Fluorescence Spectroscopy Applied in the Identification of Lubricant Oils

In this work, we report the use of fluorescence spectroscopy to identify lubricant oils. Optical characterization was performed in four commercial lubricant oils that are used in reciprocating compressors. Mid-infrared absorption of samples indicates the presence of aromatic rings showing bands at 1605 cm-1 (C=C stretching) and 815 cm-1 (C–H stretch out of plane). UV-VIS absorption spectra show bands of di- and polyaromatic rings (around 230 nm and 260 nm, respectively). By exciting the samples at 360 nm, a broad emission band centered at 440 nm is observed, indicating that this excitation is appropriate to be used for the diagnosis of oil presence in the environment. DOI: http://dx.doi.org/10.17807/orbital.v10i1.103

EXPOENTE DE AVRAMI PELA EQUAÇÃO DE JMAK PARA MÉTODO NÃO-ISOTÉRMICO DE ANÁLISE TÉRMICA

This work reports a discussion about of the general theory for phase transformations of Melh-Johnson-Avrami-Kolmogorov in process involving non-isothermal crystallization. This model allows determine as occurs the mechanism of the nucleus formation and of growth of crystalline phases during the crystallization process. To demonstrate the validity this theory, the Avrami exponent (n) of the LiO2-TeO2-WO3 vitreous system was determined from DSC non-isothermal measurements. The obtained results indicate that the nucleation process is volumetric with two-dimensional or three-dimensional crystal growth. DOI: http://dx.doi.org/10.30609/JETI.2018-2.556

Fluorescence Spectroscopy Applied in the Identification of Lubricant Oils

In this work, we report the use of fluorescence spectroscopy to identify lubricant oils. Optical characterization was performed in four commercial lubricant oils that are used in reciprocating compressors. Mid-infrared absorption of samples indicates the presence of aromatic rings showing bands at 1605 cm-1 (C=C stretching) and 815 cm-1 (C–H stretch out of plane). UV-VIS absorption spectra show bands of di- and polyaromatic rings (around 230 nm and 260 nm, respectively). By exciting the samples at 360 nm, a broad emission band centered at 440 nm is observed, indicating that this excitation is appropriate to be used for the diagnosis of oil presence in the environment. DOI: http://dx.doi.org/10.17807/orbital.v10i1.103

Autonomic function recovery and physical activity levels in post-COVID-19 young adults after immunization: an observational follow-up case-control study

Coronavirus disease 2019 (COVID-19) has detrimental multi-system consequences. Symptoms may appear during the acute phase of infection, but the literature on long-term recovery of young adults after mild to moderate infection is lacking. Heart rate variability (HRV) allows for the observation of autonomic nervous system (ANS) modulation post-SARS-CoV-2 infection. Since physical activity (PA) can help improve ANS modulation, investigating factors that can influence HRV outcomes after COVID-19 is essential to advancements in care and intervention strategies. Clinicians may use this research to aid in the development of non-medication interventions. At baseline, 18 control (CT) and 20 post-COVID-19 (PCOV) participants were observed where general anamnesis was performed, followed by HRV and PA assessment. Thus, 10 CT and 7 PCOV subjects returned for follow-up (FU) evaluation 6 weeks after complete immunization (two doses) and assessments were repeated. Over the follow-up period, a decrease in sympathetic (SNS) activity (mean heart rate: p = 0.0024, CI = −24.67–−3.26; SNS index: p = 0.0068, CI = −2.50–−0.32) and increase in parasympathetic (PNS) activity (mean RR:p = 0.0097, CI = 33.72–225.51; PNS index: p = 0.0091, CI = −0.20–1.47) were observed. At follow-up, HRV was not different between groups (p > 0.05). Additionally, no differences were observed in PA between moments and groups. This study provides evidence of ANS recovery after SARS-CoV-2 insult in young adults over a follow-up period, independent of changes in PA.info:eu-repo/semantics/publishedVersio

Autonomic Function Recovery and Physical Activity Levels in Post-COVID-19 Young Adults after Immunization: An Observational Follow-Up Case-Control Study

Coronavirus disease 2019 (COVID-19) has detrimental multi-system consequences. Symptoms may appear during the acute phase of infection, but the literature on long-term recovery of young adults after mild to moderate infection is lacking. Heart rate variability (HRV) allows for the observation of autonomic nervous system (ANS) modulation post-SARS-CoV-2 infection. Since physical activity (PA) can help improve ANS modulation, investigating factors that can influence HRV outcomes after COVID-19 is essential to advancements in care and intervention strategies. Clinicians may use this research to aid in the development of non-medication interventions. At baseline, 18 control (CT) and 20 post-COVID-19 (PCOV) participants were observed where general anamnesis was performed, followed by HRV and PA assessment. Thus, 10 CT and 7 PCOV subjects returned for follow-up (FU) evaluation 6 weeks after complete immunization (two doses) and assessments were repeated. Over the follow-up period, a decrease in sympathetic (SNS) activity (mean heart rate: p = 0.0024, CI = −24.67–−3.26; SNS index: p = 0.0068, CI = −2.50–−0.32) and increase in parasympathetic (PNS) activity (mean RR: p = 0.0097, CI = 33.72–225.51; PNS index: p = 0.0091, CI = −0.20–1.47) were observed. At follow-up, HRV was not different between groups (p \u3e 0.05). Additionally, no differences were observed in PA between moments and groups. This study provides evidence of ANS recovery after SARS-CoV-2 insult in young adults over a follow-up period, independent of changes in PA

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

Fluorescence spectroscopy applied in lubricant oils

In this work, we report the use of the fluorescence spectroscopy to identify lubricant oils. Optical characterization was performed in four commercial lubricant oils that are used in the reciprocating compressors. Mid-infrared absorption of samples indicates the presence of aromatic rings showing bands at 1605 cm-1 (C=C stretching) and 815 cm-1 (C–H stretch out of plane). UV-VIS absorption spectra show bands of di and polyaromatic rings (around 230 nm and 260 nm respectively). Exciting the samples at 360 nm, a broad emission band centred at 440 nm is observed, indicating that this excitation is appropriate to be used for the diagnosis of the oil presence in the environment.</p