63 research outputs found

    Chitosan/mangiferin particles for Cr(VI) reduction and removal

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    AbstractIn this work, chitosan/mangiferin particles (CMP) were prepared by spray-drying technique and characterized by SEM, DLS, FTIR, HPLC–UV and adsorption studies to investigate a possible application as a preventive material in cases of human and animal contamination with Cr(VI). CMP presented sizes ranging from nano to micrometers. Chitosan and mangiferin (MA) presence in the powder was confirmed by FTIR and MA quantification (136μg/mg) was performed using a calibration curve prepared by HPLC–UV. Adsorption capacity of Cr(VI) onto CMP was compared with chitosan and investigated in a batch system by considering the effects of various parameters like contact time, initial concentration of adsorbent and pH. Cr(VI) removal is pH dependent and it was found to be maximum at pH 5.0. The results showed that CMP has a potential application as a preventive material in cases of human or animal contamination with Cr(VI)

    Pervasive gaps in Amazonian ecological research

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    Pervasive gaps in Amazonian ecological research

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    Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 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 or ganism 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 ne glected 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 lostinfo:eu-repo/semantics/publishedVersio

    National identity predicts public health support during a global pandemic (vol 13, 517, 2022) : National identity predicts public health support during a global pandemic (Nature Communications, (2022), 13, 1, (517), 10.1038/s41467-021-27668-9)

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    Publisher Copyright: © The Author(s) 2022.In this article the author name ‘Agustin Ibanez’ was incorrectly written as ‘Augustin Ibanez’. The original article has been corrected.Peer reviewe
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