TiO2/PDMS nanocomposites for use on self-cleaning surfaces

AbstractIn this study, polydimethylsiloxane (PDMS)/TiO2 nanocomposite was processed by the spray method. TiO2 nanoparticles were synthesized by microwave-assisted hydrothermal method. Varying the proportion of nanoparticles in 0%, 0.5% and 1% by weight, commercial TiO2 (P25) was used for comparison purposes. The photocatalytic activity of nanocomposites impregnated with methylene blue was assessed by means of UV–visible spectroscopy. Changes in contact angle were analyzed before and after UV degradation tests. The effect of ultraviolet radiation on the chemical structure of the PDMS matrix was evaluated by Fourier transform infrared spectroscopy (FTIR). The results indicated that the addition of TiO2 nanoparticles in PDMS provides good photocatalytic properties in the decomposition of methylene blue, which is an important characteristic for the development of coatings for self-cleaning. For comparison purposes, commercial P25 was also used to investigate the photocatalytic activity

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost