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(searched for: doi:10.1016/j.molliq.2021.116741)
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Published: 16 August 2022
by MDPI
Journal: Water
Abstract:
This work describes an experimental and machine learning approach for the prediction of selenite removal on chemically modified zeolite for water treatment. Breakthrough curves were constructed using iron-coated zeolite adsorbent and the adsorption behavior was evaluated as a function of an initial contaminant concentration as well as the ionic strength. An elevated selenium concentration in water threatens human health and aquatic life. The migration of this metalloid from the contaminated sites and the problems associated with its high releases into the water has become a major environmental concern. The mobility of this emerging metalloid in the contaminated water prompted the development of an efficient, cost-effective adsorbent for its removal. Selenite [Se(IV)] removal from aqueous solutions was studied in laboratory-scale continuous and packed-bed adsorption columns using iron-coated natural zeolite adsorbents. The proposed adsorbent combines iron oxide and natural zeolite’s ability to bind contaminants. Breakthrough curves were initially obtained under variable experimental conditions, including the change in the initial concentration of Se (IV), and the ionic strength of solutions. Investigating the effect of these parameters will enhance selenite mobility retardation in contaminated water. Continuous adsorption experiment findings will evaluate the efficiency of this economical and naturally-based adsorbent for selenite removal and fate in water. Multilinear and non-linear regressions approaches were utilized, yet low coefficients of determination values were respectively obtained. Then, a comparative analysis of five boosted regression tree algorithms for a selenite breakthrough curve prediction was performed. AdaBoost, Gradient boosting, XGBoost, LightGBM, and CatBoost models were analyzed using the experimental data of the packed-bed columns. The performance of these models for the breakthrough curve prediction under different operation conditions, such as initial selenite concentration and ionic strength, was discussed. The applicability of these models was evaluated using performance metrics (i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The CatBoost model provided the best fit for a breakthrough prediction with a coefficient of determination R2 equal to 99.57. The k-fold cross-validation technique and the statistical metrics verify this model’s accurateness. A feature importance assessment indicated that Se (IV) initial concentration was the most influential experimental variable, while the ionic strength had the least effect. This finding was consistent with the column transport results, which observed Se (IV) sorption dependency on its inlet concentration; simultaneously, the ionic strength effect was negligible. This work proposes implementing machine learning-based approaches for predicting water remediation-associated processes. The significance of this work was to provide an alternative method for investigating selenite adsorption behavior and predicting the breakthrough curves using a machine-based approach. This work also highlighted the importance of management practices of adsorption processes involved in water remediation.
Yu. S. Petrova, , K. Ya. Kuznetsova, E. I. Kapitanova, L. K. Neudachina, A. V. Pestov
Russian Journal of Inorganic Chemistry, Volume 67, pp 1080-1087; https://doi.org/10.1134/s003602362207018x

Abstract:
The sorption kinetics of selected metal ions from multicomponent solutions by sorbents with taurine functions based on aminopolymers, namely, on chitosan, polyallylamine, and polyethyleneimine, has been studied. Sulfoethylated polyallylamine sorbents are distinguished by the highest silver(I) sorption selectivity and the shortest equilibration time. Sulfoethylated polyethyleneimines, unlike the other sorbents studied, can be used, depending on the pH of ammonium acetate buffer solution, for the group recovery of transition-metal ions or for co-recovering silver(I), copper(II), and nickel(II). The chemical structure of the polymer matrix and the degree of its modification do not significantly affect the initial silver(I) sorption rate from multicomponent solutions.
Serkan Sayin
Türk Doğa ve Fen Dergisi, Volume 11, pp 70-75; https://doi.org/10.46810/tdfd.1036402

Abstract:
İki kaliksarene-fonksiyonlu biyopolimerler (kaliksaren-fonksiyonlu kitosan ve kaliksaren-fonksiyonlu selüloz) sentezlenmiş ve FTIR, TGA ve element analiz gibi teknikler kullanılarak uygun bir şekilde karakterize edilmişlerdir. Ayrıca, çeşitli pH lardaki anyon ekstraksiyon davranışları dikromat ve arsenat anyonlarına karşı incelenmiştir. Sonuçlar dikromat iyonuna karşı kaliksaren-fonksiyonlu kitosanın kaliksaren-fonksiyonlu selüloza göre daha büyük ekstraksiyon kabiliyetinin olduğunu gösterdi. İlginç bir şekilde, dikromat anyonuna karşı daha düşük bir ekstraksiyon verimliliği kaliksaren-fonksiyonlu selüloz ile elde edilmesine rağmen, arsenat anyon ektraksiyon sonuçları kaliksaren-fonksiyonlu selülozun kaliksaren-fonksiyonlu kitosana göre daha etkin iyonofor olduğunu gösterdi.
Niloufar Torkian, Abbas Bahrami, Afrouzossadat Hosseini-Abari, Mohammad Mohsen Momeni, Meisam Abdolkarimi-Mahabadi, , Pejman Hajipour, Hamed Amini Rourani, Mohammad Saeid Abbasi, Sima Torkian, et al.
Published: 1 May 2022
Environmental Research, Volume 207; https://doi.org/10.1016/j.envres.2021.112157

Li Jiang, Yating Chen, Yifei Wang, Jiayang Lv, Peng Dai, Jian Zhang, Ying Huang, Wenzhou Lv
Published: 16 March 2022
Journal: ACS Omega
ACS Omega, Volume 7, pp 10502-10515; https://doi.org/10.1021/acsomega.2c00014

