by maximizing information gain about the optimum. The algorithm thus iteratively finds the globally optimal parameters with only few experiments. Taking throttle valve control as a representative industrial control example, the proposed auto-tuning method is shown to outperform manual calibration: it consistently achieves better performance with a low number of experiments. The proposed auto-tuning framework is flexible and can handle different control structures and objectives., 11 pages, 7 figures and 4 tables. To appear in IEEE Transactions on Control Systems Technology

This article examines several aspects of knowledge production among practitioners of 21st century capitalism, critiquing the motivations, funding, methods, and dissemination of today’s politically ...

The variability and interdependence of local porosity and local mean geodesic tortuosity, which is a measure for the sinuosity of shortest transportation paths, is investigated at the example of the microstructure in sack paper. By means of statistical image analysis, these two morphological characteristics are computed for several cutouts of 3D image data obtained by X-ray microcomputed tomography. Considering cutouts of different sizes allows us to study the influence of the sample size on the local variability of the considered characteristics. Moreover, the interdependence between local porosity and local mean geodesic tortuosity is quantified by modeling their joint distribution parametrically using Archimedean copulas. It turns out that the family of Gumbel copulas is an appropriate model type, which is formally validated by a goodness of fit test. Besides mean geodesic tortuosity, we consider further related morphological characteristics, describing the sinuosity of those shortest transportation paths, whose minimum diameter exceeds a predefined threshold. Moreover, we show that the copula approach investigated in this paper can also be used to quantify the negative correlation between local porosity and these modified versions of local mean geodesic tortuosity. Our results elucidate the impact of local porosity on various kinds of morphological characteristics, which are not experimentally accessible and which are important for local air permeance – a key property of sack paper.

Systemic AL amyloidosis is a rare disease that is caused by the misfolding of immunoglobulin light chains (LCs). Potential drivers of amyloid formation in this disease are post-translational modifications (PTMs) and the mutational changes that are inserted into the LCs by somatic hypermutation. Here we present the cryo electron microscopy (cryo-EM) structure of an ex vivo ��1-AL amyloid fibril whose deposits disrupt the ordered cardiomyocyte structure in the heart. The fibril protein contains six mutational changes compared to the germ line and three PTMs (disulfide bond, N-glycosylation and pyroglutamylation). Our data imply that the disulfide bond, glycosylation and mutational changes contribute to determining the fibril protein fold and help to generate a fibril morphology that is able to withstand proteolytic degradation inside the body., publishedVersion

A two-step framework to analyze local microstructure variations of paper sheets based on 3D image data is presented. First, a multi-stage workflow efficiently acquires a large set of highly resolved tomographic image data, which enables—in combination with statistical image analysis—the quantification of local variations and pairwise correlations of morphological microstructure characteristics on length scales ranging from micrometers to centimeters. Secondly, the microstructure is analyzed in terms of the local behavior of porosity, thickness, and further descriptors related to transportation paths. The power of the presented framework is demonstrated, showing that it allows one (i) to quantitatively reveal the difference in terms of local structural variations between a model paper before and after unidirectional compression via hard-nip calendering and that (ii) the field of view which is required to reliably compute the probability distributions of the considered local microstructure characteristics is at least 20 mm$^{2}$. The results elucidate structural differences related to local densification. In particular, it is shown how calendering transforms local variations in sheet thickness into marked local mass density variations. The obtained results are in line with experimental measurements of macroscopic properties (basis weight, Bekk smoothness parameters, thickness, and Gurley retention times).

Relationships between microstructure characteristics and effective transport properties of granular materials are crucial for many real-world applications. In the present paper microstructure-property relationships of sphere packings are investigated by means of modeling and simulation. Virtual microstructures are generated with the random close packing algorithm. This algorithm provides initial systems of randomly distributed, non-overlapping and densely-packed spheres of a given class of polydisperse size distributions. Next, the initial sphere packing is further densified until a certain criterion is reached, namely a predefined mean contact angle. In this way, we obtain a large database of slightly overlapping sphere systems. Subsequently, effective transport properties of the sphere systems (solid) and their complementary sets (pores) are determined using the computationally efficient resistor network method. Finally, the generated virtual microstructures are used to establish formulas expressing effective transport properties of the considered sphere packings in terms of the mean contact angle and the standard deviation of the particle radii.

