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.
View Article and Find Full Text PDFControlling the selectivity of CO 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 CO hydrogenation reaction cycle from 100 to 800 °C. The usually high CH formation was suppressed leading to full selectivity toward CO.
View Article and Find Full Text PDFACS Appl Mater Interfaces
December 2020
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 () and the shear modulus () 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 and based on compositional features.
View Article and Find Full Text PDFMetallic 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.
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