Adsorptive removal of heavy metals from aqueous solutions: Progress of adsorbents development and their effectiveness.

Increased levels of heavy metals (HMs) in aquatic environments poses serious health and ecological concerns. Hence, several approaches have been proposed to eliminate/reduce the levels of HMs before the discharge/reuse of HMs-contaminated waters. Adsorption is one of the most attractive processes for water decontamination; however, the efficiency of this process greatly depends on the choice of adsorbent. Therefore, the key aim of this article is to review the progress in the development and application of different classes of conventional and emerging adsorbents for the abatement of HMs from contaminated waters. Adsorbents that are based on activated carbon, natural materials, microbial, clay minerals, layered double hydroxides (LDHs), nano-zerovalent iron (nZVI), graphene, carbon nanotubes (CNTs), metal organic frameworks (MOFs), and zeolitic imidazolate frameworks (ZIFs) are critically reviewed, with more emphasis on the last four adsorbents and their nanocomposites since they have the potential to significantly boost the HMs removal efficiency from contaminated waters. Furthermore, the optimal process conditions to achieve efficient performance are discussed. Additionally, adsorption isotherm, kinetics, thermodynamics, mechanisms, and effects of varying adsorption process parameters have been introduced. Moreover, heavy metal removal driven by other processes such as oxidation, reduction, and precipitation that might concurrently occur in parallel with adsorption have been reviewed. The application of adsorption for the treatment of real wastewater has been also reviewed. Finally, challenges, limitations and potential areas for improvements in the adsorptive removal of HMs from contaminated waters are identified and discussed. Thus, this article serves as a comprehensive reference for the recent developments in the field of adsorptive removal of heavy metals from wastewater. The proposed future research work at the end of this review could help in addressing some of the key limitations facing this technology, and create a platform for boosting the efficiency of the adsorptive removal of heavy metals.

Aqueous Pb(II) Removal Using ZIF-60: Adsorption Studies, Response Surface Methodology and Machine Learning Predictions.

Zeolitic imidazolate frameworks (ZIFs) are increasingly gaining attention in many application fields due to their outstanding porosity and thermal stability, among other exceptional characteristics. However, in the domain of water purification via adsorption, scientists have mainly focused on ZIF-8 and, to a lesser extent, ZIF-67. The performance of other ZIFs as water decontaminants is yet to be explored. Hence, this study applied ZIF-60 for the removal of lead from aqueous solutions; this is the first time ZIF-60 has been used in any water treatment adsorption study. The synthesized ZIF-60 was subjected to characterization using FTIR, XRD and TGA. A multivariate approach was used to investigate the effect of adsorption parameters on lead removal and the findings revealed that ZIF-60 dose and lead concentration are the most significant factors affecting the response (i.e., lead removal efficiency). Further, response surface methodology-based regression models were generated. To further explore the adsorption performance of ZIF-60 in removing lead from contaminated water samples, adsorption kinetics, isotherm and thermodynamic investigations were conducted. The findings revealed that the obtained data were well-fitted by the Avrami and pseudo-first-order kinetic models, suggesting that the process is complex. The maximum adsorption capacity (qmax) was predicted to be 1905 mg/g. Thermodynamic studies revealed an endothermic and spontaneous adsorption process. Finally, the experimental data were aggregated and used for machine learning predictions using several algorithms. The model generated by the random forest algorithm proved to be the most effective on the basis of its significant correlation coefficient and minimal root mean square error (RMSE).