This paper reports a novel approach in the study of trace-element mobility during the argillization of volcanic glass that is based on in situ laser ablation inductively coupled plasma mass spectrometry glass analyses and that of spatially related illite-smectite collected in the form of fraction separates. The material studied originates from lacustrine sediments of the Dinaride Lake System that bear evidence of intensive weathering of distal tephra during the Miocene climatic optimum. Yttrium and HREE were probably mobilized from decomposing glass in the form of carbonate complexes and were consequently depleted significantly in the clays studied. On the other hand, the Mg-rich illite-smectite demonstrates an elevated adsorption potential of solvated LREE complexes. This may be explained through clay surface geochemistry controlled largely by Mg for Al octahedral substitution. This paper highlights the role of eogenetic 2:1 clay aluminosilicates that, under favourable geological conditions, may be conducive to secondary REE enrichment and the formation of potential ion adsorption-type deposits.

Two novel modified montmorillonite (Mnt) components were prepared using Mnt nanoparticles and two surfactants: docosyl-trimethylammonium chloride (BTAC) and sodium dodecyl sulfate (SDS). These modified Mnts were used to remove a carcinogenic and harmful dye, crystal violet (CV), from solution. Optimization and modelling studies of the adsorption of these two modified Mnts were performed using response surface methodology. Four influential variables (concentration of adsorbent, temperature, pH and CV concentration) were studied to obtain the optimum conditions for CV removal. The optimal values of these variables for the two modified Mnts yielded 100% dye-removal efficiency. The optimum conditions for CV adsorption on Mnt-BTAC and Mnt-BTAC-SDS, respectively, are temperatures of 25.00 and 33.29°C, pH values of 9 and 10.1, CV concentrations of 50.00 and 10.44 mg L–1 and adsorbent concentrations of 1.00 and 0.98 g L–1. In equilibrium studies of the two modified Mnts, the Temkin isotherm was selected as an appropriate model, and in kinetic studies of these Mnts, the fractal-like integrated kinetics Langmuir model was found to be the best model. The Mnt-BTAC-SDS component is an affordable adsorbent with high adsorption capacity for CV.

The worldwide use of pharmaceuticals is of concern to those researchers who develop new techniques for the removal of these compounds from the aquatic medium. The objective of the present work was to characterize and evaluate the performance of a commercial, bentonite-based organophilic clay in removing ivermectin from aqueous solution. The adsorbent was characterized by nitrogen physisorption, thermogravimetric-differential scanning calorimetry (TG-DSC), X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR). Batch-scale adsorption experiments were performed to evaluate the kinetics, isotherms, thermodynamics and effect of pH on removal of this drug and reuse of the clay. The bentonite has a small specific surface area with an irregular surface. The Elovich kinetic model fits the experimental data better than other models, indicating that chemisorption contributes to drug removal in this case. The Langmuir and Sips isothermal models best fit the experimental equilibrium data. The process was shown to be favorable (ΔG°ads<0), endothermic (ΔH°ads>0), with an increase in the degrees of freedom at the solid–liquid interface (ΔS°ads>0), and with characteristics of a physical-chemical adsorption process (Ea = 11.065 kJ mol–1) under the study conditions. Adsorption was favored at the natural pH of the solution and the organophilic clay could be regenerated with water and reused in consecutive adsorption cycles. The amount of ivermectin adsorbed on the organophilic clay ranged from 1.78 to 3.88 mg g–1. The organophilic clay was shown to be a cost-effective potential adsorbent for ivermectin-contaminated water-treatment applications.

Mineral reactions in soils demonstrably take place on a human timescale. The weathering of silicate ‘rock-forming’ minerals releases nutrients that are essential for plant growth, including silica. This process consumes CO2, which is ultimately derived from the atmosphere, through enhanced rock weathering. From a human perspective, the weathering process has two beneficial functions – crop nutrition and climate mitigation – through the removal of atmospheric CO2. By considering these as a coupled process, the release of silica during weathering can be matched to what is taken from the soil by a crop (e.g. wheat). A simple analysis shows that the amount of silica that accumulates in wheat during a 4 month growing period is readily released by the weathering of pyroxene and plagioclase, minerals that commonly occur in basaltic igneous rocks. In contrast, the dissolution rate for quartz is so low that it cannot supply the silica taken up by the crop and is inert. Similarly, dissolution of clay minerals releases sufficient silica for plant uptake. Rapid weathering of silicate minerals within soils is evident from images of surfaces of grains exposed in soils for periods of 10–100 years. The evidence for silicate rock weathering as part of the soil system that sustains humanity is provided by the vegetation that we see around us.

This work studied the efficiency of Tamazert kaolinite clay for adsorbing the cationic dye crystal violet from an aqueous solution in a batch system. The kinetics of the process and the equilibrium of adsorption were studied using non-linear models. The characterization of Tamazert kaolinite clay showed that it has structural, textural and surface properties that are suitable for adsorption. The effects of various process parameters such as contact time, initial dye concentration, initial pH, adsorbent dose and temperature were tested. The kinetic study using non-linear regression showed that the pseudo-second order model best fitted the experimental data. The intra-particle model was also used to estimate the contribution of intra-particle diffusion to this process. The adsorption isotherms were fitted to Freundlich, Langmuir and Redlich–Peterson models, showing that the adsorption is limited to a monolayer with a monolayer adsorption capacity of 44.2 mg g–1. The thermodynamic study indicated that the process is exothermic, spontaneous and accompanied by a decrease in entropy.

Clay minerals are essential components of soil systems and understanding their role in soil structure and function is critical for soil environmental quality management and sustainable agricultural development. An in-depth study of clay minerals and the development of related materials is essential for a complete understanding and effective management of soil systems. This review is a detailed compilation of relevant studies over the past decade in this area, focusing on an overview of clay minerals and their modified materials and their regulation of soil structure and function. We focus on the direct influence of clay minerals on the physical, chemical and biological properties of soils, such as soil structure, soil fertility, plant growth, soil microbial activity and soil carbon sequestration. Finally, we concluded by summarizing the existing issues with clay mineral materials in soil improvement and by outlining potential future development trends and strategies.

The treatment of water containing heavy metals has attracted increasing attention because the ingestion of such water poses risks to human health. Due to their relatively large specific surface areas and surface charges, clay minerals play a significant role in the adsorption of heavy metals in water. However, the major factors that influence the adsorption rates of clay minerals are not well understood, and thus methods to predict the sorption of heavy metals by clay minerals are lacking. A method that can identify the most appropriate clay minerals for removal of a given heavy metal, based on the predicted sorption of the clay minerals, is required. This paper presents a widely applicable deep learning neural network approach that yielded excellent predictions of the influence of the sorption ratio on the adsorption of heavy metals by clay minerals. The neural network model was based on datasets of heavy-metal parameters that are available generally. It yielded highly accurate predictions of the adsorption rate based on training data from the dataset and was able to account for a wide range of input parameters. A Pearson sensitivity analysis was used to determine the contributions of individual input parameters to the adsorption rates predicted by the neural network. This newly developed method can predict the major factors influencing heavy-metal adsorption rates. The model described here could be applied in a wide range of scenarios.