We investigate the problem of estimating geodesic tortuosity and constrictivity as two structural characteristics of stationary random closed sets. They are of central importance for the analysis of effective transport properties in porous or composite materials. Loosely speaking, geodesic tortuosity measures the windedness of paths whereas the notion of constrictivity captures the appearance of bottlenecks resulting from narrow passages within a given materials phase. We first provide mathematically precise definitions of these quantities and introduce appropriate estimators. Then, we show strong consistency of these estimators for unboundedly growing sampling windows. In order to apply our estimators to real datasets, the extent of edge effects needs to be controlled. This is illustrated using a model for a multi-phase material that is incorporated in solid oxid fuel cells., Comment: 30 pages, 7 figures

A pluri Gaussian model for three phase microstructures is presented and relationships between model parameters and microstructure characteristics are discussed. In particular, analytical formulas for two point coverage probability functions in terms of covariance functions of the underlying Gaussian random fields are considered, which allow for an efficient estimation of model parameters. The model is fitted to tomographic image data obtained by FIB tomography, which represent porous gas diffusion electrodes consisting of silver and polytetrafluorethylene. The considered type of electrode is used as oxygen depolarized cathode for the production of chlorine. In order to fit the microstructure model, the covariance functions of the Gaussian random fields are parameterized, which leads to a stochastic microstructure model with five parameters. It is shown that most microstructure characteristics of tomographic image data are well reproduced by the model despite the low number of model parameters. Finally, limitations of the model with respect to the fit of continuous phase size distributions are discussed. Combining stochastic microstructure modeling with numerical simulation of effective macroscopic properties will allow in future work for a model based investigation of microstructure property relationships for the considered gas diffusion electrodes

The properties of particulate materials are influenced by purely geometric properties like size, size distribution, and shape. However, size is an ambiguous quantity for non-spherical particles. Therefore, the impact of particle shape on the size as measured by several common and original estimators is investigated. Particularly, a high-fidelity digital sieving method based on the first principal axis of a particle is proposed. An XRCT scan of asphalt concrete is analysed for the shape distribution and its compliance with typical standards. Then, a larger synthetic particle set with a statistically similar, but slightly more extreme shape distribution is generated for testing the size estimators. It is found that the equivalent volume sphere should be abandoned and alternatives are proposed. The analysis is based on simple discrete computational geometry and can easily be implemented.

We show that attenuation X-ray microcomputed tomography (μ-CT) offers a route to extract the three-dimensional pore space of paper reliably enough to distinguish samples of the same kind of paper. Here, we consider two sack kraft papers for cement bags with different basis weights and thicknesses. Sample areas of approximately 5 mm2 with a resolution of 1.5 μm are considered, i.e. sizes that exceed sample areas of 2 mm2 for which the pore structure was previously studied in the literature. The image segmentation is based on indicator kriging as a local method that removes ambiguities in assigning voxels as pore or as fibre. The microstructures of the two samples are statistically compared in terms of descriptors such as sheet thickness, porosity, fractions of externally accessible pores and mean geodesic tortuosity. We demonstrate that a quantitative comparison of samples in terms of porosity and thickness requires a common definition of the sheet surfaces. Finally, the statistical pore space analysis based on the μ-CT scans reliably reveals structural differences between the two paper samples, but only when several descriptors are used. LAY DESCRIPTION This paper is a seemingly abundant material. Its intrinsic porosity enables a vast number of commercial applications. Particularly packing products, e.g. cement bags, often incorporate sack kraft paper due to its high porosity and its additional mechanical strength. A direct quantification of the porosity of sack kraft papers is, hence, particularly desirable. However, experimental quantification of paper porosity or its pore network properties is difficult and often highly indirect. A nondestructive statistical analysis of the 3D microstructure holds the promise to directly assess the pores. In particular, X-ray microcomputed tomography (μ-CT), frequently with sub-μm resolution, has been established as a method to study the fibre and pore structure of paper. The question arises, whether statistical analysis of the microstructure based on μ-CT imaging is sufficient to reliably distinguish between different sack kraft papers. Here, we explore whether the pore structure of paper can be extracted and statistically analysed for larger sample areas despite the fact that a larger sample size directly translates into a lower resolution of the μ-CT scan. We expect that a large sample size increases the region of interest on the basis of which samples can be better distinguished. A lowered resolution poses a severe challenge for the reliable identification of voxel data as pores or as fibres, because the contrast between paper fibres (made of cellulose) and air, which is established due to X-ray absorption, is weak. We show that we can reliably assign each voxel by using an indicator kriging as a two-step method. This method performs an initial voxel identification based on the overall distribution of measured grey values and refines the identification by inspecting the local environment of each voxel. For the pore space extracted in such a way, we can then compute quantities that are related to the geometry and connectivity properties of the pores. Furthermore, we address a paper-born challenge for such an analysis, i.e. we cannot always unambiguously tell whether a pore is located inside the paper sheet or at the surface of the paper. The way the paper surfaces are extracted from the microstructure decisively determines the final specifications of the predicted properties. A significant distinction of the samples is only possible when comparing the properties of the pore network.

