MXenes for advanced energy storage: A computational perspective

Le résumé fourni par la source

The urgent demand for sustainable energy solutions has accelerated the development of rechargeable batteries, where the design of high-performance electrode materials plays a pivotal role. Since their discovery in 2011, two-dimensional (2D) transition metal carbides and nitrides, known as MXenes, have garnered significant attention owing to their excellent electrical conductivity, high mechanical strength, tailorable surface chemistry, and large specific surface area with abundant active sites. These unique properties render MXenes highly promising for electrochemical energy storage, particularly in lithium−ion, lithium−sulfur, and lithium−oxygen batteries, as well as emerging non-lithium−ion battery systems. However, their practical implementation is hindered by critical challenges including structural restacking, poor stability, and unfavorable surface terminations. In this review, we focus on a computational perspective for the rational design and performance optimization of MXene-based electrodes. Density functional theory (DFT) calculations have yielded fundamental insights into the intrinsic properties, modification mechanisms, and electrochemical behaviors of MXenes. Additionally, machine learning has enabled high-throughput screening and predictive modeling of their structure−property relationships. More importantly, this review not only summarizes the theoretical advances in MXenes for energy storage applications but also extracts actionable theoretical insights and performance prediction principles. These efforts provide a comprehensive theoretical reference for computational studies on MXenes, allowing researchers to efficiently grasp the research landscape, core mechanisms, and modification strategies. Furthermore, this review offers targeted guidance for experimental investigations by facilitating the optimization of modification schemes, the selection of suitable battery systems, and the reduction of trial-and-error in experimental design.

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