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Behavior of the Enthalpy of Adsorption in Nanoporous Materials Close to Saturation Conditions.

Ariana Torres-KnoopAli PoursaeidesfahaniThijs J H VlugtDavid Dubbeldam
Published in: Journal of chemical theory and computation (2017)
Many important industrial separation processes based on adsorption operate close to saturation. In this regime, the underlying adsorption processes are mostly driven by entropic forces. At equilibrium, the entropy of adsorption is closely related to the enthalpy of adsorption. Thus, studying the behavior of the enthalpy of adsorption as a function of loading is fundamental to understanding separation processes. Unfortunately, close to saturation, the enthalpy of adsorption is hard to measure experimentally and hard to compute in simulations. In simulations, the enthalpy of adsorption is usually obtained from energy/particle fluctuations in the grand-canonical ensemble, but this methodology is hampered by vanishing insertions/deletions at high loading. To investigate the fundamental behavior of the enthalpy and entropy of adsorption at high loading, we develop a simplistic model of adsorption in a channel and show that at saturation the enthalpy of adsorption diverges to large positive values due to repulsive intermolecular interactions. However, there are many systems that can avoid repulsive intermolecular interactions and hence do not show this drastic increase in enthalpy of adsorption close to saturation. We find that the conventional grand-canonical Monte Carlo method is incapable of determining the enthalpy of adsorption from energy/particle fluctuations at high loading. Here, we show that by using the continuous fractional component Monte Carlo, the enthalpy of adsorption close to saturation conditions can be reliably obtained from the energy/particle fluctuations in the grand-canonical ensemble. The best method to study properties at saturation is the NVT energy (local-) slope methodology.
Keyphrases
  • aqueous solution
  • monte carlo
  • molecular dynamics
  • heavy metals
  • machine learning
  • mass spectrometry
  • deep learning
  • energy transfer