Introduction: The destruction of habitats has not only reduced biological diversity but also affected essential ecosystem services of the Central European cultural landscape. Therefore, in the further development of the cultural landscape and in the management of natural resources, special importance must be attached to the habitat demands of species and the preservation of ecosystem services. The study of ecosystem services has extended its influence into spatial planning and landscape ecology, the integration of which can offer an opportunity to enhance the saliency, credibility, and legitimacy of landscape ecology in spatial planning issues.Objective: This paper proposes a methodology to detect red deer habitats for e.g. huntable game. The model is established on remote sensing based value-added information products, the derived landscape structure information and the use of spatially and temporally imprecise in-situ data (e.g. available hunting statistics). In order to realize this, four statistical model approaches were developed and their predictive performance assessed.Methods: Altogether, our results indicate that based on the data mentioned above, modeling of habitats is possible using a coherent statistical model approach. All four models showed an overall classification of > 60% and in the best case 71,4%. The models based on logistic regression using preference data derived from 5-year hunting statistics, which has been interpreted as habitat suitability. The landscape metrics (LSM) will be calculated on the basis of the Global Forest Change dataset (HANSEN et al. 2013b ). The interpolation of landcover data into landscape-level was made with the software FRAGSTAT and the moving window approach. Correlation analysis is used to identify relevant LSM serving as inputs; logistic regression was used to derive a final binary classifier for habitat suitability values. Three model variations with different sets of LSM are tested using the unstandardized regression coefficient. Results lead to an insight of the effect of each LSM but not on the strength of the effect. Furthermore, the predicted outcome is rather difficult to interpret as different units and scales for each LSM are used. Hence, we calculated the fourth model using the standardized regression coefficient. It harmonized the measurement units of the LSM and thus allowed a better comparison, interpretation, and evaluation.Conclusion: Our research reveals that applying a statistical model using coarse data is effective to identify potential red deer habitats in a significant qualitative manner. The presented approach can be analogously applied to other mammals if the relevant structural requirements and empirical habitat suitability data (e.g. home range, biotopes, and food resources) are known. The habitat preferences of red deer are best described by LSM concerning area-relation and wildlife-edge relations. Most important are edges between meadows, pastures or agricultural field and forest, as well as short paths between those elements for food resources. A large proportion of forest is important for species survival and positively influences the occurrence of red deer. Outcomes help to understand species –habitat relation and on which scale wildlife perceives the landscape. In addition, they support the practical habitat management and thus the overall species diversity.

International audience; Effective conductivity and permeability of a versatile, graph-based model of random structures are investigated numerically. This model, originally introduced in Gaiselmann et al. (2014) allows one to simulate a wide class of realistic materials. In the present work, an extensive dataset of two-phase microstructures with wide-ranging morphological features is used to assess the relationship between microstructure and effective transport properties, which are computed using Fourier-based methods on digital images. Our main morphological descriptors are phase volume fractions, mean geodesic tortuosity, two “hydraulic radii” for characterizing the length scales of heterogeneities, and a “constrictivity” parameter that describes bottleneck effects. This additional parameter, usually not considered in homogenization theories, is an essential ingredient for predicting transport properties, as observed in Gaiselmann et al. (2014). We modify the formula originally developed in Stenzel et al. (2016) for predicting the effective conductivity and propose a formula for permeability. For the latter one, different geometrical definitions of the hydraulic radius are compared. Our predictions are validated using tomographic image data of fuel cells.

