Altered morphology and diffusivity of water confined in MXenes: Machine learning-accelerated computations combined...

Area of Science:

• Materials Science
• Physical Chemistry
• Computational Science

Background:

• Nanoconfined water exhibits unique properties due to limited space, frustrated hydrogen bonding, and surface interactions.
• These properties are crucial for advancing energy storage and transport applications.

Purpose of the Study:

• To systematically analyze the thermodynamic and dynamic behavior of water confined within functionalized 2D Ti3C2Tx MXene layers.
• To establish a model for water diffusivity based on interfacial properties.

Main Methods:

• Integration of machine learning-accelerated ab initio molecular dynamics with experimental X-ray Diffraction (XRD) and Inelastic Neutron Scattering (INS).
• Analysis of water intercalation, interlayer spacing, molecular polarization, and hydrogen bonding dynamics.

Main Results:

• Layer-dependent staging characteristics observed in interlayer spacing.
• Water polarization is influenced by molecule count and morphology, affecting electrostatic potential.
• A linear combination of exponential model accurately describes water diffusivity based on interfacial electrostatic potential, hydrogen bond lifetime, and molecular orientation.

Conclusions:

• Computational insights align well with experimental XRD and INS measurements.
• Tailoring MXene surface chemistry and water content offers strategies for optimizing water morphology and transport.
• Potential applications in electrochemical energy storage and nanofluidic devices.