Processo de obtenção de revestimento superhidrofóbico antirrefletor e produto assim obtido

Universidade Federal do Rio Grande do SulFísicaQuímicaDepositad

Influência do ciclo menstrual no monitoramento de aulas de zumba® / Influence of the menstrual cycle on monitoring of zumba® training sessions

O objetivo do presente estudo foi verificar a influência do ciclo menstrual (CM) no monitoramento de aulas de Zumba®. Participaram 8 mulheres (idade = 33,1 ± 5,2 anos; estatura = 162 ± 9 cm; massa corporal = 68,1 ± 7,2 kg) entre 18 a 45 anos de uma academia de ginástica com experiência de no mínimo 3 meses na modalidade. As voluntárias realizaram 5 aulas de Zumba® (1 aula piloto e as 4 aulas experimentais). As mesmas foram alocadas de 10 em 10 dias de maneira a contemplar todas as fases do ciclo menstrual de 21 a 35 dias (fase folicular [FF], ovulatória [FO] e lútea [FL]). Como instrumentos de monitoramento foram utilizadas a escala de percepção do bem estar (PBE), variabilidade da frequência cardíaca em repouso (VFC), percepção subjetiva de esforço (PSE), escala de afetividade (EA) e impulso de treinamento (TRIMP) pela frequência cardíaca. Os resultados demonstraram que a FL aumenta a PSE em relação a FO e FL (p<0,05, TE = 0,68). O TRIMP foi menor na fase lútea (p<0,05, TE = -0,66 a -0,62). As demais variáveis não apresentaram diferença significativa (p>0,05). Conclui-se que as fases do ciclo menstrual alteram a percepção subjetiva de esforço e carga interna de treinamento em aulas de Zumba®.

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

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Filmes superhidrofóbicos e antirrefletores em largo espectro

Revestimentos antirrefletores no vidro tem sido objeto de estudo de grande parcela da comunidade científica e tecnológica para obtenção de alta transmitância óptica em sistemas ópticos e, sobretudo para melhorar a eficiência em muitas aplicações, tais como painéis de células solares, telas de exibição de vídeo, pára-brisas de automóveis, óculos e janelas de edifícios. Mais recentemente, superhidrofobicidade tem apresentado crescente interesse para diversas áreas, tais como roupas repelentes a água, superfícies metálicas e microfluido. Neste trabalho, combinamos superhidrofobicidade e antirreflexão à autolimpeza e transparência. Essas propriedades são obtidas pela combinação em multiescala de topologia de superfície baseada na deposição de nanopartículas de sílica (SNPs), revestimentos de índice gradual e revestimentos interferométricos, utilizando politetrafluoroetileno (PTFE), em três rotas. Na primeira rota com apenas uma camada (vidro/SNPs), as amostras apresentaram um ângulo de contato (WCA) de 161°±2° com alto valor de histerese angular e pouca antireflexão. Na segunda rota com duas camadas (vidro/SNPs/PTFE), as amostras apresentaram um WCA de 169°±2° com baixo valor de histerese angular e com melhor antireflexão. Na terceira rota, composta por três camadas (vidro/SNPs/aerogel/PTFE), as amostras apresentaram um WCA de 158°±2° com baixo valor de histerese angular (<5°) e uma transmitância em incidência normal acima de 99%, com decréscimo de menos de 2% para incidência oblíqua a 20°. Estes resultados mostram a obtenção simultânea propriedades antirrefletoras e autolimpantes em vidro, devido à combinação de efeitos de revestimentos interferométricos e de índice gradual, na região do visível e do infravermelho.Anti-reflective coatings on glass have been subject of great technological and scientific attention for low-loss transmission optical systems, and particularly they enhance efficiency in many applications, such as solar panels and cells, video display screens, automobile windscreens, eyeglasses and windows of buildings. More recently, superhydrophobicity has found increasing interest for other diverse areas, such as waterrepellent clothing, metallic surfaces and microfluidics. In this work, we combine superhydrophobicity and anti-reflection with regard to self-cleaning and transparency. These properties are pursued by combination of multi-scale surface topology based on silica nanoparticles (SNPs), index grading and interference coating, as well as Polytetrafluoroethylene (PTFE) self-assembly, using three approaches. In the first, onelayer approach (glass/SNPs), the resulting samples presented water contact angle (WCA) of 161o ± 2o with high angular hysteresis and some antireflection. In the second, two-layer approach (glass/SNPs/PTFE), the resulting samples presented a WCA of 169o ± 2o with very low hysteresis, as well as significant antireflection. The third, three-layer approach (glass/SNPs/silica aerogel/PTFE), produced surfaces with WCA of 158o ± 2o with also very low hysteresis (<5o), in addition to normal transmittance of 99% or higher, which decreased less than 2% at 20o incidence. These results show that proper structure-coated glass, with a combination of interference and graded-index effects, may provide simultaneous self-cleaning and wide-angle antireflection properties, in the visible and infrared spectra