ACS applied energy materials 3(12), 12565 - 12574 (2020). doi:10.1021/acsaem.0c02494, Commercially used LiNi$_{1/3}$Mn$_{1/3}$Co$_{1/3}$O$_2$ (NMC111) in lithium-ion batteries mainly consists of a large-grained nonporous active material powder prepared by coprecipitation. However, nanomaterials are known to have extreme influence on gravimetric energy density and rate performance but are not used at the industrial scale because of their reactivity, low tap density, and diminished volumetric energy density. To overcome these problems, the build-up of hierarchically structured active materials and electrodes consisting of microsized secondary particles with a primary particle scale in the nanometer range is preferable. In this paper, the preparation and detailed characterization of porous hierarchically structured active materials with two different median secondary particle sizes, namely, 9 and 37 μm, and primary particle sizes in the range 300–1200 nm are presented. Electrochemical investigations by means of rate performance tests show that hierarchically structured electrodes provide higher specific capacities than conventional NMC111, and the cell performance can be tuned by adjustment of processing parameters. In particular, electrodes of coarse granules sintered at 850 °C demonstrate more favorable transport parameters because of electrode build-up, that is, the morphology of the system of active material particles in the electrode, and demonstrate superior discharge capacity. Moreover, electrodes of fine granules show an optimal electrochemical performance using NMC powders sintered at 900 °C. For a better understanding of these results, that is, of process-structure–property relationships at both granule and electrode levels, 3D imaging is performed with a subsequent statistical image analysis. Doing so, geometrical microstructure characteristics such as constrictivity quantifying the strength of bottleneck effects and descriptors for the lengths of shortest transportation paths are computed, such as the mean number of particles, which have to be passed, when going from a particle through the active material to the aluminum foil. The latter one is at lowest for coarse-grained electrodes and seems to be a crucial quantity., Published by ACS Publications, Washington, DC

Within the last decade the combination of photoredox catalysis and other catalytic modes of activation has become a powerful tool for organic synthesis to enable transformations that are not possible using single catalyst systems and hence are complementary to traditional methodology. Especially reactions proceeding via synergistic catalysis where co-catalyst and photocatalyst simultaneously and separately activate different reaction partners greatly benefit from the special properties of molecules and transition metal complexes in their excited state being oxidizing and reducing in nature at the same time. Apart from allowing for the generation of radical (open-shell) reactive intermediates by SET under mild conditions from bench-stable, abundant precursors, the photocatalyst often acts to interweave the distinct catalytic cycles by interaction at multiple points of the reaction mechanism to provide overall redox-neutral processes by shuttling electrons within in this complex network of elementary reaction steps. Synergistic strategies moreover may allow to performing such reactions with enantioselectivity, while mostly the selectivity is achieved by the chiral co-catalyst. The merger of photocatalysis has been achieved with a broad range of alternative modes of catalysis including organocatalysis, Brønstedt and Lewis acid and base catalysis, enzyme catalysis as well as in the context of cross-coupling transition metal catalysis overcoming challenging steps in this methodology and therefore has contributed to considerably expand the repertoire of suitable coupling partners. While only selected examples will be discussed, this chapter will highlight various dual catalytic platforms focusing on the photocatalytically generated intermediates, but also illustrating the diverse roles of photocatalysts in the context of such synergistic multicatalysis reactions.

A synthesis route to ZnPd, ZnPt and Cu5Zn8 based on a vapor-solid reaction of stoichiometric amounts of the elements is presented. The high vapor pressure of zinc, usually hindering a controlled synthesis, forms part of the developed synthesis strategy with a focus on the catalytically active intermetallic compound ZnPd. The method provides excellent compositional control of better than 0.1 at% and results in dense samples without requiring detailed knowledge of the corresponding thermodynamics. Utilizing the well-defined ZnPd samples, the bulk defect model as well as the reducibility of surface oxides was clarified. The well-controlled synthesis route creates new opportunities for exploring the reaction kinetics and formation processes of intermetallic compounds.

