Separation of the glycerol-biodiesel phases in an ethyl transesterification synthetic route using water

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Biodiesel is obtained by the transesterification of vegetable oil (or fat) and alcohol, with methanol being the most used alcohol. Methanol can be replaced by ethanol; however, this alcohol acts as a surfactant in the reaction mixture, promoting a stable dispersion of the glycerol in biodiesel, which hinders the separation of the glycerol-biodiesel phases. In this study, it was found that the addition of 1% v/v water relative to the total volume of the reaction mixture expedites the separation of the phases by interrupting the emulsifying action of ethanol with an immediate separation of glycerol from biodiesel. The characterization of the produced biodiesels was performed using hydrogen nuclear magnetic resonance (H-1 NMR) and gas chromatography (GC). H-1 NMR indicated a 96.9% conversion of triglycerides to biodiesel. The fatty acid compositions of the synthesized ethyl and methyl biodiesels determined using GC are essentially the same.Biodiesel is obtained by the transesterification of vegetable oil (or fat) and alcohol, with methanol being the most used alcohol. Methanol can be replaced by ethanolhowever, this alcohol acts as a surfactant in the reaction mixture, promoting a stabl26917451750FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)sem informaçãosem informaçãoThe authors are grateful to Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, for financial suppor

Influence of water and ultraviolet irradiation on the induction period of the oxidation of biodiesel

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Biodiesel degrades due to oxidative processes, causing a decrease in its quality. In the present work, it has been clearly shown that the incidence of ultraviolet radiation on biodiesels obtained from soy, canola, linseed and microalgae oils initiate oxidative processes which lead to the decrease in the induction period (IP) of the fuel. The influence of the residual water content of the same biodiesels on the oxidation process was also investigated with and without the incidence of ultraviolet radiation. Between 190 and 850 ppm of water in the biodiesel and without UV irradiation, no significant change in the IP values was observed under the experimental conditions.Biodiesel degrades due to oxidative processes, causing a decrease in its quality. In the present work, it has been clearly shown that the incidence of ultraviolet radiation on biodiesels obtained from soy, canola, linseed and microalgae oils initiate oxid284676680FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)sem informaçãosem informaçãoThe authors are grateful to CNPq for financial support and to Dr. Fabio Batista (EXTRAE-UNICAMP) who graciously provided the microalgae oi

Binary blends of biodiesel from macauba (acromia aculeata) kernel oil with other biodiesels

CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOMacauba is a palm tree which provides large amount of oil. Particularly its oil from the kernel presents excellent stability to oxidation as it is mainly constituted by saturated organic chains. This stability is inherited by the biodiesel prepared with t292240247CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO404808/2013-1The authors thank the Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq (404808/2013-1) for the financial support. The authors would also like to thank Gustavo Giraldi Shimamoto for monitoring the biodiesel synthesis reactio

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