In the last decade organocatalysis has developed into an essential third branch of asymmetric catalysis that now complements the fields of metal and enzyme catalysis and provides widely applicable methods for efficient organic synthesis. [1, 2] Especially the combination and integration in cooperative catalysis such as domino reactions [3] and the recent efforts in combining organocatalysis with metal activation [4] demonstrate that the potential of organocatalysis for the development of new activation modes in selective organic synthesis is still not fully uncovered. Moreover, photocatalysis with visible light [5] is undoubtedly one of the emerging strategies to meet the increasing demand for more sustainable chemical processes. Building on seminal results employing photoinduced electron-transfer processes, [6] which often required UV light, a number of powerful methods have been developed recently applying organometallic complexes such as [Ru-(bpy) 3 ] 2+ and [Ir(ppy) 2 (dtb-bpy)]. [5,7] Of particular note is the cooperative combination of photocatalysis with an organocatalytic cycle [8] offering one of the rare catalytic methods for the enantioselective a-alkylation of aldehydes. [9,10] However, the high cost and potential toxicity of the ruthenium and iridium salts as well as their limited availability in the future are disadvantages of these metal-based methods. Stimulated by the attractiveness of using green light, the most abundant part of solar light, we speculated that a number of red to orange dyes could also be used successfully in photoredox catalysis, and the choice of appropriate reaction conditions would additionally allow for the cooperative merging with asymmetric organocatalysis.Herein, we present a versatile metal-free, purely organic photoredox catalysis with visible light. As a first example of our strategy we demonstrate the successful application of simple, inexpensive organic dyes as effective photocatalysts for the cooperative organocatalytic asymmetric intermolecular a-alkylation of aldehydes. [11] Initial studies began with the screening of a number of red and orange dyes (Scheme 1) for the photocatalytic reductive dehalogenation of a-bromoacetophenone (E 0 = À0.49 V vs. SCE) [12] as a test reaction (Table 1). [6c, 13] Following the observation that classic organic dyes show striking similarities to the widely employed organometallic ruthenium-and iridium-containing photosensitizers, we chose our test candidates based on their l max , their redox potential E 0 , and their precedented use as photosensitizers for semiconductor-based photocatalysis or dye solar cells. [14,15] To achieve this desired transformation we investigated the conditions reported by Stephenson and co-workers for the photocatalytic dehalogenation of activated benzylic halides in the presence of [Ru(bpy) 3 ] 2+ . In accordance with their results Scheme 1. Absorption and redox properties of red and orange organic dyes used as photoredox catalysts (l max (CH 3 CN) in nm; 3 in CH 2 Cl 2 ; E 0 (dye/dyeC À ) ...

Empirical relationships between effective conductivities in porous and composite materials and their geometric characteristics such as volume fraction e, tortuosity s and constrictivity b are established. (simplified formula) with intrinsic conductivity r 0 , geodesic tortuosity s geod and relative prediction errors of 19% and 18%, respectively. We critically analyze the methodologies used to determine tortuosity and constrictivity. Comparing geometric tortuosity and geodesic tortuosity, our results indicate that geometric tortuosity has a tendency to overestimate the windedness of transport paths. Analyzing various definitions of constrictivity, we find that the established definition describes the effect of bottlenecks well. In summary, the established relationships are important for a purposeful optimization of materials with specific transport properties, such as porous electrodes in fuel cells and batteries.

The microstructure influence on conductive transport processes is described in terms of volume fraction ε, tortuosity τ, and constrictivity β. Virtual microstructures with different parameter constellations are produced using methods from stochastic geometry. Effective conductivities σeff  are obtained from solving the diffusion equation in a finite element model. In this way, a large database is generated which is used to test expressions describing different micro–macro relationships such as Archie's law, tortuosity, and constrictivity equations. It turns out that the constrictivity equation has the highest accuracy indicating that all three parameters (ε,τ,β) are necessary to capture the microstructure influence correctly. The predictive capability of the constrictivity equation is improved by introducing modifications of it and using error‐minimization, which leads to the following expression: σeff =σ02.03ε1.57β0.72/τ2 with intrinsic conductivity σ0. The equation is important for future studies in, for example, batteries, fuel cells, and for transport processes in porous materials. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1983–1999, 2014

Dream team: Heterogeneous inorganic semiconductors and chiral organocatalysts team up for the stereoselective photocatalytic formation of carbon–carbon bonds. However, the connection between the organic and inorganic catalysts should not be too tight: Covalent immobilization inactivates the system.

