Computational methods for adsorption study in wastewater
treatment

J 2023

Computational methods for adsorption study in wastewater treatment

MALLOUM, Alhadji, Kayode A. ADEGOKE, Joshua O. IGHALO, Jeanet CONRADIE, Chinemerem R. OHORO et. al.

Basic information

Original name

Computational methods for adsorption study in wastewater treatment

Authors

Edition

Journal of Molecular Liquids, Amsterdam, Elsevier B.V. 2023, 0167-7322

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10301 Atomic, molecular and chemical physics

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 6.000 in 2022

RIV identification code

RIV/00216224:14310/23:00133181

Organization unit

Faculty of Science

DOI

http://dx.doi.org/10.1016/j.molliq.2023.123008

UT WoS

001082346400001

Keywords in English

Adsorption; Computational methods; Molecular simulation; Wastewater treatment; DFT

Abstract

V originále

Computation methods or molecular simulations can be used to identify materials with high adsorption capacity for wastewater treatment rapidly. They can also provide complementary insights into the dynamics of adsorption processes. In addition, molecular simulations can be used to understand the adsorption mechanism and the interaction between contaminants and adsorbents. Considering their applicability in wastewater treatment, computational methods have been widely used for adsorption studies. Due to affordability, most of these methods are based on density functional theory (DFT) calculations. However, molecular dynamics and Monte Carlo simulations have also been used to get insights into the adsorption of pollutants. With the growth of artificial intelligence, machine learning-based approaches have also been used to predict the adsorption capacity of materials. Despite the importance of computational methods in wastewater treatment, no review article summarizes the methods used in the literature. Therefore, we undertake to summarize, classify, and provide a detailed description of computational methods used in the adsorption studies for wastewater treatment. For each method, we review some outstanding works using the method and provide the advantages and limits of the method. Finally, future directions and possible improvements of the computational methods are suggested as perspectives.

Citovat

MALLOUM, Alhadji, Kayode A. ADEGOKE, Joshua O. IGHALO, Jeanet CONRADIE, Chinemerem R. OHORO, James F. AMAKU, Kabir O. OYEDOTUN, Nobanathi W. MAXAKATO, Kovo G. AKPOMIE, Emmanuel S. OKEKE and Chijioke OLISAH. Computational methods for adsorption study in wastewater treatment. Journal of Molecular Liquids. Amsterdam: Elsevier B.V., 2023, vol. 390, November 2023, p. 1-16. ISSN 0167-7322. Available from: https://dx.doi.org/10.1016/j.molliq.2023.123008.

@article{2364937,
   author = {Malloum, Alhadji and Adegoke, Kayode A. and Ighalo, Joshua O. and Conradie, Jeanet and Ohoro, Chinemerem R. and Amaku, James F. and Oyedotun, Kabir O. and Maxakato, Nobanathi W. and Akpomie, Kovo G. and Okeke, Emmanuel S. and Olisah, Chijioke},
   article_location = {Amsterdam},
   article_number = {November 2023},
   doi = {http://dx.doi.org/10.1016/j.molliq.2023.123008},
   keywords = {Adsorption; Computational methods; Molecular simulation; Wastewater treatment; DFT},
   language = {eng},
   issn = {0167-7322},
   journal = {Journal of Molecular Liquids},
   title = {Computational methods for adsorption study in wastewater treatment},
   url = {https://www.sciencedirect.com/science/article/pii/S0167732223018147?via%3Dihub},
   volume = {390},
   year = {2023}
}

TY  - JOUR
ID  - 2364937
AU  - Malloum, Alhadji - Adegoke, Kayode A. - Ighalo, Joshua O. - Conradie, Jeanet - Ohoro, Chinemerem R. - Amaku, James F. - Oyedotun, Kabir O. - Maxakato, Nobanathi W. - Akpomie, Kovo G. - Okeke, Emmanuel S. - Olisah, Chijioke
PY  - 2023
TI  - Computational methods for adsorption study in wastewater treatment
JF  - Journal of Molecular Liquids
VL  - 390
IS  - November 2023
SP  - 1-16
EP  - 1-16
PB  - Elsevier B.V.
SN  - 01677322
KW  - Adsorption
KW  - Computational methods
KW  - Molecular simulation
KW  - Wastewater treatment
KW  - DFT
UR  - https://www.sciencedirect.com/science/article/pii/S0167732223018147?via%3Dihub
N2  - Computation methods or molecular simulations can be used to identify materials with high adsorption capacity for wastewater treatment rapidly. They can also provide complementary insights into the dynamics of adsorption processes. In addition, molecular simulations can be used to understand the adsorption mechanism and the interaction between contaminants and adsorbents. Considering their applicability in wastewater treatment, computational methods have been widely used for adsorption studies. Due to affordability, most of these methods are based on density functional theory (DFT) calculations. However, molecular dynamics and Monte Carlo simulations have also been used to get insights into the adsorption of pollutants. With the growth of artificial intelligence, machine learning-based approaches have also been used to predict the adsorption capacity of materials. Despite the importance of computational methods in wastewater treatment, no review article summarizes the methods used in the literature. Therefore, we undertake to summarize, classify, and provide a detailed description of computational methods used in the adsorption studies for wastewater treatment. For each method, we review some outstanding works using the method and provide the advantages and limits of the method. Finally, future directions and possible improvements of the computational methods are suggested as perspectives.
ER  -

MALLOUM, Alhadji, Kayode A. ADEGOKE, Joshua O. IGHALO, Jeanet CONRADIE, Chinemerem R. OHORO, James F. AMAKU, Kabir O. OYEDOTUN, Nobanathi W. MAXAKATO, Kovo G. AKPOMIE, Emmanuel S. OKEKE and Chijioke OLISAH. Computational methods for adsorption study in wastewater treatment. \textit{Journal of Molecular Liquids}. Amsterdam: Elsevier B.V., 2023, vol.~390, November 2023, p.~1-16. ISSN~0167-7322. Available from: https://dx.doi.org/10.1016/j.molliq.2023.123008.

Detailed Information on Publication Record