Mecanismos químicos e mineralógicos de transformação da magnesioferrita de solo derivado de tufito, da região do Alto Paranaíba, MG

Magnetic soils forming on tuffite of the region of Alto Paranaíba, Minas Gerais, Brazil, usually contain iron-rich spinels exceptionally rich in magnesium and titanium. In this work, samples of the magnetically separated portion from the sand fraction of a Brunizém (Chernossolo) and from its mother-rock material were analyzed with synchrotron X-ray diffraction and 57Fe-Mössbauer spectroscopy. Magnesioferite (MgFe2O4) and maghemite (its pure non-stoichiometric spinel structure, Fe8/3 ⊕ 1/3 O4, where ⊕ = cation vacancy, corresponds to γFe2O3) were the magnetic iron oxides so identified. Basing on these data, a consistent chemical-mineralogical model is proposed for the main transformation steps involving these iron oxides in the pedosystem, starting on magnesioferrite to finally render hematite (αFe2O3), passing through maghemite as an intermediate specie

Iron oxides of soils formed on gneiss of the Bação Complex geodomain, Quadrilátero Ferrífero, Minas Gerais, Brazil

O objetivo deste trabalho foi efetuar a caracterização mineralógica dos óxidos de ferro de horizontes B de três perfis de solos desenvolvidos sobre gnaisse do geodomínio do Complexo Bação, no Quadrilátero Ferrífero, em Minas Gerais. As amostras foram coletadas ao longo dos segmentos de alta, média e baixa vertente. As frações de terra fina (diâmetro médio, f = 2 mm) foram separadas, em todas as amostras. A composição química dos elementos maiores foi determinada por meio da técnica de fluorescência de raios X; a análise mineralógica foi realizada com difratometria de raios X e espectroscopia Mössbauer. Todas as amostras têm composição mineralógica similar, cuja ocorrência geral corresponde à seqüência quartzo >> gibbsita > caulinita > goethita. Os resultados Mössbauer a 4,2 K confirmam a coexistência de goethita (majoritária) e hematita. Os conteúdos de alumínio isomórfico foram deduzidos dos valores de campos hiperfinos e correspondem às seguintes fórmulas químicas das goethitas: aFe0,79Al0,21OOH (alta vertente), aFe0,75Al0,25OOH (meia vertente) e aFe0,78Al0,22OOH (baixa vertente). A dinâmica de transformação dos óxidos de ferro nos horizontes B ao longo da vertente é um indicador das oscilações paleoclimáticas na área: goethita mais aluminosa é um indicador do paleoambiente úmido, e goethita menos aluminosa revela condições pedogênicas mais secas.The objective of this work was to characterize iron oxides from B-horizons of three soil profiles developing on gneiss of the Bação Complex geodomain in the Quadrilátero Ferrífero, Minas Gerais, Brazil. Samples were collected from the uppest, middle and lowest segments along the slope. The earth fine fractions (mean diameter, f = 2 mm) were separated for all samples. The chemical composition of the major elements was determined with the X-ray fluorescence technique; the mineralogical analysis was performed with powder X-ray diffractomer and Mössbauer spectroscopy. All samples have similar mineralogical composition, with a general occurrence corresponding to the sequence quartz >> gibbsite > kaolinite > goethite. From the 4.2 K-Mössbauer results, the coexistence of goethite (major) and hematite is confirmed. The isomorphic aluminum contents, as they were deduced from the hyperfine fields, lead to the following chemical formulas for goethites: aFe0.79Al0.21OOH (upslope), aFe0.75Al0.25OOH (midslope) and aFe0.78Al0.22OOH (downslope). The iron oxides transformation dynamics in B horizons along the slope is a useful indicator of the paleo-climatic oscillations in this area: aluminous goethite is an indicator of humid paleo-environments, whereas aluminous-poorer goethite reveals drier pedogenic conditions

Extensão universitária: contribuições de palestras temáticas no processo formativo de professores de Química

Este trabalho tem como objetivo desenvolver atividades com os licenciandos em Química, tendo em vista aproximação com outros setores sociais, bem como outros espaços acadêmicos, tendo em vista uma compreensão da responsabilidade e compromisso social de sua profissão. A equipe organizadora foi constituída por seis discentes dos cursos de Química, sendo realizado o levantamento dos interesses dos licenciandos, por meio de um formulário online. Também foram realizadas entrevistas semiestruturadas com os docentes palestrantes e os discentes que assistiram às palestras. Além disso, também integrou o corpus da análise, os relatos dos discentes executores das atividades. Os resultados apontaram que o macrotema de maior interesse dos licenciandos foi “ciência, tecnologia e inovação” (24%), enquanto o macrotema “processos de globalização e política internacional” (0%) não despertou o interesse do licenciandos. A partir disso, foram organizadas oito palestras, presencial e remotamente

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

Magnesioferrita e caminho pedogenético de transformação de óxidos de ferro magnéticos em dois perfis de solo derivados de tufito da região do Alto Paranaíba (MG)

Solos magnéticos derivados de tufito da região do Alto Paranaíba (MG) têm mineralogia bastante variável, mas são relativamente ricos em óxidos de Fe isoestruturais ao espinélio, mais especificamente: (Ti, Mg)-magnetita e (Ti, Mg)-maghemita. No presente trabalho, foram estudados os concentrados magnéticos (magnetização de saturação, 34,4 < sigma/J T-1 kg-1 < 43,7) da fração areia de pedomateriais de um Brunizém (Chernossolo) (amostras AP31CR; AP31B e AP31A) e de um Chernossolo Léptico (AP33CR e AP33A) derivados de tufito, coletados no município de Patos de Minas (MG). Foram identificadas, por difratometria de raios X (método do pó) e espectroscopia Mössbauer do 57Fe, a 298 K e a 110 K, maghemita (gamaFe2O3) e hematita (alfaFe2O3) e uma inédita magnesioferrita (fórmula ideal, MgFe2O4), inédita em solos do Brasil, nas frações minerais desses materiais magnéticos. Observou-se, também, que as proporções ponderais e os tamanhos de partículas dos óxidos de Fe variam progressivamente ao longo dos perfis estudados. Os diâmetros médios estimados dos cristalitos de magnesioferrita variam progressivamente, em cada perfil: 27 nm (concentrado magnético da amostra AP31CR); 25 nm (AP31B); 23 nm (AP31A); 48 nm (AP33CR) e 32 nm (AP33A). A proporção de Al isomorficamente substituinte na hematita aumenta sistematicamente de 5 a 13 mol %, da base para o topo do perfil AP31, e tende a se manter constante, em torno de 9 mol %, no perfil AP33. Propõe-se um modelo geral de transformação envolvendo somente óxidos de Fe, em que magnesioferrita é o precursor pedogenético da maghemita, até hematita, nesses pedossistemas: MgFe2O4 -> gFe2O3 -> aFe2O3