Experimental measurements have reported ultrafast and radius-dependent water transport in carbon nanotubes which are absent in boron nitride nanotubes. Despite considerable effort, the origin of this contrasting (and fascinating) behavior is not understood. Here, with the aid of machine learning-based molecular dynamics simulations that deliver first-principles accuracy, we investigate water transport in single-wall carbon and boron nitride nanotubes. Our simulations reveal a large, radius-dependent hydrodynamic slippage on both materials, with water experiencing indeed a ≈5 times lower friction on carbon surfaces compared to boron nitride. Analysis of the diffusion mechanisms across the two materials reveals that the fast water transport on carbon is governed by facile oxygen motion, whereas the higher friction on boron nitride arises from specific hydrogen–nitrogen interactions. This work not only delivers a clear reference of quantum mechanical accuracy for water flow in single-wall nanotubes but also provides detailed mechanistic insight into its radius and material dependence for future technological application.

中文翻译：

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单壁纳米管中的水流：氧气使其滑动，氢气使其粘附

实验测量报告了碳纳米管中的超快和半径依赖性水传输，这在氮化硼纳米管中是不存在的。尽管付出了相当大的努力，但这种对比鲜明（且引人入胜）的行为的起源尚不清楚。在这里，借助提供第一性原理准确性的基于机器学习的分子动力学模拟，我们研究了单壁碳和氮化硼纳米管中的水传输。我们的模拟揭示了这两种材料的大的、依赖于半径的流体动力学滑移，与氮化硼相比，水在碳表面的摩擦力确实降低了约 5 倍。对两种材料的扩散机制的分析表明，碳上的快速水传输受容易的氧运动控制，而氮化硼上较高的摩擦是由特定的氢-氮相互作用引起的。这项工作不仅为单壁纳米管中水流的量子力学精度提供了清晰的参考，而且还为未来的技术应用提供了对其半径和材料依赖性的详细机制洞察。