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Pore structures provide extra freedoms for the design of porous media, leading to desirable properties, such as high catalytic rate, energy storage efficiency, and specific strength. This unfortunately makes the porous media susceptible to failure. Deep understanding of the failure mechanism in microstructures is a key to customizing high-performance crack-resistant porous media. However, solving the fracture problem of the porous materials is computationally intractable due to the highly complicated configurations of microstructures. To bridge the structural configurations and fracture responses of random porous media, a unique generative deep learning model is developed. A two-step strategy is proposed to deconstruct the fracture process, which sequentially corresponds to elastic deformation and crack propagation. The geometry of microstructure is translated into a scalar of elastic field as an intermediate variable, and then, the crack path is predicted. The neural network precisely characterizes the strong interactions among pore structures, the multiscale behaviors of fracture, and the discontinuous essence of crack propagation. Crack paths in random porous media are accurately predicted by simply inputting the images of targets, without inputting any additional input physical information. The prediction model enjoys an outstanding performance with a prediction accuracy of 90.25% and possesses a robust generalization capability. The accuracy of the present model is a record so far, and the prediction is accomplished within a second. This study opens an avenue to high-throughput evaluation of the fracture behaviors of heterogeneous materials with complex geometries., Competing Interests: Competing interests statement:The authors declare no competing interest.

Cu-based catalysts are highly active and selective for several CO 2 conversion reactions; however, traditional monometallic Cu-based catalysts suffer poor thermal stability due to the aggregation of copper particles at high temperatures. In this work, we demonstrate a crystal engineering strategy to controllably prepare copper/silica (CuO x /SiO 2 ) catalysts for the reverse water gas shift reaction (RWGS) at high temperatures. We show that CuO x /SiO 2 catalysts derived from the in situ reduction of pure copper silicate nanotubes in a CO 2 and H 2 atmosphere exhibit superior catalytic activity with enhanced stability compared to traditional monometallic Cu-based catalysts for the RWGS at high temperatures. Detailed structural characterization reveals that there is a strong interaction between Cu and SiO 2 in CuO x /SiO 2 catalysts, which produces more Cu + sites and smaller CuO x nanoparticles. Moreover, CuO x /SiO 2 catalysts possess a unique dot core/rod shell structure, which could prevent the aggregation of Cu particles. This structural confinement effect, enhanced CO 2 adsorption by Cu + , and small CuO x nanoparticles presumably caused the catalyst's extraordinary activity with enhanced stability at high temperatures.

Single-atom noble metals on a catalyst support tend to migrate and agglomerate into nanoparticles owing to high surface free energy at elevated temperatures. Temperature-induced structure reconstruction of a support can firmly anchor single-atom Pt species to adapt to a high-temperature environment. We used Mn 3 O 4 as a restructurable support to load single-atom Pt and further turned into single-atom Pt-on-Mn 2 O 3 catalyst via high-temperature treatment, which is extremely stable under calcination conditions of 800 °C for 5 days in humid air. High-valence Pt 4+ with more covalent bonds on Mn 2 O 3 are essential for anchoring isolated Pt atoms by strong interaction. An optimized catalyst formed by moderate H 2 O 2 etching exhibits the best performance and excellent thermal stability of single-atom Pt in high-temperature CH 4 oxidation on account of more exposed Pt atoms and strong Pt-Mn 2 O 3 interaction., (© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Oxygen evolution reaction (OER) is a key reaction for many renewable energy storage and conversion techniques. Developing efficient non-precious metal-based electrocatalysts for OER has attracted increasing attention. Herein is reported a strategy to fabricate hierarchical cobalt borate/Ti 3 C 2 T x MXene (Co-B i /Ti 3 C 2 T x ) hybrid through fast chemical reactions at room temperature. This interesting hierarchical structure of Co-B i /Ti 3 C 2 T x hybrid is beneficial for exposing more active sites, improving mass diffusion, and charge-transfer pathways for electrochemical reaction. Moreover, a strong interaction between Co-B i and Ti 3 C 2 T x ensures efficient charge transfer and facilitates the electrostatic attraction of more anionic intermediates for a fast redox process. Consequently, the hierarchical Co-B i /Ti 3 C 2 T x hybrid shows extraordinary OER catalytic activity to deliver a current density of 10 mA cm -2 at an overpotential of 250 mV, and a Tafel slope of about 53 mV dec -1 ., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)