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Magnesium batteries have been considered promising candidates for next‐generation energy storage systems owing to their high energy density, good safety without dendrite formation, and low cost of magnesium resources. However, high‐performance cathodes with stable capacity, good conductivity, and fast ions transport are needed, since many conventional cathodes possess a low performance and poor preparation controllability. Herein, a liquid‐driven coaxial flow focusing (LDCFF) approach for preparing a novel microcapsule system with controllable size, high loading, and stable magnesium‐storage performance is presented. Taking the MoS2‐infilled microcapsule as a case study, the magnesium battery cathode based on the microcapsules displays a capacity of 100 mAh g−1 after 100 cycles. High capacity retention is achieved at both low and high temperatures of −10, ‒5, and 45 °C, and a stable rate‐performance is also obtained. The influences of the liquid flow rates on the size and shell thickness of the microcapsules are investigated; and electron and ion diffusion properties are also studied by first‐principle calculations. The presented LDCFF method is quite general, and the high performance of the microcapsules enables them to find broad applications for making emerging energy‐storage materials and secondary battery systems.

Accurately evaluating the adsorption ability of adsorbents for heavy metal ions (HMIs) and organic pollutants in water is critical for the design and preparation of emerging highly efficient adsorbents. However, predicting adsorption capabilities of adsorbents at arbitrary sites is challenging, with currently unavailable measuring technology for active sites and the corresponding activities. Here, we present an efficient artificial intelligence (AI) approach to predict the adsorption ability of adsorbents at arbitrary sites, as a case study of three HMIs (Pb(II), Hg(II), and Cd(II)) adsorbed on the surface of a representative two-dimensional graphitic-C3N4. We apply the deep neural network and transfer learning to predict the adsorption capabilities of three HMIs at arbitrary sites, with the predicted results of Cd(II) > Hg(II) > Pb(II) and the root-mean-squared errors less than 0.1 eV. The proposed AI method has the same prediction accuracy as the ab initio DFT calculation, but is millions of times faster than the DFT to predict adsorption abilities at arbitrary sites and only requires one-tenth of datasets compared to training from scratch. We further verify the adsorption capacity of g-C3N4 towards HMIs experimentally and obtain results consistent with the AI prediction. It indicates that the presented approach is capable of evaluating the adsorption ability of adsorbents efficiently, and can be further extended to other interdisciplines and industries for the adsorption of harmful elements in aqueous solution.

High performance aluminum-ion (Al-ion) batteries are widely concerned owning to high theoretical capacity, abundance of Al metal and good safety. Here, we develop a hierarchical VS2@VS4 composing of VS4 nanorod...

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