Cargando…

State-of-the-art predictive modeling of heavy metal ions removal from the water environment using nanotubes

In this research, molecular dynamics (MD) simulation is used to investigate the efficiency of carbon nanotubes (CNT) and boron nitride nanotubes (BNNT) in removing lead ions from contaminated waters. Then the effect of functionalizing nanotubes with –COO– and COOH– functional groups and the nanotube...

Descripción completa

Detalles Bibliográficos
Autores principales: Ghasemi, Zeinab, Farzad, Farzaneh, Zaboli, Ameneh, Zeraatkar Moghaddam, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10349052/
https://www.ncbi.nlm.nih.gov/pubmed/37452035
http://dx.doi.org/10.1038/s41598-023-38442-w
_version_ 1785073793946877952
author Ghasemi, Zeinab
Farzad, Farzaneh
Zaboli, Ameneh
Zeraatkar Moghaddam, Ali
author_facet Ghasemi, Zeinab
Farzad, Farzaneh
Zaboli, Ameneh
Zeraatkar Moghaddam, Ali
author_sort Ghasemi, Zeinab
collection PubMed
description In this research, molecular dynamics (MD) simulation is used to investigate the efficiency of carbon nanotubes (CNT) and boron nitride nanotubes (BNNT) in removing lead ions from contaminated waters. Then the effect of functionalizing nanotubes with –COO– and COOH– functional groups and the nanotubes’ absorption performance of two different concentrations of lead ions are studied. To better evaluate adsorption process, the set of descriptors, such as interaction energies, radial distribution function, etc., are calculated. The MD results show that the absorption performance is significantly improved by modifying the surface of CNT and BNNT with functional groups. In addition, the adsorption capacity increases in higher concentrations of Pb ions at BNNTCOO– and CNTCOOH systems. The interaction energy of BNNTCOO– with a concentration of 50 lead ions is − 2879.28 kJ/mol, which is about 106 kJ/mol more negative than BNNTCOO– at a concentration of 20 lead ions. Also, it is observed that the functionalization of both nanotubes with –COO– increases their absorption capacity. The obtained results from this study provide significant information about the mechanisms of lead adsorption on the surface of nanotubes.
format Online
Article
Text
id pubmed-10349052
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-103490522023-07-16 State-of-the-art predictive modeling of heavy metal ions removal from the water environment using nanotubes Ghasemi, Zeinab Farzad, Farzaneh Zaboli, Ameneh Zeraatkar Moghaddam, Ali Sci Rep Article In this research, molecular dynamics (MD) simulation is used to investigate the efficiency of carbon nanotubes (CNT) and boron nitride nanotubes (BNNT) in removing lead ions from contaminated waters. Then the effect of functionalizing nanotubes with –COO– and COOH– functional groups and the nanotubes’ absorption performance of two different concentrations of lead ions are studied. To better evaluate adsorption process, the set of descriptors, such as interaction energies, radial distribution function, etc., are calculated. The MD results show that the absorption performance is significantly improved by modifying the surface of CNT and BNNT with functional groups. In addition, the adsorption capacity increases in higher concentrations of Pb ions at BNNTCOO– and CNTCOOH systems. The interaction energy of BNNTCOO– with a concentration of 50 lead ions is − 2879.28 kJ/mol, which is about 106 kJ/mol more negative than BNNTCOO– at a concentration of 20 lead ions. Also, it is observed that the functionalization of both nanotubes with –COO– increases their absorption capacity. The obtained results from this study provide significant information about the mechanisms of lead adsorption on the surface of nanotubes. Nature Publishing Group UK 2023-07-14 /pmc/articles/PMC10349052/ /pubmed/37452035 http://dx.doi.org/10.1038/s41598-023-38442-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ghasemi, Zeinab
Farzad, Farzaneh
Zaboli, Ameneh
Zeraatkar Moghaddam, Ali
State-of-the-art predictive modeling of heavy metal ions removal from the water environment using nanotubes
title State-of-the-art predictive modeling of heavy metal ions removal from the water environment using nanotubes
title_full State-of-the-art predictive modeling of heavy metal ions removal from the water environment using nanotubes
title_fullStr State-of-the-art predictive modeling of heavy metal ions removal from the water environment using nanotubes
title_full_unstemmed State-of-the-art predictive modeling of heavy metal ions removal from the water environment using nanotubes
title_short State-of-the-art predictive modeling of heavy metal ions removal from the water environment using nanotubes
title_sort state-of-the-art predictive modeling of heavy metal ions removal from the water environment using nanotubes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10349052/
https://www.ncbi.nlm.nih.gov/pubmed/37452035
http://dx.doi.org/10.1038/s41598-023-38442-w
work_keys_str_mv AT ghasemizeinab stateoftheartpredictivemodelingofheavymetalionsremovalfromthewaterenvironmentusingnanotubes
AT farzadfarzaneh stateoftheartpredictivemodelingofheavymetalionsremovalfromthewaterenvironmentusingnanotubes
AT zaboliameneh stateoftheartpredictivemodelingofheavymetalionsremovalfromthewaterenvironmentusingnanotubes
AT zeraatkarmoghaddamali stateoftheartpredictivemodelingofheavymetalionsremovalfromthewaterenvironmentusingnanotubes