通过莫尔扭转调节界面水摩擦
Tuning Interfacial Water Friction through Moiré Twist
AI总结：
该研究使用基于从头算分子动力学数据训练的DP深度神经网络势，模拟扭转双层石墨烯上的界面水，揭示莫尔扭转角接近魔角时摩擦增大、扩散降低及AA/AB堆叠区域占据差异。
摘要：
纳米流体学在基础研究以及从海水淡化、能量收集到生物分析的多种应用中都具有关键作用。动态调控扩散和摩擦等纳米流体性质，为该领域的发展提供了一条途径。扭转双层石墨烯，尤其在魔角时，因其由强电子关联导致的非常规超导性和关联绝缘体行为而受到关注。然而，扭转双层石墨烯中莫尔图案的电子性质对水的结构和动力学性质的影响仍基本未被探索。采用密度泛函理论模拟大尺寸单胞所带来的计算挑战阻碍了相关进展。本研究通过研究扭转双层石墨烯上的水行为来填补这一空白，所采用的深度神经网络势（DP）模型利用从头算分子动力学模拟得到的数据集进行训练。研究发现，随着扭转角接近魔角，界面水摩擦增加，导致水的扩散减弱。值得注意的是，分析表明，在具有更大莫尔图案的较小扭转角下，水更倾向于停留在AA堆叠区域，而非AB（或BA）堆叠区域；随着莫尔图案变小，这种差异逐渐减弱。本探索表明，利用扭转双层石墨烯的独特性质来有效控制和优化纳米流体行为具有潜力。
英文摘要：
Nanofluidics is pivotal in fundamental research and diverse applications, from water desalination to energy harvesting and biological analysis. Dynamically manipulating nanofluidic properties, such as diffusion and friction, presents an avenue for advancement in this field. Twisted bilayer graphene, particularly at the magic angle, has garnered attention for its unconventional superconductivity and correlated insulator behavior due to strong electronic correlations. However, 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 dataset from ab initio molecular dynamics simulations. It is found that as the twisted angle approaches the magic angle, interfacial water friction increases, leading to 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 exploration illustrates the potential for leveraging the distinctive properties of twisted bilayer graphene to effectively control and optimize nanofluidic behavior.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
