Qingxuan Wang, Xin Qian, Tsuneyoshi Nakayama, Jun Liu, Jun Zhou
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引用次数: 0
摘要
了解二维有机-无机杂化钙钛矿(2D-HOIPs)的热输运对于开发具有超低导热性能的功能材料至关重要。在这项工作中,我们采用热阻网络模型来计算2D-HOIPs的导热系数,并揭示了一种有效的分子设计策略来降低其导热系数。我们的研究结果表明,用支链有机阳离子取代线性有机阳离子可以显著降低热导率,使其比线性有机阳离子低四倍,低至0.07 W m$^{-1}$ K$^{-1} $。该研究通过分子工程为2D-HOIPs的热输运定制提供了新的见解,为热管理和能量转换应用提供了一种有前途的方法。
Theoretical insights into thermal conductivity of 2D hybrid perovskites based on thermal resistance network model
Understanding thermal transport in two-dimensional organic-inorganic hybrid perovskites (2D-HOIPs) is crucial for developing functional materials with ultralow thermal conductivity. In this work, we employ a thermal resistance network model to calculate the thermal conductivity of 2D-HOIPs and reveal an effective molecular design strategy for its reduction. Our findings demonstrate that replacing linear organic cations with branched ones significantly reduces thermal conductivity, making it up to four times lower than its linear counterparts, reaching as low as 0.07 W m$^{-1}$ K$^{-1} $. This study provides new insights into the tailoring of thermal transport in 2D-HOIPs through molecular engineering, offering a promising approach for thermal management and energy conversion applications.
期刊介绍:
Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions.
The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.