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The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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Deep learning revealed distinct microscopic properties at graphene-water and air-water interfaces, despite similar sum-frequency generation (SFG) spectra. Differences lie in thickness, hydrogen bonding, and dynamics, highlighting unique solid-liquid interface characteristics.

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Area of Science:

  • Surface science
  • Computational chemistry
  • Materials science

Background:

  • Air-water and graphene-water interfaces are key models for liquid-gas and liquid-solid boundaries.
  • Sum-frequency generation (SFG) spectroscopy shows similarities between these interfaces, but interpretations vary.
  • Experimental discrepancies in SFG spectra necessitate advanced computational approaches.

Purpose of the Study:

  • To computationally investigate and differentiate the microscopic properties of air-water and graphene-water interfaces.
  • To resolve discrepancies in the interpretation of experimental SFG spectra for these systems.
  • To leverage deep learning for first-principles SFG spectra computation.

Main Methods:

  • Utilized deep learning to compute first-principles sum-frequency generation (SFG) spectra.
  • Analyzed and compared SFG spectra of air-water and graphene-water interfaces.
  • Investigated interfacial thickness, hydrogen bonding, and surface dynamics.

Main Results:

  • Despite similar SFG spectra, air-water and graphene-water interfaces exhibit fundamentally different microscopic properties.
  • Key differences were identified in SFG-active layer thickness, hydrogen-bonding network structure, and surface dynamics.
  • Graphene-water interfaces show roughness suppression and electronic interactions absent in air-water interfaces.

Conclusions:

  • Similarities in SFG signals do not imply similar interfacial structures or dynamics.
  • The solid-liquid (graphene-water) interface possesses unique characteristics compared to the liquid-gas (air-water) interface.
  • Deep learning-based first-principles calculations are crucial for accurate interpretation of interfacial phenomena.