Polymorphism is a key feature of amyloid fibril structures but it remains challenging to explain these variations for a particular sample. Here, we report electron cryomicroscopy-based reconstructions from different fibril morphologies formed by a peptide fragment from an amyloidogenic immunoglobulin light chain. The observed fibril morphologies vary in the number and cross-sectional arrangement of a structurally conserved building block. A comparison with the theoretically possible constellations reveals the experimentally observed spectrum of fibril morphologies to be governed by opposing sets of forces that primarily arise from the β-sheet twist, as well as peptide–peptide interactions within the fibril cross-section. Our results provide a framework for rationalizing and predicting the structure and polymorphism of cross-β fibrils, and suggest that a small number of physical parameters control the observed fibril architectures.

Controlled synthesis of supported intermetallic In-Pd compounds. High selectivity of In-Pd compounds in the steam reforming of methanol. Long-term measurements prooveprove the stability of In-Pd/In 2 O 3 in MSR. KEYWORDS InPd, In 3 Pd 2 , In 7 Pd , In 2 O 3 , MSR, CO 2 selective, methanol steam reforming ABSTRACT 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/In 2 O 3 with hydrogen. Reduction procedures were developed to obtain supported intermetallic InPd and In 3 Pd 2 particles by reactive metal-support interaction (RMSI) without detectable amounts of other compounds. In 7 Pd 3 could only be obtained in admixture with elemental indium due to the direct reduction of the In 2 O 3 support at temperatures above 350 °C. All materials exhibit catalytic activity for methanol steam reforming and exhibit high CO 2 selectivities up to 98%. Long-term measurements proved the superior stability of the In-Pd/In 2 O 3 materials in comparison to Cu-based systems over 100 hours time on stream with high selectivity.

Applications of microflow conditions for visible light photoredox catalysis have successfully been developed. Operationally simple microreactor and FEP (fluorinated ethylene propylene copolymer) tube reactor systems enable significant improvement of several photoredox reactions using different photocatalysts such as [Ru(bpy)(3)](2+) and Eosin Y. Apart from rate acceleration, this approach facilitates previously challenging transformations of nonstabilized intermediates. Additionally, the productivity of the synergistic, catalytic enantioselective photoredox α-alkylation of aldehydes was demonstrated to be increased by 2 orders of magnitude.

In this paper, various kinds of applications are presented, in which tomographic image data depicting microstructures of materials are semantically segmented by combining machine learning methods and conventional image processing steps. The main focus of this paper is the grain-wise segmentation of time-resolved CT data of an AlCu specimen which was obtained in between several Ostwald ripening steps. The poorly visible grain boundaries in 3D CT data were enhanced using convolutional neural networks (CNNs). The CNN architectures considered in this paper are a 2D U-Net, a multichannel 2D U-Net and a 3D U-Net where the latter was trained at a lower resolution due to memory limitations. For training the CNNs, ground truth information was derived from 3D X-ray diffraction (3DXRD) measurements. The grain boundary images enhanced by the CNNs were then segmented using a marker-based watershed algorithm with an additional postprocessing step for reducing oversegmentation. The segmentation results obtained by this procedure were quantitatively compared to ground truth information derived by the 3DXRD measurements. A quantitative comparison between segmentation results indicates that the 3D U-Net performs best among the considered U-Net architectures. Additionally, a scenario, in which "ground truth" data is only available in one time step, is considered. Therefore, a CNN was trained only with CT and 3DXRD data from the last measured time step. The trained network and the image processing steps were then applied to the entire series of CT scans. The resulting segmentations exhibited a similar quality compared to those obtained by the network which was trained with the entire series of CT scans.