Chord‐length and free‐path distribution functions for many‐body systems

3D imaging of SARS-CoV-2 infected hamster lungs by X-ray phase contrast tomography enables drug testing

X-ray Phase Contrast Tomography (XPCT) based on wavefield propagation has been established as a high resolution three-dimensional (3D) imaging modality, suitable to reconstruct the intricate structure of soft tissues, and the corresponding pathological alterations. However, for biomedical research, more is needed than 3D visualisation and rendering of the cytoarchitecture in a few selected cases. First, the throughput needs to be increased to cover a statistically relevant number of samples. Second, the cytoarchitecture has to be quantified in terms of morphometric parameters, independent of visual impression. Third, dimensionality reduction and classification are required for identification of effects and interpretation of results. To address these challenges, we here design and implement a novel integrated and high throughput XPCT imaging and analysis workflow for 3D histology, pathohistology and drug testing. Our approach uses semi-automated data acquisition, reconstruction and statistical quantification. We demonstrate its capability for the example of lung pathohistology in Covid-19. Using a small animal model, different Covid-19 drug candidates are administered after infection and tested in view of restoration of the physiological cytoarchitecture, specifically the alveolar morphology. To this end, we then use morphometric parameter determination followed by a dimensionality reduction and classification based on optimal transport. This approach allows efficient discrimination between physiological and pathological lung structure, thereby providing quantitative insights into the pathological progression and partial recovery due to drug treatment. Finally, we stress that the XPCT image chain implemented here only used synchrotron radiation for validation, while the data used for analysis was recorded with laboratory μ CT radiation, more easily accessible for pre-clinical research.

Nanoporous Gold: From Structure Evolution to Functional Properties in Catalysis and Electrochemistry

Nanoporous gold (NPG) is characterized by a bicontinuous network of nanometer-sized metallic struts and interconnected pores formed spontaneously by oxidative dissolution of the less noble element from gold alloys. The resulting material exhibits decent catalytic activity for low-temperature, aerobic total as well as partial oxidation reactions, the oxidative coupling of methanol to methyl formate being the prototypical example. This review not only provides a critical discussion of ways to tune the morphology and composition of this material and its implication for catalysis and electrocatalysis, but will also exemplarily review the current mechanistic understanding of the partial oxidation of methanol using information from quantum chemical studies, model studies on single-crystal surfaces, gas phase catalysis, aerobic liquid phase oxidation, and electrocatalysis. In this respect, a particular focus will be on mechanistic aspects not well understood, yet. Apart from the mechanistic aspects of catalysis, best practice examples with respect to material preparation and characterization will be discussed. These can improve the reproducibility of the materials property such as the catalytic activity and selectivity as well as the scope of reactions being identified as the main challenges for a broader application of NPG in target-oriented organic synthesis.

Photochromism from wavelength-selective colloidal phase segregation

Phase segregation is ubiquitously observed in immiscible mixtures, such as oil and water, in which the mixing entropy is overcome by the segregation enthalpy1-3. In monodispersed colloidal systems, however, the colloidal-colloidal interactions are usually non-specific and short-ranged, which leads to negligible segregation enthalpy4. The recently developed photoactive colloidal particles show long-range phoretic interactions, which can be readily tuned with incident light, suggesting an ideal model for studying phase behaviour and structure evolution kinetics5,6. In this work, we design a simple spectral selective active colloidal system, in which TiO2 colloidal species were coded with spectral distinctive dyes to form a photochromic colloidal swarm. In this system, the particle-particle interactions can be programmed by combining incident light with various wavelengths and intensities to enable controllable colloidal gelation and segregation. Furthermore, by mixing the cyan, magenta and yellow colloids, a dynamic photochromic colloidal swarm is formulated. On illumination of coloured light, the colloidal swarm adapts the appearance of incident light due to layered phase segregation, presenting a facile approach towards coloured electronic paper and self-powered optical camouflage.

Quantitative 3D Characterization of Nanoporous Gold Nanoparticles by Transmission Electron Microscopy

Quantitative structural characterization of nanomaterials is important to tailor their functional properties. Corrosion of AgAu-alloy nanoparticles (NPs) results in porous structures, making them interesting for applications especially in the fields of catalysis and surface-enhanced Raman spectroscopy. For the present report, structures of dealloyed NPs were reconstructed three-dimensionally using scanning transmission electron microscopy tomography. These reconstructions were evaluated quantitatively, revealing structural information such as pore size, porosity, specific surface area, and tortuosity. Results show significant differences compared to the structure of dealloyed bulk samples and can be used as input for simulations of diffusion or mass transport processes, for example, in catalytic applications.

