Exploring Thermal Transport in Electrochemical Energy Storage Systems Utilizing Two-Dimensional Materials: Prospects and Hurdles
Exploring Thermal Transport in Electrochemical Energy Storage Systems Utilizing Two-Dimensional Materials: Prospects and Hurdles
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DOI：
10.1615/annualrevheattransfer.2023049365
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Dibakar Datta;Eon Soo Lee
中科院分区：
文献类型：
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作者：
Dibakar Datta;Eon Soo Lee
二维材料及其异质结构在电化学储能系统（EESS）如电池中有着巨大的应用。对这些材料的热传输和机理的全面而扎实的理解对于EESS的实际设计至关重要。实验在提供复杂结构的改进控制和表征方面具有挑战性，特别是对于低维材料。理论和模拟工具，如第一性原理计算，玻尔兹曼输运方程，分子动力学模拟，晶格动力学模拟和非平衡绿色函数提供了可靠的预测热导率和物理见解，以了解材料中的基本热输运机制。然而，进行这些计算需要高计算资源。新材料合成技术的发展和快速准确预测物理性能的快速增长的需求需要新的计算方法。机器学习（ML）方法提供了一个很有前途的解决方案来满足这些需求。本文详细介绍了EESS中热输运的原子/分子研究和ML的最新进展。本文还讨论了最新的重大实验进展。然而，设计最佳的低维材料基异质结构就像一个多变量优化问题。例如，特定的异质结构可以适合于热传输，但是可以具有较低的机械强度/稳定性。对于双层/多层结构，层间距离可能影响热输运性质和层间强度。最后一部分展望了EESS中基于低维材料的异质结构热输运设计的未来研究方向。
Two-dimensional materials and their heterostructures have enormous applications in Electrochemical Energy Storage Systems (EESS) such as batteries. A comprehensive and solid understanding of these materials' thermal transport and mechanism is essential for the practical design of EESS. Experiments have challenges in providing improved control and characterization of complex structures, especially for low dimensional materials. Theoretical and simulation tools such as first-principles calculations, boltzmann transport equations, molecular dynamics simulations, lattice dynamics simulation, and non-equilibrium Green's function provide reliable predictions of thermal conductivity and physical insights to understand the underlying thermal transport mechanism in materials. However, doing these calculations require high computational resources. The development of new materials synthesis technology and fast-growing demand for rapid and accurate prediction of physical properties require novel computational approaches. The machine learning (ML) method provides a promising solution to address such needs. This review details the recent development in atomistic/molecular studies and ML of thermal transport in EESS. The paper also addresses the latest significant experimental advances. However, designing the best low-dimensional materials-based heterostructures is like a multivariate optimization problem. For example, a particular heterostructure may be suitable for thermal transport but can have lower mechanical strength/stability. For bi/multilayer structures, the interlayer distance may influence the thermal transport properties and interlayer strength. Therefore, the last part addresses the future research direction in low-dimensional materials-based heterostructure design for thermal transport in EESS.