In the present paper, fabrication, characterization and modeling techniques are combined to analyze the microstructure of tape-cast open-porous materials. This kind of material is highly permeable to gases and at the same time has a high catalytic reactivity which leads to successful applications e.g. in high-temperature fuel cells. However, the microstructure of such material is complex and not fully understood. The main goal of this paper is to provide a realistic model of tape-cast porous microstructures based on the simulation of their fabrication process. In order to accomplish this, four different samples are fabricated by firing the green tapes obtained by the tape casting process. The microstructure of each sample is analyzed by means of micro-tomographic image data. Furthermore, a method for modeling open-porous 3D microstructures is proposed, where the cast slurry is represented by a system of spheres with a given volume fraction and radius distribution. Model-based post-processing of the simulated microstructures is proposed to improve the fit of the surface area. Finally, model validation is performed by means of a detailed statistical analysis of experimentally produced and virtual microstructures. Keywords: Open-porous materials, 3D microstructure modeling, Micro-computed tomography, Molten carbonate fuel cell

A versatile framework to create 3D mesoscale models of hot mix asphalt is presented. The mortar phase of a German standard hot mix asphalt is tested in the linear viscoelastic regime using a modified dynamic shear rheometry setup. The widely used generalised Maxwell-model is fit to master-curve data. Virtually any particle size distribution can be matched by a nested Voronoi tessellation algorithm, which is shown by generating geometrical models for three different types of hot mix asphalt. Several techniques to reduce the computational effort are introduced at the geometry and mesh levels. Representative volume elements for mixture SMA 8 S are determined and used to predict the complex shear modulus and loss angle. An analysis of the reinforcing properties of the mineral aggregate phase is presented. The influence of the loading rate on the size of representative volume elements is investigated.

Silica monoliths are hierarchically structured, versatile materials that are widely used in analytical separation science, e.g., liquid chromatography. Their functionality strongly depends on the 3D morphology of their macropore and mesopore spaces. In the present paper, we consider three differently manufactured silica monolith samples, where the process conditions of their hydrothermal treatment (affecting, e.g., mesopore size) have been varied, and present a parametric stochastic 3D microstructure model that is able to generate digital twins of the resulting mesopore spaces. The model, which is based on random point processes and relative neighborhood graphs, theoretically guarantees the complete connectivity of both, the silica phase and the mesopores. The parametric model is fitted to electron tomographic image data. For this purpose, we optimize a cost function that is based on empirically derived relationships between model parameters and volume fraction, mean geodesic tortuosity and constrictivity. Validation is performed regarding further microstructure characteristics, which are not used for model fitting, and regarding effective diffusivity, which is numerically simulated by a particle-tracking algorithm based on random walks.

Samantha Smith came to fame by writing a letter to the Soviet leader, Yuri Andropov, in December 1982, expressing her fears about a potential nuclear war between the two superpowers. Her letter was quoted in the main Soviet newspaper Pravda in April 1983 and the Smith family was subsequently invited to visit the Soviet Union during the summer. Samantha’s trip captured the attention of the world media on both sides of the ideological divide. However, her trip has usually been dismissed as a quirky footnote in Cold War history. Scholarly interest has only just begun, sparked by the growing research into the significance of citizen diplomacy, which saw a real explosion in the late Cold War. This article will demonstrate that Samantha Smith’s trip was a pivotal moment in this broader process because it provided a very public challenge to the strong political discourse of the early Reagan administration, which promoted conflict. Based on an analysis of Soviet and American media reporting, and using hitherto unused archival evidence, as well as oral history interviews, this article examines why and how Samantha Smith became an icon of citizen diplomacy in the early 1980s. Analysing the receptions, representations and legacies of her trip on both sides of the ideological divide, it will shed fresh light on the role of children as soft power during the Cold War and highlight Samantha Smith’s role as a precursor of a rapidly expanding citizen diplomacy that played a largely ignored part in bringing the conflict to an end.

International audience; We compare two conceptually different stochastic microstructure models , i.e., a graph-based model and a pluri-Gaussian model, that have been introduced to model the transport properties of three-phase microstructures occurring, e.g., in solid oxide fuel cell electrodes. Besides comparing both models, we present new results regarding the relationship between model parameters and certain mi-crostructure characteristics. In particular, an analytical expression is obtained for the expected length of triple phase boundary per unit volume in the pluri-Gaussian model. As a case study, we consider 3D image data which show a representative cutout of a solid oxide fuel cell anode obtained by FIB-SEM tomography. The two models are fitted to image data and compared in terms of morphological characteristics (like mean geodesic tortuosity and constrictivity) as well as in terms of effective transport properties. The Stokes flow in the pore phase and effective conductivities in the solid phases are computed numerically for realizations of the two models as well as for the 3D image data using Fourier methods. The local and effective physical responses of the model realizations are compared to those obtained from 3D image data. Finally, we assess the accuracy of the two methods to predict permeability as well as electronic and ionic conductivities of the anode.