The publisher has not yet granted permission to display this abstract.
Nida Shams Jalbani, , Shahabuddin Memon, , Asif Ali Bhatti
Journal of Dispersion Science and Technology pp 1-9; https://doi.org/10.1080/01932691.2022.2046043

Abstract:
Calixarenes are known as fascinated macromolecules due to their flexible structure that can be cast into different fields of application. These macromolecules have been used as quick and highly selective functional material for the extraction and separation of metal ions. This study explores metal ion removal efficiency of new calixarene coated silica resin from aqueous environment through solid phase extraction. The calixarene-coated silica resin (CCS resin) was synthesized and characterized by FTIR, SEM, XRD, EDS and BET. Static and dynamic adsorption experiments were followed to check the removal efficiency of CCS resin. Adsorption experiment shows that, CCS resin has strong potential for the removal of divalent and trivalent metal ions as compare to monovalent metal ions. Adsorption data have been evaluated by applying Langmuir, Freundlich, D-R models and Thomas dynamic adsorption models. The adsorption isotherm of the CCS resin agreed well with the Langmuir adsorption equation with regression coefficient of 0.99 and good monolayer adsorption capacities such as 3.22, 2.95, 3.04, 3.11, 2.81 and 2.82 (mol.g−1) for Hg2+ Pb2+ La3+ Cr3+ Al3+ and Fe3+ respectively. The D-R isotherm model suggests that the adsorption process follow ion exchange mechanism with mean sorption energy falls in the range of 9.0–16 KJ.mol−1. Moreover, the exhaustion capacity of column was calculated by using the Thomas model, which shows very small qo value with good fit to data (R2 =0.99). The thermodynamic and kinetic studies have also been performed, which reveals that the reaction is spontaneous and exothermic in nature and follows pseudo second-order kinetics. Graphical Abstract
, Nida Shams Jalbani, Savas Kaya, Goncagül Serdaroglu, Mustafa Elik, Shahabuddin Memon
Published: 21 December 2021
Separation Science and Technology, Volume 57, pp 1884-1899; https://doi.org/10.1080/01496395.2021.2009869

Abstract:
Water is being contaminated by different oxyanions which have many negative effects on the human body. This study deals with the adsorptive removal of Cr2O7−2, AsO4−3, and ClO4−1 oxyanions from water using diethanolaminomethylcalix[4]arene bonded silica (DBS) resin. The capacity of DBS resin for the removal of oxyanions was checked through batch method under the optimized parameters such as pH, adsorbent dosage, concentration of oxyanions, and effect of temperature. The experimental data were analyzed through Langmuir, Freundlich, and Dubinin–Radushkevitch (D–R) adsorption isotherm models and the Langmuir model was the best fit model with a good correlation coefficient (R2 0.998). The thermodynamic and kinetic studies were performed to check the mechanism and adsorption pathway of oxyanions onto DBS resin. The thermodynamic parameters such as (Δ, Δ, and Δ) describes that the adsorption of oxyanions was spontaneous and endothermic and followed by pseudo second order kinetic models very well. The reusability of resin was also checked and it has been observed that after 27 cycles only 2% loss in adsorption capacity. Moreover, the oxyanions were optimized at the B3LYP/LANL2DZ/6-311++G (d,p) level using G09W software to analyze the oxyanions-DBS interactions phenomenon. Graphical abstract
Juying Li, Xiaotong Huang, Zhangming Hou,
Published: 14 December 2021
Journal: Chemosphere
The publisher has not yet granted permission to display this abstract.
Yong-Yuan Chen, Xi-Wen Lan, Hao Ren, Wen-Jie Li, Jun Chen, Xin-Yu Jiang,
Published: 4 October 2021
Journal of Environmental Chemical Engineering, Volume 9; https://doi.org/10.1016/j.jece.2021.106500

The publisher has not yet granted permission to display this abstract.
, Nida Shams Jalbani, Savas Kaya, , Riadh Marzouki, Shahabuddin Memon
International Journal of Environmental Analytical Chemistry pp 1-17; https://doi.org/10.1080/03067319.2021.1979534

Abstract:
This study describes the removal of methyl orange (MO) and methyl red (MR) dyes from water samples using morpholinomethylcalix[4]arene immobilised silica (MIS) resin. The silica surface has been modified by p-morpholinomethylcalix[4]arene moiety and was characterised by FT-IR spectroscopy and SEM techniques. The adsorption capacity of MIS-resin was checked through batch adsorption experiments under the optimised conditions of pH, MIS-resin dose, time, and temperature. Results show that adsorption of MO and MR dyes are highly affected by the change in pH; thus, the higher adsorption percentages were achieved at pH 5.3 and 6.6 respectively. The adsorbent dosage has been optimised and it was noticed that the maximum adsorption was achieved by using 40 mg.L−1 of MIS-resin dose. The adsorption rate of dyes was investigated by applying the pseudo-first and second-order kinetic models and it has been observed that the experimental data shows a better correlation coefficient with the pseudo-second-order kinetic model. The feasibility of adsorption was analysed by thermodynamic parameters such as ∆H°, ∆G°, and ∆S° values indicate that the adsorption of dyes is exothermic and spontaneous. The equilibrium data have been validated using Langmuir and Freundlich models and the Langmuir model has a good correlation coefficient (R2 0.99). The MIS-resin was applied onto industrial effluents and it has been observed that the prepared resin is a very efficient adsorbent for the treatment of dyes contaminated wastewater. The adsorption of MO and MR dyes onto MIS-resin was well defined by computational chemical modelling at the B3LYP/LANL2DZ/6-311++G (d,p) level using G09W software.
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