Machine-Learning Potential Molecular Dynamics Reveals the Critical Role of Flexibility in Solid-Liquid Nanofluidic...

Area of Science:

• Nanofluidics
• Materials Science
• Surface Physics

Background:

• Atomically thin 2D materials used as nanochannel walls are typically modeled as rigid boundaries.
• However, these walls exhibit inherent thermal fluctuations at finite temperatures.
• Understanding solid-liquid interfacial friction is crucial for nanochannel fluid transport.

Purpose of the Study:

• Investigate the impact of wall flexibility on solid-liquid interfacial friction in nanochannels.
• Analyze the relationship between friction coefficient, nanochannel size, and wall properties.
• Explore methods to control nanoscale fluid flow through wall mechanics.

Main Methods:

• Employed machine-learning-potential molecular dynamics simulations.
• Simulated water confined within flexible nanochannels of varying lateral sizes (L).
• Analyzed the friction coefficient (λ) and its dependence on channel size and wall bending stiffness (D).

Main Results:

• Discovered that the friction coefficient (λ) increases linearly with 1/L in flexible nanochannels, a phenomenon absent in rigid channels.
• Observed that this size dependence weakens for thicker walls with higher bending stiffness (D).
• Decomposed friction into lattice roughness and thermal-fluctuation-induced ripple contributions, with the latter scaling as D^(-1/2).

Conclusions:

• Demonstrated that thermal fluctuations in flexible 2D material nanochannels significantly influence solid-liquid interfacial friction.
• Showed that friction can be controlled by mechanical strain applied to channel walls, by tuning phonon modes and surface fluctuations.
• Provided insights into manipulating nanoscale flow via local curvature and fluid-solid coupling control.