In this paper, we deal with one of the most urgent and relevant topics nowadays, i.e., water pollution. The problem is finding a valid candidate for the absorption and removal of different kinds of pollutants commonly found in water. There are already some indications about graphene oxide as a potential candidate. In the present work, we take a step forward to show how graphene nanoplatelets (rather than the oxide form of this material) are capable of decontaminating water. In this starting step, we use a specific substance as a model pollutant, i.e., acetonitrile, leaving for the future steps, to extend the analysis to additional types of pollutants. In addition to laboratory-produced graphene nanoplatelets, we already examined in the past; now we wish to consider also commercially available ones, so that the new results will not be bound to a laboratory (low technology readiness level) material, but will become interesting also from the industrial point of view, thanks to the scalability of the nanoplatelets production. For this aim, we compare the performance of two types of filters based on two classes of nanomaterials, i.e., those produced by microwave and ultrasound assisted exfoliation, already analyzed in our earlier works, with those commercially distributed by an Italian company, i.e., NANESA, http://www.nanesa.com/. The latter is an innovative SME involved in the production of graphene-based nanomaterials. We focus here in the graphene nanoplatelets, commercially available in industrial batches (GXNan grades). The present study leads to determine which filtering membrane, among the various types of commercial graphene considered, shows the greatest stability, and the lack of breakage of the membrane, concentrating on such accessory features, given that all types of graphene showed excellent adsorption properties.

An α, α^′-dipyridyl adduct of a complex compound hexaaquatribenzene-1,2,4,5-tetracarbonatotetra iron (III) with porous structure was synthesized for the first time. According to the results of elemental, X-ray, IR-spectroscopic and differential-thermal analyses the individuality, chemical formula, thermal destruction, and form of coordination of acidic anion and dipyridyl were established. During interaction of a complex compound with dipyridyl, it completely loses all crystallization molecule of water resulting in a compound with a chemical formula of Fe₄(C₆H₂(COO)₄)₃(dpy)₂(dipyridyl). Using the identification of diffraction pattern the parameters of lattice cell of the complex compound were determined.

With the deep integration of artificial intelligence technology in education, the development of AI integration capabilities among pre-service teachers—as the core of future educational human resources—has become crucial for enhancing educational quality and driving digital transformation in education. Based on the AI-TPACK (Artificial Intelligence-Technological Pedagogical Content Knowledge) theoretical framework, this study employs questionnaire surveys and structural equation modeling to explore the structural characteristics, influencing factors, and formation mechanisms of AI-TPACK competencies among pre-service teachers in Chinese universities. Findings indicate that while pre-service teachers demonstrate moderately high overall AI-TPACK levels, their technical knowledge (AI-TK) and technological integration competencies (e.g., AI-TPK, AI-TCK) remain relatively weak. School technical support, technological attitudes, and technological competence significantly influence their AI-TPACK capabilities, with institutional level and teaching experience serving as important external moderating factors. Building on these findings, this paper proposes a systematic framework for developing pre-service teachers' AI integration capabilities from a human resource development perspective. This framework encompasses four dimensions: curriculum optimization, practice enhancement, resource support, and policy guidance. It aims to provide theoretical foundations and practical pathways for pre-service teacher training and teacher human resource development in higher education institutions.

Google Earth images in the Marche Region of Central Italy revealed a circular structure consisting of a ring system made up of concentric hills and valleys. Cartography, DEM, geological, and available geophysical data were used to constrain the possible origin of the structure. Located in the Messinian foredeep deposits of the Central Apennines, it has a rim diameter of 3.75 km and a central uplift connected to its southernmost part. As it was formed in the clays of the Lower Pliocene, and clays are believed to have emerged definitively after the Upper Pliocene, its age might be constrained to the Lower Pleistocene. Similar concentric structures are usually found in impact craters, sedimentary domes, and volcanic landforms. As salt domes and magmatic activity are not found in this region, this study seeks to validate the results of previous work that it was the result of an ancient impact crater of hydrological, brachyanticline, or clayey diapiric origins. Specifically, an observed second ring portion with a curvature radius about double the first in size will be investigated in this work. This second ring portion appears to be concentric to the first one and is visible along its northern and western parts. Although double concentric rings are usually due to impact craters, the absence of the ring portion in the other two directions and the probable deviation of a river, deduced by studying hydrography, support the hypothesis that it might be of clay diapir origin.

The growth of mobile Internet has facilitated access to information by minimizing geographical barriers. For this reason, this paper forecasts the number of users, incomes, and traffic for operators with the most significant penetration in the mobile internet market in Colombia to analyze their market growth. For the forecast, the convolutional neural network (CNN) technique is used, combined with the recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit (GRU) techniques. The CNN training data corresponds to the last twelve years. The results currently show a high concentration in the market since a company has a large part of the market; however, the forecasts show a decrease in its users and revenues and the growth of part of the competition. It is also concluded that the technique with the most precision in the forecasts is CNN-GRU.