The development of effective electrode materials is essential to realizing high- performance lithium ion batteries (LIBs), which play an important role in powering modern society. By means of first-principles calculations, we have herein explored the potential of two-dimensional (2D) polymers made of carbonyl-bridged triphenylamine (CTPA) and carbonyl-bridged triphenylborane (CTPB) as electrode materials for LIBs. Our investigations demonstrate that the carbonyl groups of 2D CTPB and CTPA are rather active to accommodate Li. Both 2D CTPA and CTPB show the transition from semiconductor to metal after combining with Li. The migration of Li through the pore space of 2D CTPB and CTPA is facilitated with the diffusion barrier of 0.76 and 0.79 eV, respectively. 2D CTPB exhibits a high theoretical capacity of 760.86 mAh g-1 because it can accommodate Li at both the carbonyl sites and the surface sites of the skeleton, which is ascribed to the promotion of the electron-deficient B center. As a comparison, 2D CTPA can only combine with Li at the carbonyl sites and shows a capacity of 251.09 mAh g-1. With fast Li-diffusion ability, high capacity and low average operating voltage, 2D CTPA and CTPB are predicted to be promising non-metal anode materials for LIBs.
Metal ion batteries (MIBs), represented by lithium ion batteries are important energy storage devices for storing renewable energy. Advanced development of MIBs depends on the exploration of efficient and sustainable electrode materials. Organic electrode materials (OEMs) with redox‐active moieties are low‐cost and eco‐friendly alternatives to conventional inorganic electrode materials for MIBs. Computational simulation plays an important role in understanding the energy storage mechanism of different active functional groups and boosting the discovery of new OEMs for high‐efficient MIBs. Here, we will review recent progress of OEMs and comprehensively survey factors that determine their electrochemical properties. Dependable computational methods to guide the design of OEMs are comprehensively discussed and machine learning is highlighted as an emerging method to reveal the underlying structure–performance relationship and facilitate screening of OEMs with high‐efficiency. Finally, we summarize the available molecular design strategies to effectively improve the redox activity and stability of OEMs, and discuss challenges and opportunities of theoretical calculations of OEMs for MIBs.This article is categorized under: Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Density Functional Theory Structure and Mechanism > Molecular Structures
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