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Batool F, Irfan A, Al-Hussain SA, Al-Farraj ES, Iqbal S, Akbar J, Noreen S, Akhtar T, Iqbal T, Zaki MEA. Development of Ion Character Property Relationship (IC-PR) for Removal of 13-Metal Ions by Employing a Novel Green Adsorbent Aerva javanica. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27238213. [PMID: 36500307 PMCID: PMC9741335 DOI: 10.3390/molecules27238213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 11/26/2022]
Abstract
The novel Aerva javanica absorbent was applied for the removal of thirteen selected metal ions from a distilled water solution of each metal by the batch adsorption method. The optimization remediation parameters of the metal ions for the batch adsorption approach were developed, which were the initial concentrations (60 ppm), contact time (60 min) and pH (7). The basic properties of metal ion affected the adsorption results; therefore, 21 properties of metal ions were selected, which are called "descriptors". The most significant descriptors were selected that were vital for the adsorption results, such as covalent index, polarizability and ion charge. The developed model equation by the descriptors provided more than 80% accuracy in the predicted results. Furthermore, Freundlich and Langmuir adsorption models were also applied on the results. Constants of the Freundlich and Langmuir models were also used for model generation, and the results revealed the importance of a covalent index for the removal phenomenon of metal ions. The current study provided a suitable Ion Character Property Relationship (IC-PR) for the removal of metal ions, and future predictions can be achieved on the proposed adsorbent with significant accuracy. The ecofriendly and cost effective Aerva javanica absorbent in the batch experimental model of the current study predicted that this novel absorbent can be used for the removal of a wide spectrum of heavy metal ions from different sources of waste waters.
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Affiliation(s)
- Fozia Batool
- Department of Chemistry, University of Sargodha, Sargodha 40100, Pakistan
- Correspondence: (F.B.); (M.E.A.Z.); Tel.: +9-234-4747-4109 (F.B.)
| | - Ali Irfan
- Department of Chemistry, Government College University Faisalabad, Faisalabad 38000, Pakistan
| | - Sami A. Al-Hussain
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13623, Saudi Arabia
| | - Eida S. Al-Farraj
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13623, Saudi Arabia
| | - Shahid Iqbal
- Department of Chemistry, University of Education Lahore, Jauharabad Campus, Lahore 41200, Pakistan
| | - Jamshed Akbar
- Department of Chemistry, University of Sargodha, Sargodha 40100, Pakistan
| | - Sobia Noreen
- Department of Chemistry, University of Sargodha, Sargodha 40100, Pakistan
| | - Taslim Akhtar
- Govt. Associate College for Women, Mandi Bahauddine 50400, Pakistan
| | - Tunzeel Iqbal
- The Rawalpindi Women University Rawalpindi, Rawalpinfi 46000, Pakistan
| | - Magdi E. A. Zaki
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13623, Saudi Arabia
- Correspondence: (F.B.); (M.E.A.Z.); Tel.: +9-234-4747-4109 (F.B.)
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Ma D, Yao Q, Wang J, Hao Q, Chen H, Ma L, Sun M, Ma X. Simple descriptor based machine learning model development for synergy prediction of different metal loadings and solvent swellings on coal pyrolysis. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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