Summary Detecting crossovers in cryo‐electron microscopy images of protein fibrils is an important step towards determining the morphological composition of a sample. Currently, the crossover locations are picked by hand, which introduces errors and is a time‐consuming procedure. With the rise of deep learning in computer vision tasks, the automation of such problems has become more and more applicable. However, because of insufficient quality of raw data and missing labels, neural networks alone cannot be applied successfully to target the given problem. Thus, we propose an approach combining conventional computer vision techniques and deep learning to automatically detect fibril crossovers in two‐dimensional cryo‐electron microscopy image data and apply it to murine amyloid protein A fibrils, where we first use direct image processing methods to simplify the image data such that a convolutional neural network can be applied to the remaining segmentation problem. Lay Description The ability of protein to form fibrillary structures underlies important cellular functions but can also give rise to disease, such as in a group of disorders, termed amyloid diseases. These diseases are characterised by the formation of abnormal protein filaments, so‐called amyloid fibrils, that deposit inside the tissue. Many amyloid fibrils are helically twisted, which leads to periodic variations in the apparent width of the fibril, when observing amyloid fibrils using microscopy techniques like cryogenic electron microscopy (cryo‐EM). Due to the two‐dimensional projection, parts of the fibril orthogonal to the projection plane appear narrower than parts parallel to the plane. The parts of small width are called crossovers. The distance between two adjacent crossovers is an important characteristic for the analysis of amyloid fibrils, because it is informative about the fibril morphology and because it can be determined from raw data by eye. A given protein can typically form different fibril morphologies. The morphology can vary depending on the chemical and physical conditions of fibril formation, but even when fibrils are formed under identical solution conditions, different morphologies may be present in a sample. As the crossovers allow to define fibril morphologies in a heterogeneous sample, detecting crossovers is an important first step in the sample analysis. In the present paper, we introduce a method for the automated detection of fibril crossovers in cryo‐EM image data. The data consists of greyscale images, each showing an unknown number of potentially overlapping fibrils. In a first step, techniques from image analysis and pattern detection are employed to detect single fibrils in the raw data. Then, a convolutional neural network is used to find the locations of crossovers on each single fibril. As these predictions may contain errors, further postprocessing steps assess the quality and may slightly alter or reject the predicted crossovers., publishedVersion

We studied the combined effects of pressure (0.1-200 MPa) and temperature (22, 30, and 38 °C) on the catalytic activity of designed amyloid fibrils using a high-pressure stopped-flow system with rapid UV/Vis absorption detection. Complementary FT-IR spectroscopic data revealed a remarkably high pressure and temperature stability of the fibrillar systems. High pressure enhances the esterase activity as a consequence of a negative activation volume at all temperatures (about -14 cm(3) mol(-1) ). The enhancement is sustained in the whole temperature range covered, which allows a further acceleration of the enzymatic activity at high temperatures (activation energy 45-60 kJ mol(-1) ). Our data reveal the great potential of using both pressure and temperature modulation to optimize the enzyme efficiency of catalytic amyloid fibrils.

DTA/TG/MS measurements were used to investigate the temperature-dependent and successive phase formation of different intermetallic In–Pd compounds by controlled reduction of PdO/In2O3 with hydrogen. Reduction procedures were developed to obtain supported intermetallic InPd and In3Pd2 particles by reactive metal–support interaction (RMSI) without detectable amounts of other compounds. In7Pd3 could only be obtained in admixture with elemental indium due to the direct reduction of the In2O3 support at temperatures above 350 °C. All materials exhibit catalytic activity for methanol steam reforming and exhibit high CO2 selectivities up to 98%. Long-term measurements proved the superior stability of the In–Pd/In2O3 materials in comparison with Cu-based systems over 100 h times on stream with high selectivity.