Excitation dependent recombination studies on SnO2/TiO2 electrospun nanofibers

Poly(vinyl acetate) (PVAc)/TiO2 nanofibers, PVAc/SnO2 nanoribbons and PVAc/SnO2-TiO2 nanoribbons were produced via electrospinning. TiO2 nanofibers and SnO2 nanoribbons were obtained by removal of the polymeric matrix (PVAc) after calcination at 450 °C. Interestingly, PVAc/SnO2-TiO2 nanoribbons were transformed into SnO2-TiO2 nanofibers after calcination under the similar conditions. Fiber morphology and elemental mapping confirmed through SEM and TEM microscope techniques respectively. The X-ray diffraction measurements suggested the presence of anatase TiO2 and rutile SnO2 and both were present in the SnO2-TiO2 mixed system. Systematic photoluminescence studies were performed on the electrospun nanostructures at different excitation wavelengths (λex1 = 325, λex2 = 330, λex3 = 350, λex4 = 397 and λex5 = 540 nm). We emphasize that the defects in the SnO2-TiO2 based on the defect levels present in TiO2 and SnO2 and anticipate that these defect levels may have great potential in understanding and characterizing various semiconducting nanostructures. © The Royal Society of Chemistry

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Not Available

Not AvailableNot AvailableNot Availabl

Not Available

Not AvailableNot AvailableNot Availabl

Field Validation of DNDC Model for Methane and Nitrous Oxide Emissions from Rice-based Production Systems of India

The DNDC (DeNitrification and DeComposition) model was tested against experimental data on CH4and N2O emissions from rice fields at different geographical locations in India. There was a good agreement between the simulated and observed values of CH4 and N2O emissions. The difference between observed and simulated CH4 emissions in all sites ranged from −11.6 to 62.5 kg C ha−1 season−1. Most discrepancies between simulated and observed seasonal fluxes were less than 20% of the field estimate of the seasonal flux. The relative deviation between observed and simulated cumulative N2O emissions ranged from −237.8 to 28.6%. However, some discrepancies existed between observed and simulated seasonal patterns of CH4 and N2O emissions. The model simulated zero N2O emissions from continuously flooded rice fields and poorly simulated CH4emissions from Allahabad site. For all other simulated cases, the model satisfactorily simulated the seasonal variations in greenhouse gas emission from paddy fields with different land management. The model also simulated the C and N balances in all the sites, including other gas fluxes, viz. CO2, NO, NO2, N2 and NH3 emissions. Sensitivity tests for CH4 indicate that soil texture and pH significantly influenced the CH4 emission. Changes in organic C content had a moderate influence on CH4 emission on these sites. Introducing the mid-season drainage reduced CH4 emissions significantly. Process-based biogeochemical modeling, as with DNDC, can help in identifying strategies for optimizing resource use, increasing productivity, closing yield gaps and reducing adverse environmental impacts

Biogeochemical cycling of C, N, and water in paddy rice agriculture in Monsoon Asia

We report latest results of an ongoing project to evaluate the biogeochemical cycling of C, N, and water in paddy rice agriculture in Monsoon Asia. The core project focus is application of the DNDC biogeochemical model to estimate greenhouse gas emissions from paddy rice, and possible impacts of changes in agricultural management on those emissions. Simulations are done at the scale of sub-national political units (county equivalents and/or provinces/states). One effort has been to develop geospatial input data sets of agricultural land use and management, including crop rotations, cropping intensity, water management (irrigated or rainfed), fertilizer use. We have worked with both statistical census data and MODIS space-borne remote sensing data to map paddy rice across the domain. A second effort has been model development for application to rainfed paddy agriculture, and model testing against field data from paddy sites in India. The final thrust is model application across the domain to estimate water use, crop yield, net C-sequestration in soils, and emissions of methane and nitrous oxide

Highly anisotropic titanates from electrospun TiO 2-SiO 2 composite nanofibers and rice grain-shaped nanostructures

10.1039/c1ra00466bRSC Advances23992-99