Liang Chenxing, Aluru Narayana R
Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States.
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, United States.
ACS Nano. 2024 Jun 25;18(25):16141-16150. doi: 10.1021/acsnano.4c00733. Epub 2024 Jun 10.
Foundations of nanofluidics can enable advances in diverse applications such as water desalination, energy harvesting, and biological analysis. Dynamically manipulating nanofluidic properties, such as diffusion and friction, is an area of great scientific interest. Twisted bilayer graphene, particularly at the magic angle, has garnered attention for its unconventional superconductivity and correlated insulator behavior due to strong electronic correlations. The impact of the electronic properties of moiré patterns in twisted bilayer graphene on structural and dynamic properties of water remains largely unexplored. Computational challenges, stemming from simulating large unit cells using density functional theory, have hindered progress. This study addresses this gap by investigating water behavior on twisted bilayer graphene, employing a deep neural network potential (DP) model trained with a data set from molecular dynamics simulations. It is found that as the twisted angle approaches the magic angle, interfacial water friction increases, leading to a reduced water diffusion. Notably, the analysis shows that at smaller twisted angles with larger moiré patterns, water is more likely to reside in AA stacking regions than AB (or BA) stacking regions, a distinction that diminishes with smaller moiré patterns. This study illustrates the potential for leveraging the distinctive properties of moiré systems to effectively control and optimize interfacial fluid behavior.
纳米流体学的基础能够推动水净化、能量收集和生物分析等各种应用的发展。动态操纵纳米流体特性，如扩散和摩擦，是一个具有重大科学意义的领域。扭曲双层石墨烯，特别是在魔角时，因其强电子关联导致的非常规超导性和相关绝缘体行为而备受关注。扭曲双层石墨烯中莫尔条纹的电子性质对水的结构和动态性质的影响在很大程度上仍未得到探索。使用密度泛函理论模拟大晶胞所带来的计算挑战阻碍了研究进展。本研究通过采用基于分子动力学模拟数据集训练的深度神经网络势（DP）模型，研究扭曲双层石墨烯上的水行为，填补了这一空白。研究发现，随着扭曲角接近魔角时，界面水摩擦增加，导致水扩散减少。值得注意的是，分析表明，在具有较大莫尔条纹的较小扭曲角下，水更有可能存在于AA堆积区域而非AB（或BA）堆积区域，随着莫尔条纹变小，这种差异会减小。本研究说明了利用莫尔系统独特性质有效控制和优化界面流体行为的潜力。
