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This study introduces a physics-based adsorption model using density functional theory and PC-SAFT. The model accurately predicts adsorption isotherms and properties with minimal data, outperforming existing methods for gas separation applications.

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Area of Science:

  • Physical Chemistry
  • Materials Science
  • Chemical Engineering

Background:

  • Adsorption processes are crucial for applications like gas separation and cooling.
  • Designing these processes requires accurate equilibrium adsorption data, which is often limited.
  • Existing empirical models struggle with data interpolation and extrapolation.

Purpose of the Study:

  • To develop a physics-based model for predicting adsorption isotherms and equilibrium properties.
  • To create a model requiring minimal data for parametrization.
  • To enable accurate data interpolation and extrapolation for adsorption processes.

Main Methods:

  • The model integrates one-dimensional classical density functional theory (1D-DFT) with perturbed-chain statistical associating fluid theory (PC-SAFT).
  • It employs a thermodynamically consistent and computationally efficient approach to model pore phenomena.
  • Parameters are adjusted using a single isotherm for a given adsorbent-fluid pair.

Main Results:

  • The physics-based model demonstrates superior extrapolation capabilities compared to empirical models (e.g., Langmuir, Toth) for adsorption isotherms.
  • It accurately predicts temperature-dependent adsorption properties using limited input data.
  • Parameter transferability to different fluids via standard combining rules was confirmed.

Conclusions:

  • The developed model offers a robust and efficient method for predicting adsorption equilibria.
  • It significantly reduces the data requirements for designing adsorption-based technologies.
  • The model shows promise for applications involving various fluids and porous materials like metal-organic frameworks.