Connecting theory and simulation with experiment for the study of diffusion in nanoporous solids

Nanoporous solids are ubiquitous in chemical, energy, and environmental processes, where controlled transport of molecules through the pores plays a crucial role. They are used as sorbents, chromatographic or membrane materials for separations, and as catalysts and catalyst supports. Defined as materials where confinement effects lead to substantial deviations from bulk diffusion, nanoporous materials include crystalline microporous zeotypes and metal–organic frameworks (MOFs), and a number of semi-crystalline and amorphous mesoporous solids, as well as hierarchically structured materials, containing both nanopores and wider meso- or macropores to facilitate transport over macroscopic distances. The ranges of pore sizes, shapes, and topologies spanned by these materials represent a considerable challenge for predicting molecular diffusivities, but fundamental understanding also provides an opportunity to guide the design of new nanoporous materials to increase the performance of transport limited processes. Remarkable progress in synthesis increasingly allows these designs to be put into practice. Molecular simulation techniques have been used in conjunction with experimental measurements to examine in detail the fundamental diffusion processes within nanoporous solids, to provide insight into the free energy landscape navigated by adsorbates, and to better understand nano-confinement effects. Pore network models, discrete particle models and synthesis-mimicking atomistic models allow to tackle diffusion in mesoporous and hierarchically structured porous materials, where multiscale approaches benefit from ever cheaper parallel computing and higher resolution imaging. Here, we discuss synergistic combinations of simulation and experiment to showcase theoretical progress and computational techniques that have been successful in predicting guest diffusion and providing insights. We also outline where new fundamental developments and experimental techniques are needed to enable more accurate predictions for complex systems.

Effect of capillary condensation on gas transport properties in porous media

We investigate the effect of capillary condensation on gas diffusivity in porous media composed of randomly packed spheres with moderate wettability. To simulate capillary phenomena at the pore scale while retaining complex pore networks of the porous media, we employ density functional theory (DFT) for coarse-grained lattice gas models. The lattice DFT simulations reveal that capillary condensations preferentially occur at confined pores surrounded by solid walls, leading to the occlusion of narrow pores. Consequently, the characteristic lengths of the partially wet structures are larger than those of the corresponding dry structures with the same porosities. Subsequent gas diffusion simulations exploiting the mean-square displacement method indicate that while the effective diffusion coefficients significantly decrease in the presence of partially condensed liquids, they are larger than those in the dry structures with the same porosities. Moreover, we find that the ratio of the porosity to the tortuosity factor, which is a crucial parameter that determines an effective diffusion coefficient, can be reasonably related to the porosity even for the partially wet porous media.

Determination of the effective gas diffusivity of a porous composite medium from the three-dimensional reconstruction of its microstructure

The present work describes a procedure to calculate the effective diffusivity of a porous composite medium from the three-dimensional reconstruction of its microstructure. We perform Monte Carlo simulations based on the mean-square displacement method on numerical models of composite materials microstructures. First, computations of the effective diffusivity in the bulk diffusion regime account for the effect of the tortuosity of the geometry on gas diffusion. The Bruggeman equation, which is often used in the literature to relate the effective diffusivity to the porosity of the structure, appears to be inaccurate for porosities ε<0.40. A more accurate correlation for this range of porosities is provided based on the results of our simulations. Second, the Bosanquet equation, which accounts for the effect of pore confinement on gas diffusion, is validated provided that the definition of the Knudsen number is based on the appropriate characteristic length. The procedure to calculate this characteristic length is demonstrated for analytical geometries. However, in practice, geometries obtained from experimental measurements are discrete. For discrete geometries, we show the effect of the resolution of the geometry on the accuracy of the calculation of the effective diffusivity and other properties of the porous material. In addition, the tesselation of solid surfaces affects the calculation of the chord-length distribution regardless of the resolution. This hinders the accurate estimation of the characteristic length necessary to compute the Knudsen number and the effective diffusivity.

Random-walk approach to the d-dimensional disordered Lorentz gas

A correlated random walk approach to diffusion is applied to the disordered nonoverlapping Lorentz gas. By invoking the Lu-Torquato theory for chord-length distributions in random media [J. Chem. Phys. 98, 6472 (1993)], an analytic expression for the diffusion constant in arbitrary number of dimensions d is obtained. The result corresponds to an Enskog-like correction to the Boltzmann prediction, being exact in the dilute limit, and better or nearly exact in comparison to renormalized kinetic theory predictions for all allowed densities in d=2,3 . Extensive numerical simulations were also performed to elucidate the role of the approximations involved.

Heterogeneity of colloidal particle networks analyzed by means of Minkowski functionals

The heterogeneity and large scale connectivity of colloidal particle networks, which are generated by Brownian dynamics simulations, is examined. This is achieved by employing integral geometric measures in the form of the Minkowski functionals or quermassintegrals. It is found that these measures in conjunction with the parallel-body technique amount to a powerful tool to characterize the structure, going beyond the information contained in the pair-correlation function. The development of heterogeneities during network formation as well as their dependence on the volume fraction and the interaction potential is studied. In particular, it is found that slow coagulation enhances the heterogeneity of the network compared to fast coagulation.