Filmes superhidrofóbicos e antirrefletores em largo espectro

Revestimentos antirrefletores no vidro tem sido objeto de estudo de grande parcela da comunidade científica e tecnológica para obtenção de alta transmitância óptica em sistemas ópticos e, sobretudo para melhorar a eficiência em muitas aplicações, tais como painéis de células solares, telas de exibição de vídeo, pára-brisas de automóveis, óculos e janelas de edifícios. Mais recentemente, superhidrofobicidade tem apresentado crescente interesse para diversas áreas, tais como roupas repelentes a água, superfícies metálicas e microfluido. Neste trabalho, combinamos superhidrofobicidade e antirreflexão à autolimpeza e transparência. Essas propriedades são obtidas pela combinação em multiescala de topologia de superfície baseada na deposição de nanopartículas de sílica (SNPs), revestimentos de índice gradual e revestimentos interferométricos, utilizando politetrafluoroetileno (PTFE), em três rotas. Na primeira rota com apenas uma camada (vidro/SNPs), as amostras apresentaram um ângulo de contato (WCA) de 161°±2° com alto valor de histerese angular e pouca antireflexão. Na segunda rota com duas camadas (vidro/SNPs/PTFE), as amostras apresentaram um WCA de 169°±2° com baixo valor de histerese angular e com melhor antireflexão. Na terceira rota, composta por três camadas (vidro/SNPs/aerogel/PTFE), as amostras apresentaram um WCA de 158°±2° com baixo valor de histerese angular (<5°) e uma transmitância em incidência normal acima de 99%, com decréscimo de menos de 2% para incidência oblíqua a 20°. Estes resultados mostram a obtenção simultânea propriedades antirrefletoras e autolimpantes em vidro, devido à combinação de efeitos de revestimentos interferométricos e de índice gradual, na região do visível e do infravermelho.Anti-reflective coatings on glass have been subject of great technological and scientific attention for low-loss transmission optical systems, and particularly they enhance efficiency in many applications, such as solar panels and cells, video display screens, automobile windscreens, eyeglasses and windows of buildings. More recently, superhydrophobicity has found increasing interest for other diverse areas, such as waterrepellent clothing, metallic surfaces and microfluidics. In this work, we combine superhydrophobicity and anti-reflection with regard to self-cleaning and transparency. These properties are pursued by combination of multi-scale surface topology based on silica nanoparticles (SNPs), index grading and interference coating, as well as Polytetrafluoroethylene (PTFE) self-assembly, using three approaches. In the first, onelayer approach (glass/SNPs), the resulting samples presented water contact angle (WCA) of 161o ± 2o with high angular hysteresis and some antireflection. In the second, two-layer approach (glass/SNPs/PTFE), the resulting samples presented a WCA of 169o ± 2o with very low hysteresis, as well as significant antireflection. The third, three-layer approach (glass/SNPs/silica aerogel/PTFE), produced surfaces with WCA of 158o ± 2o with also very low hysteresis (<5o), in addition to normal transmittance of 99% or higher, which decreased less than 2% at 20o incidence. These results show that proper structure-coated glass, with a combination of interference and graded-index effects, may provide simultaneous self-cleaning and wide-angle antireflection properties, in the visible and infrared spectra

Processo de obtenção de revestimento superhidrofóbico antirrefletor e produto assim obtido

Universidade Federal do Rio Grande do SulFísicaQuímicaDepositad