Influência do extensor de cadeia nas propriedades reológicas de Blendas de Polímeros Biodegradáveis PLA/PBAT / Influence of the chain extender on the rheological properties of PLA/PBAT Biodegradable Polymer Blends

O objetivo do presente trabalho foi estudar a influência do extensor de cadeia, Joncryl ADR 4368, no comportamento reológico de um sistema imiscível formado por uma blenda de polímeros biodegradáveis PLA/PBAT.  As composições selecionadas foram de 0%, 80% e 100% de PLA em peso nomeadas como: PBAT, 80PLA e PLA, e para a utilização do extensor de cadeia foram utilizadas formulações com 0,3% e 0,6% em peso, respectivamente. Foram determinadas as propriedades viscoelásticas em regime dinâmico oscilatório (RDO) do PLA e do PBAT bem como de suas blendas, com e sem extensor de cadeia, por reometria em regime cisalhamento dinâmico oscilatório. Por fim, aplicando-se o modelo de Palierne foi possível prever o módulo complexo do sistema PLA/PBAT estimando o tamanho da fase dispersa

Pervasive gaps in Amazonian ecological research

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

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 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,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 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

Morphology and thermal properties of poly(3-hydroxybutyrate-co-3-hydroxyvalerate)/attapulgite nanocomposites

Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) - PHBV is a biodegradable polyester which has been studied as an option for the production of disposable goods. Attapulgite is a fibrous clay mineral. The aim of this work was to produce and characterize renewable resource derived-nanocomposites based on PHBV and organophilic attapulgite (MAT). The nanocomposites were characterized by XRD, SEM and thermal analysis. It was observed reduction in degree of crystallinity, in melting and glass transition temperatures and in thermal stability of polymer due to the addition of clay to PHBV matrix. The best results were obtained for PHBV films containing 3 and 5% MAT. These films presented a slight increase in processing window and decrease in crystalline temperature and in degree of crystallinity as compared to pure PHBV

The Omicron Lineages BA.1 and BA.2 (<i>Betacoronavirus</i> SARS-CoV-2) Have Repeatedly Entered Brazil through a Single Dispersal Hub

Brazil currently ranks second in absolute deaths by COVID-19, even though most of its population has completed the vaccination protocol. With the introduction of Omicron in late 2021, the number of COVID-19 cases soared once again in the country. We investigated in this work how lineages BA.1 and BA.2 entered and spread in the country by sequencing 2173 new SARS-CoV-2 genomes collected between October 2021 and April 2022 and analyzing them in addition to more than 18,000 publicly available sequences with phylodynamic methods. We registered that Omicron was present in Brazil as early as 16 November 2021 and by January 2022 was already more than 99% of samples. More importantly, we detected that Omicron has been mostly imported through the state of São Paulo, which in turn dispersed the lineages to other states and regions of Brazil. This knowledge can be used to implement more efficient non-pharmaceutical interventions against the introduction of new SARS-CoV variants focused on surveillance of airports and ground transportation