Adsorption of Cr (VI), and Pb (II) from aqueous solution by 1-Butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide functionalized biomass Hazel Sterculia (Sterculia Foetida L.)

Isothermal and Kinetics Modeling Approach for the Bioremediation of Potentially Toxic Trace Metal Ions Using a Novel Biosorbent Acalypha wilkesiana (Copperleaf) Leaves

The presence of trace metals in wastewater brings serious environmental pollution that threatens human health as well as the ecosystem throughout the world due to their non-biodegradability nature. The present study focuses on the bioremediation of toxic trace metals, namely arsenic (As), cadmium (Cd), and chromium (Cr), using Acalypha wilkesiana leaf raw biomass. The optimization of various process variables was done to determine the removal percentage of trace metal using Acalypha wilkesiana leaf raw biomass, and the optimum conditions were an adsorbent dose of 0.5 g, contact time 10 h, 8 h, and 10 h, process temperature 30 °C, initial concentration of trace metal as 30 µg/L, 30 mg//L, and 40 mg/L, and pH of 7.5, 7 and 7.5 for As5+, and Cd2+ and Cr6+, respectively. Acalypha wilkesiana leaf raw biomass is characterized using a scanning electron microscope (SEM), energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and Fourier transformation infrared spectroscopy (FTIR), zeta potential before and after adsorption of the trace metal ions. The study was well fitted for the equilibrium data for Langmuir isotherm for As5+, Cd2+, and Cr6+, Freundlich for As5+, Dubinin-Radushkevinch (D-R) for Cr6+, and Temkin for As5+ and Cr6+. The adsorption of all three trace metals was confirmed by the kinetics and thermodynamic studies to be following pseudo-second-order kinetics with endothermic as well as spontaneous processes, respectively. Thus, the present study indicates Acalypha wilkesiana leaf raw biomass as an effective and efficient novel biosorbent to remediate different trace metals from aqueous systems with its possible application in existing and novel methods for wastewater management.

Design of Zr-MOFs by Introducing Multiple Ligands for Efficient and Selective Capturing of Pb(II) from Aqueous Solutions

The existence of lead ions seriously affects the quality of many metal products in metallurgical enterprises. Currently, the various methods of lead-ion removal tried by researchers will affect valuable metals in the removal process, thus resulting in low economic efficiency. In this study, a novel metal-organic framework adsorbent (UiO-FHD) which efficiently and selectively captures lead ions is developed by introducing multiple ligands. The maximum adsorption capacity of lead ions is 433.15 mg/g at pH 5. The adsorption process accords with the pseudo-second-order kinetic and the Langmuir isotherm models at room temperature. Thermodynamic experiments indicate that the removal of Pb(II) is facilitated by appropriate temperature reduction. The performance tests indicate that UiO-FHD maintains a high removal rate of 90.35% for Pb(II) after four consecutive adsorption-desorption cycles. The distribution coefficient of lead ions (26.7 L/g) shows that UiO-FHD has excellent selective adsorption for lead ions. It is revealed that the chelation of the sulfhydryl groups and the electrostatic interaction of the hydroxyl groups are the dominant factors to improve the removal rate of Pb(II) by density functional theory calculations. This study clarifies the value of self-designed novel organic ligands in metal-organic framework materials that selectively capture heavy-metal ions.

Machine Learning Quantitative Structure-Property Relationships as a Function of Ionic Liquid Cations for the Gas-Ionic Liquid Partition Coefficient of Hydrocarbons

Ionic liquids (ILs) are known for their unique characteristics as solvents and electrolytes. Therefore, new ILs are being developed and adapted as innovative chemical environments for different applications in which their properties need to be understood on a molecular level. Computational data-driven methods provide means for understanding of properties at molecular level, and quantitative structure-property relationships (QSPRs) provide the framework for this. This framework is commonly used to study the properties of molecules in ILs as an environment. The opposite situation where the property is considered as a function of the ionic liquid does not exist. The aim of the present study was to supplement this perspective with new knowledge and to develop QSPRs that would allow the understanding of molecular interactions in ionic liquids based on the structure of the cationic moiety. A wide range of applications in electrochemistry, separation and extraction chemistry depends on the partitioning of solutes between the ionic liquid and the surrounding environment that is characterized by the gas-ionic liquid partition coefficient. To model this property as a function of the structure of a cationic counterpart, a series of ionic liquids was selected with a common bis-(trifluoromethylsulfonyl)-imide anion, [Tf2N]-, for benzene, hexane and cyclohexane. MLR, SVR and GPR machine learning approaches were used to derive data-driven models and their performance was compared. The cross-validation coefficients of determination in the range 0.71-0.93 along with other performance statistics indicated a strong accuracy of models for all data series and machine learning methods. The analysis and interpretation of descriptors revealed that generally higher lipophilicity and dispersion interaction capability, and lower polarity in the cations induces a higher partition coefficient for benzene, hexane, cyclohexane and hydrocarbons in general. The applicability domain analysis of models concluded that there were no highly influential outliers and the models are applicable to a wide selection of cation families with variable size, polarity and aliphatic or aromatic nature.