The effect of nanopores geometry on desalination of single-layer graphene-based membranes: A molecular dynamics study

[Display omitted] •The desalination process using a single layer graphene membrane is studied.•The effects of the nanopore geometry on the desalination process are inspected.•The aspect ratio, the hydraulic diameter and the equal area method were employed.•The methods properly predict the trend of t...

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Veröffentlicht in:Journal of molecular liquids 2021-10, Vol.339, p.116749, Article 116749
Hauptverfasser: Sarvestani, Amirhossein Bagheri, Chogani, Alireza, Shariat, Maryam, Moosavi, Ali, Kariminasab, Hamid
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Sprache:eng
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Zusammenfassung:[Display omitted] •The desalination process using a single layer graphene membrane is studied.•The effects of the nanopore geometry on the desalination process are inspected.•The aspect ratio, the hydraulic diameter and the equal area method were employed.•The methods properly predict the trend of the water flow rate and the salt rejection.•Establishing suitable parameters for comparing different pores is very beneficial. The water desalination process using nanoporous single-layer graphene membranes is simulated through classical molecular dynamics. The effect of nanopores shapes on the capacity of the membrane for filtration of water is investigated. According to the results, the geometry of the nanopores considerably affects the performance of the membrane and can completely change the water flow rate and salt rejection. The results reveal that the effective area of the nanopores plays a critical role and for a better understanding of the impact of this parameter, aspect ratio and the equal diameter of noncircular pores based on different methods such as equal area, equal perimeter, and hydraulic diameter for each case are calculated. In the case of noncircular nanopores, the results indicate that aspect ratio and hydraulic diameter, as well as equal area method, can adequately capture the associated trends, while the equal perimeter method is unable to correctly predict them.
ISSN:0167-7322
1873-3166
DOI:10.1016/j.molliq.2021.116749