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Modeling of Adsorption and Desorption in Pores of Simple Geometry Using Molecular Dynamics

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Sarkisov, L., Monson, P. A.·2001·Langmuir

Abstract

We present results from molecular dynamics simulations of adsorption and desorption by diffusive mass transfer into model pores of well-defined geometry. The pore geometries considered were chosen to investigate issues related to classical theories of hysteresis in adsorption. We consider the slit pore with or without a closed end, a wedge or triangular pore, and finally a model with an ink bottle geometry. In each case we show that the molecular dynamics simulations yield the same results for adsorption and desorption as grand canonical Monte Carlo simulations of the same systems. Using computer graphics visualization, we show how hysteresis for the slit and wedge geometries is related to the evolution of vapor−liquid interfaces in a manner largely consistent with the classical picture of these systems. We find that for the ink bottle geometry the large pore can empty during desorption even while the small pore remains filled with fluid. This behavior is different from the classical picture of adsorption and desorption in this geometry based on the concept of pore blocking.

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Metadata

Authors
Sarkisov, L., & Monson, P. A.
Venue
Langmuir
Published
2001-10-30
Volume
17
Issue
24
Pages
7600-7604
ISSN
  • 0743-7463 (ISSN-L)
  • 1520-5827
OpenAlex
W2060768954
Cited by
267
References
2
FWCI
5.24
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Modeling of Adsorption and Desorption in Pores of Simple Geometry Using Molecular Dynamics. https://app.cochat.ai/papers/10.1021/la015521u
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Sarkisov, L., & Monson, P. A. (2001). Modeling of Adsorption and Desorption in Pores of Simple Geometry Using Molecular Dynamics. Langmuir. https://doi.org/10.1021/la015521u

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.