RESUMO

We assessed microplastic (µP) pollution in water and sediment samples during the dry and rainy season (October/2018 and March/2019, respectively) from the Guarapiranga Reservoir in the Metropolitan Region of São Paulo, Brazil, which provides drinking water for up to 5.2 million people. The concentration of mPs varied spatially and seasonally, with the higher concentrations observed near the urbanized areas and during the dry season. Water column concentrations ranged from 150 to 3100 particles/m3 and 0.07-25.05 mm3 plastic/m3 water during the dry season, and 70-7900 particles/m3 and 0.06-4.57 mm3 plastic/m3 water during the rainy season. Sediment samples were collected only during the rainy season, with concentrations ranging from 210 to 22,999 particles/kg dry weight and 0.15-111.46 mm3/kg dry weight. The particle size distribution exhibited seasonal variation, with µPs >1 mm predominating during the dry season, constituting 60-75% of all particles. In terms of quantity, fibers accounted for the majority of microplastics, comprising 55-95% during the dry season and 70-92% during the rainy season. However, when considering particle volume, irregular particles dominated in some samples, accounting for up to 95% of the total amount. The predominant colors of microplastics were white/crystal, black, and blue, with the main compositions identified as polypropylene (PP) and polyethylene terephthalate (PET), suggesting the influence of untreated domestic sewage discharge. Additionally, some additives were detected, including the pigments Fast RED ITR and phthalocyanine blue. The management of reservoir water levels appears to influence the quantity of µPs in the water column. As the water level increases up to 90% of the reservoir capacity during the rainy season, the amount of µPs in the water decreases, despite the higher influx of particles resulting from surface runoff caused by rainy conditions. This suggests a "dilution" effect combined to the polymictic mixing hydrodynamics. Our results may contribute to the creation and improvement of monitoring programs regarding mP pollution and to the adoption of specific public policies, which are still lacking in legislation.

Assuntos

RESUMO

Controlling the selectivity of CO2 hydrogenation catalysts is a fundamental challenge. In this study, the selectivity of supported Ni catalysts prepared by the traditional impregnation method was found to change after a first CO2 hydrogenation reaction cycle from 100 to 800 °C. The usually high CH4 formation was suppressed leading to full selectivity toward CO. This behavior was also observed after the catalyst was treated under methane or propane atmospheres at elevated temperatures. In situ spectroscopic studies revealed that the accumulation of carbon species on the catalyst surface at high temperatures leads to a nickel carbide-like phase. The catalyst regains its high selectivity to CH4 production after carbon depletion from the surface of the Ni particles by oxidation. However, the selectivity readily shifts back toward CO formation after exposing the catalysts to a new temperature-programmed CO2 hydrogenation cycle. The fraction of weakly adsorbed CO species increases on the carbide-like surface when compared to a clean nickel surface, explaining the higher selectivity to CO. This easy protocol of changing the surface of a common Ni catalyst to gain selectivity represents an important step for the commercial use of CO2 hydrogenation to CO processes toward high-added-value products.

RESUMO

The discovery of low-modulus Ti alloys for biomedical applications is challenging due to a vast number of compositions and available solute contents. In this work, machine learning (ML) methods are employed for the prediction of the bulk modulus (K) and the shear modulus (G) of optimized ternary alloys. As a starting point, the elasticity data of more than 1800 compounds from the Materials Project fed linear models, random forest regressors, and artificial neural networks (NN), with the aims of training predictive models for K and G based on compositional features. The models were then used to predict the resultant Young modulus (E) for all possible compositions in the Ti-Nb-Zr system, with variations in the composition of 2 at. %. Random forest (RF) predictions of E deviate from the NN predictions by less than 4 GPa, which is within the expected variance from the ML training phase. RF regressors seem to generate the most reliable models, given the selected target variables and descriptors. Optimal compositions identified by the ML models were later investigated with the aid of special quasi-random structures (SQSs) and density functional theory (DFT). According to a combined analysis, alloys with 22 Zr (at. %) are promising structural materials to the biomedical field, given their low elastic modulus and elevated beta-phase stability. In alloys with Nb content higher than 14.8 (at. %), the beta phase has lower energy than omega, which may be enough to avoid the formation of omega, a high-modulus phase, during manufacturing.

RESUMO

Metallic nanoalloys are essential because of the synergistic effects rather than the merely additive effects of the metal components. Nanoscience is currently able to produce one-atom-thick linear atomic chains (LACs), and the NiAl(110) surface is a well-tested template used to build them. We report the first study based on ab initio density functional theory methods of one-dimensional transition-metal (TM) nanoalloys (i.e., LACs) grown on the NiAl(110) surface. This is a comprehensive and detailed computational study of the effect of alloying groups 10 and 11 metals (Pd, Pt, Cu, Ag, and Au) in LACs supported on the NiAl(110) surfaces to elucidate the structural, energetic, and electronic properties. From the TM series studied here, Pt appears to be an energy-stabilization species; meanwhile, Ag has a contrasting behavior. The work function changes because the alloying in LACs was satisfactorily explained from the explicit surface dipole moment calculations using an ab initio calculation-based approach, which captured the electron density redistribution upon building the LAC.