Novel hierarchical structure of MoS2/TiO2/Ti3C2Tx composites for dramatically enhanced electromagnetic absorbing properties

In order to prevent the microwave leakage and mutual interference, more and more microwave absorbing devices are added into the design of electronic products to ensure its routine operation. In this work, we have successfully prepared MoS2/TiO2/Ti3C2Tx hierarchical composites by one-pot hydrothermal method and focused on the relationship between structures and electromagnetic absorbing properties. Supported by comprehensive characterizations, MoS2 nanosheets were proved to be anchored on the surface and interlayer of Ti3C2Tx through a hydrothermal process. Additionally, TiO2 nanoparticles were obtained in situ. Due to these hierarchical structures, the MoS2/TiO2/Ti3C2Tx composites showed greatly enhanced microwave absorbing performance. The MoS2/TiO2/Ti3C2Tx composites exhibit a maximum reflection loss value of −33.5 dB at 10.24 GHz and the effective absorption bandwidth covers 3.1 GHz (13.9–17 GHz) at the thickness of 1.0 mm, implying the features of wide frequency and light weight. This work in the hierarchical structure of MoS2/TiO2/Ti3C2Tx composites opens a promising door to the exploration of constructing extraordinary electromagnetic wave absorbents.

Deep learning model to predict complex stress and strain fields in hierarchical composites

Materials-by-design is a paradigm to develop previously unknown high-performance materials. However, finding materials with superior properties is often computationally or experimentally intractable because of the astronomical number of combinations in design space. Here we report an AI-based approach, implemented in a game theory–based conditional generative adversarial neural network (cGAN), to bridge the gap between a material’s microstructure—the design space—and physical performance. Our end-to-end deep learning model predicts physical fields like stress or strain directly from the material microstructure geometry, and reaches an astonishing accuracy not only for predicted field data but also for derivative material property predictions. Furthermore, the proposed approach offers extensibility by predicting complex materials behavior regardless of component shapes, boundary conditions, and geometrical hierarchy, providing perspectives of performing physical modeling and simulations. The method vastly improves the efficiency of evaluating physical properties of hierarchical materials directly from the geometry of its structural makeup.