Hydrolysis of cellulose using heteropoly salts derivatives from H3PW12O40 with different redox properties as catalysts

Heteropoly salts containing different numbers of vanadium atoms (K4[PVW11O40] - KPWV1 andK6[PV3W9O40] - KPWV3) were synthesized from the heteropoly acid H3PW12O40 (HPW), and used as catalystsin hydrolysis of cellulose reactions in order to change the redox properties and verify whether the clusterof catalysts are involved in mechanism reaction. The hydrolysis reactions following a full 23 factorialdesign with the variables: mass ratio (catalyst/substrate), reaction time and temperature. The variables evaluatedwere significant at a 90% confidence level including second and third order interactions. According tothe conducted experiments, the catalysts were all active in hydrolysis. The best results occurred when HPWwas used suggesting that the redox properties did not have much influence in depolymerization of celluloseand the hydrolysis mechanism are assigned to acidic properties of the medium. The main products obtainedfrom the reactions were glucose and HMF, which are products of great interest in the chemical industry

Carvão ativado a partir de resíduos de bambu (Bambusa vulgaris) utilizando CO2 como agente ativante para adsorção de azul de metileno e fenol

Bamboo (Bambusa vulgaris) waste were used as a raw material for producing activated carbon (AC). The materials were collected and turned into AC by carbonization (500 °C, 1.67 °C.min-1, 60 min) and activation (800 °C, 10 °C.min-1, 60 min) processes with CO2 (100 mL.min-1). The obtained material (CO2 AC) was characterized by its yield, elemental analysis, ash content, methylene blue and iodine indexes, surface area (SBET), Boehm titration method, scanning electron microscopy and used as adsorbent for removing the methylene blue and phenol contaminants. The Langmuir and Freundlich isotherms models were selected for understanding the adsorption process. CO2 AC produced showed yield of 21.6%, carbon content of 82.13% and SBET of 856.78 m2.g-1, presenting rapid removal and high adsorption capacity for methylene blue (298.82 mg.g-1) and phenol (558.29 mg.g-1).Resíduos de bambu (Bambusa vulgaris) foram utilizados como matéria-prima para a produção de carvão ativado (CA). Os materiais foram coletados e transformados em CA mediante processos de carbonização (500ºC, 1,67 ºC min-1, 60 min) e ativação (800 ºC, 10 ºC min-1, 60 min) com CO2 (100 mL min-1). O material obtido (CA CO2) foi caracterizado pelo seu rendimento, análise elementar, teor de cinzas, índices de azul de metileno e de iodo, área superficial (SBET), método titulométrico de Boehm, microscopia eletrônica de varredura e utilizado como adsorvente para remoção dos contaminantes azul de metileno e fenol. Os modelos de isotermas de Langmuir e de Freundlich foram selecionados para entender o processo de adsorção. O CA CO2 produzido apresentou rendimento de 21,6%, teor de carbono de 82,13% e SBET de 856,78 m2 g-1, apresentando uma remoção rápida e elevada capacidade de adsorção para o azul de metileno (298,82 mg g-1) e fenol (558,29 mg g-1)

Produção, caracterização e avaliação da capacidade adsortiva de carvões ativados em forma de briquete

Carvões ativados em forma de briquetes foram preparados a partir do resíduo da madeira de candeia, gerado no processo de extração do óleo α-bisabolol. O material foi briquetado, carbonizado e ativado com CO2, variando-se a temperatura (700-900°C) e o tempo de ativação (1-3 horas). Para a caracterização dos carvões ativados foram realizadas análise elementar (CHN-O), análise dos grupos funcionais (titulação de Boehm), bur-off, área superficial BET estimada, índice de iodo, cálculo de área superficial com azul de metileno SAM, testes de adsorção com azul de metileno e fenol e índice de resistência mecânica. Os resultados mostraram que, com o aumento da temperatura e do tempo de ativação, ocorre aumento da área superficial, do volume de poros, da basicidade do carvão e da capacidade de adsorção de azul de metileno e de fenol.Palavras-chave:Candeia; Carvão Ativado; Briquete

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

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