Global interpretation and generalizability of boosted regression models for the prediction of methylene blue adsorption by different clay minerals and alkali activated materials.

In this study, Gradient Boosted Regression Trees is applied, for the first time, to predict governing factors for methylene blue (MB) adsorption on a variety of adsorbents involving clay minerals, such as kaolinite and sepiolite together with industrial wastes red mud and fly ash, and alkali activated materials synthesized from aforementioned raw materials. Dataset was constructed using electronic databases, such as ScienceDirect, Scopus, Elsevier, and Google, experimental studies published between 2005 and 2022 were covered. The final dataset included experimental conditions, such as adsorbent type, adsorbent properties (surface characteristics, density, and chemical modifications), pH of the medium, adsorbent dosage, and temperature; and it involved 914 datapoints, which were extracted out of 75 papers (out of ∼1360 initially screened). Among distinct parameters, initial adsorbate concentration was found to be the most dominant factor affecting the MB uptake. Concordantly, pH of the solution medium, raw material selection, and modification types were also found to be significant in MB adsorption. Results showed that in terms of raw material and modification types, sepiolite and chemical (acid and/or alkaline modification) and thermal treatments, respectively, come forward as the most powerful candidates for enhanced MB adsorption performance. Modifications applied on adsorbents should be evaluated separately, as there is no general rule applicable for all experimental conditions, and the strength of the contribution of modification type also depends on initial adsorbate concentration. Implementation of various imputation methods showed the importance of reporting experimental factors, such as surface area, in the literature. Range of applicability of the suggested modeling procedure was assessed to help experimenters in testing MB uptake under novel experimental conditions.

A novel alkali activated magnesium silicate as an effective and mechanically strong adsorbent for methylene blue removal.

A novel, cheap, and easy-to-synthesize sepiolite-based alkali-activated material (Sep-AAM), synthesized by the reaction of a magnesium silicate source, sepiolite, with sodium silicate solution, demonstrating high mechanical strength and methylene blue (MB) removal performance is introduced. Kinetics data indicated that MB adsorption occurs through pseudo-second-order adsorption kinetics model, while the Langmuir isotherm model provided a better fit to adsorption isotherms. The Sep-AAM provided a removal capacity of 99.92 mg g-1 at 50 °C, setting a new benchmark value among the materials used for this purpose. Thermodynamical parameters indicated that the adsorption of MB onto Sep-AAM was endothermic and the interaction between Sep-AAM and MB included weak chemical bonding. Regenerability of the Sep-AAM in powder and monolith forms was confirmed up to four-cycles. Structural parameters determined by several characterization tools demonstrated that the surface hydroxyl groups are responsible for the superior MB adsorption performance. The mechanical strength measurements showed that Sep-AAM in monolith form displayed a remarkable compressive strength value of 40 MPa. To establish a new approach forward on the development of AAMs for wastewater treatment, this study shows that sepiolite can effectively be utilized and Sep-AAM provides a sustainable solution for dye removal with advanced mechanical properties.