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This study evaluated non-fibrous Mg-clays as potential adsorbents of emerging contaminants (ECs) from water. The materials were characterized, and their textural and structural properties were related to their ability to remove two model EC molecules: ciprofloxacin (CPX) and lidocaine (LID). The results showed that Ad-6 and Ad-7 are mixed-layer kerolite/stevensite, while Ad-5 and Ad-8 are mainly composed of smectite minerals like stevensite and saponite, respectively. Ad-8 exhibited the highest CPX-adsorption capacity (0.91 mmol·g−1 clay), likely due to its saponite content. Mixed-layer materials also performed well, with Ad-6 and Ad-7 achieving an adsorption capacity of 0.8 and 0.55 mmol·g−1 clay, respectively. Adsorption studies suggested that CPX is adsorbed through ion exchange in materials with high smectite content (Ad-8 and Ad-5), while interstratified materials showed enhanced retention due to kerolite presence, which improves their porous structures. Similar findings were observed for LID, indicating a cationic-exchange mechanism for LID adsorption in all the materials and suggesting that the molecular size of the EC could regulate the removal capacity of these materials. This work showed that the studied Mg-clays could be effectively used for the removal of pharmaceutical pollutants, expanding their commercial possibilities. [ABSTRACT FROM AUTHOR]

Addressing climate change requires transitioning to cleaner energy sources and adopting advanced CO2 capture techniques. Clay minerals are effective in CO2 adsorption due to their regenerative properties. Recent advancements in nanotechnology further improve their efficiency and potential for use in carbon capture and storage. This study examines the CO2 adsorption properties of montmorillonite and saponite, which are subjected to a novel microwave-assisted acid treatment to enhance their adsorption capacity. While montmorillonite shows minimal changes, saponite undergoes significant alterations. Furthermore, the addition of silica pillars to smectites results in a new nanomaterial with a higher surface area (653 m2 g−1), denoted as reversed smectite, with enhanced CO2 adsorption capabilities, potentially useful for electrochemical devices for converting captured CO2 into value-added products. [ABSTRACT FROM AUTHOR]

In the northern Patagonian region of Argentina, some of the largest bentonite deposits exist. Four bentonite samples (B1, B2, B3, and B4) from two deposits were thoroughly characterised and evaluated as potential adsorbents of anthocyanins extracted from red cabbage. Anthocyanins (ACys) are natural colourants obtained from fruits and plants and have shown excellent antioxidant properties that have promising applications in health. The adsorption of ACys onto bentonites enhances their stability relative to that observed in aqueous solutions. Material characterisation showed that three of the samples are soFdium bentonites (Na-bentonites: B2, B3, and B4), while the fourth exhibits a large quantity of magnesium and calcium in its interlayer, classifying it as a Mg-Ca-Na-bentonite (B1). Na-bentonites demonstrated higher ACy adsorption capacities, occurring through cation exchange, while Mg-Ca-Na-bentonite adsorption occurs by another type of interaction and a cooperative adsorption mechanism, resulting in the lowest ACy adsorption. The highest adsorption capacity was noted for B3, characterised by the highest smectite content, cation exchange capacity, and proportion of monovalent cations in its interlayer. Under the conditions studied, sodium smectites proved to be capable of retaining red cabbage ACys, thereby introducing a novel avenue for potential applications of regional materials with potential implications for health-related uses. [ABSTRACT FROM AUTHOR]

This paper introduces the generalized beta (GB) model as a new modeling tool in the educational assessment area and evaluation analysis, specifically. Unlike normal model, GB model allows us to capture some real characteristics of data and it is an important tool for understanding the phenomenon of learning. This paper develops a contrast with the normal model, making one to observe that there are situations in which the most common assumption is that the normality of data is not always the best. The theory of educational assessment should begin to open to new statistical tools offered and add new models in order to capture one of the best features of the data and reject strong assumptions like symmetry.