A family of catechol-based submicron particles, with sizes between 200 and 300 nm, was tested for the removal of Cd(II), Pb(II) and Cr(VI) in water. The highest adsorption capacity was obtained with catechol-based particles in the case of Pb(II), followed by Cd(II). However, the catechol particles failed to adsorb Cr(VI). Our results indicate an up to four-fold increase of the adsorption capacity of these particles compared to that of activated carbon under the same experimental conditions. To check the biocompatible character of the submicron particles, their stability was evaluated in a phosphate buffer solution (PBS) and in a cell culture medium. The results confirmed that the presence of proteins in the medium favors their stability. A bioluminescent Vibrio fischeri test and a cytotoxicity assay on the HepG2 cell line were used to determine that the catechol particles did not exhibit any substantial toxicity. The results show that these catechol-based particles can be used as an efficient biocompatible adsorbent to remove heavy metals at extremely low concentrations.

In arid and semiarid regions, groundwater becomes the main source to meet the drinking water needs of large cities, food production, and industrial activities. For this reason, necessary studies must be carried out to estimate its quantity and quality, always seeking sustainable management, thus avoiding social conflicts or a decrease in the productive activities of humanity. This research explains the behavior of groundwater quality concerning arsenic speciation and its relationship with fluoride. The average total arsenic concentration of 19.95 µg/L and 20.29 µg/L is reported for the study period from 2015 to 2020, respectively, according to the Mexican standard. If the population drinks water directly, it is exposed to possible damage to health. The predominant arsenic species is As (V), with 95% and As (III) with 5%, this finding will allow us to define in greater detail the type of remediation that is required to reduce the content of this element in the water. Regarding the relationship between arsenic and fluorine, very small Pearson correlation coefficients of the order of 0.3241 and 0.3186 were found. The estimation of the space–time variation made it possible to identify the areas with the highest concentration of arsenic and fluorine, allowing the definition of the operating policies of these wells, thereby protecting the health of the inhabitants who consume this water.

The automotive industry is entering a digital revolution, driven by the need to develop new products in less time that are high-quality and environmentally friendly. A proper manufacturing process influences the performance of the door grommet during its lifetime. In this work, uniaxial tensile tests based on molecular dynamics simulations have been performed on an ethylene–propylene–diene monomer (EPDM) material to investigate the effect of the crosslink density and its variation with temperature. The Mooney–Rivlin (MR) model is used to fit the results of molecular dynamics (MD) simulations in this paper and an exponential-type model is proposed to calculate the parameters $C_1\left(T\right)$ and $C_2\left(T\right)$. The experimental results, confirmed by hardness tests of the cured part according to ASTM 1415-88, show that the free volume fraction and the crosslink density have a significant effect on the stiffness of the EPDM material in a deformed state. The results of molecular dynamics superposition on the MR model agree reasonably well with the macroscopically observed mechanical behavior and tensile stress of the EPDM at the molecular level. This work allows the accurate characterization of the stress–strain behavior of rubber-like materials subjected to deformation and can provide valuable information for their widespread application in the injection molding industry.

The quantity and quality of the supply of fresh water to households, commercial areas, small industries, green spaces irrigation and public and private institutions in large cities face challenges from the supply sources availability and suitable distribution network performance to the full satisfaction of the established drinking water guidelines. In Mexico, the main source of water comes from groundwater. Most of the Mexican aquifers are located in arid and semi-arid weather conditions. The groundwater’s physical–chemical properties are closely related to geology. This study was carried out at the north-central part of the country in which igneous and sedimentary rocks predominate, with high calcium carbonate (CaCO₃) concentrations. The accumulation of CaCO₃ in the pipelines is also known as scale deposit that decreases the fluid flow, causing a deficiency in the water supply. The main objectives of this study were determining the physical–chemical groundwater parameters and saturation indexes injected into the drinking water networks and characterizing the scale deposits by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The results indicate that the scale deposits are mainly calcium carbonate and silica oxide crystals, caused by the water aggressiveness according to the saturation indexes and the lack of control over the saturation pH.

Rainfall is one of the most important meteorological phenomena since it provides water to the Earth's surface, which has a significant impact on the daily life of human beings. Gaining the knowledge of its behavior in a semi-arid basin is an important and challenging task to take advantage of this natural resource, given that water is usually scarce in such regions. Artificial intelligence and machine learning algorithms help to identify rainfall patterns and trends within a region. Multiple linear regression, random forest (RF), support vector machine, and artificial neural network (ANN) algorithms were implemented using daily rainfall data from climatological stations located within the basin using one station as a predictor variable and the remaining ones as input variables. The metrics to evaluate the model were the coefficient of determination (R2), mean absolute error, root mean square error, and the Kling–Gupta efficiency coefficient. The results showed that the daily rainfall prediction is better individually than overall, finding that the models obtained by RF and ANN simulate better daily rainfall in the basin.

The rubber industry is evolving by incorporating innovative tools to improve production processes. A proper manufacturing process determines the behavior and service life of the resulting products. In this research, molecular dynamics simulations were used to study the effect of temperature in the cured structure on the resulting mechanical properties of EPDM. The results of the simulations at different temperatures of the crosslinked ethylene–propylene–diene monomer (EPDM) were then compared in terms of the radius of gyration, free volume, root mean square displacement, stress curves, viscosity, and gel point. Then, using the superposition principle, viscosity and tensile stress were evaluated. The molecular dynamics superposition results could reasonably predict the mechanical behavior of EPDM during and after the injection process. The results provide new insights into the molecular-level crosslinking mechanisms of amorphous polymers and their influence on mechanical behavior, which facilitates the design of the injection process for rubber component applications. The results show an increase in viscosity and a decrease in the critical gel point with increasing temperature. The hardness tests performed on an automotive component demonstrate that this has an impact on the resulting properties.