Giant Light-Emission Enhancement in Lead Halide Perovskites by Surface Oxygen Passivation.

Surface condition plays an important role in the optical performance of semiconductor materials. As new types of semiconductors, the emerging metal-halide perovskites are promising for next-generation optoelectronic devices. We discover significantly improved light-emission efficiencies in lead halide perovskites due to surface oxygen passivation. The enhancement manifests close to 3 orders of magnitude as the perovskite dimensions decrease to the nanoscale, improving external quantum efficiencies from <0.02% to over 12%. Along with about a 4-fold increase in spontaneous carrier recombination lifetimes, we show that oxygen exposure enhances light emission by reducing the nonradiative recombination channel. Supported by X-ray surface characterization and theoretical modeling, we propose that excess lead atoms on the perovskite surface create deep-level trap states that can be passivated by oxygen adsorption

Immunostimulatory Activity of Protein Hydrolysate from Oviductus Ranae on Macrophage In Vitro

Oviductus Ranae is the dry oviduct of Rana chensinensis, which is also called R. chensinensis oil. Oviductus Ranae is a valuable Chinese crude drug and is recorded in the Pharmacopoeia of the People’s Republic of China. The aim of this study was to investigate the immunostimulatory activity of protein hydrolysate of Oviductus Ranae (ORPH) and to assess its possible mechanism. Immunomodulatory activity of ORPH was examined in murine macrophage RAW 264.7 cells. The effect of ORPH on the phagocytic activity of macrophages was determined by the neutral red uptake assay. After treatment with ORPH, NO production levels in the culture supernatant were investigated by Griess assay. The mRNA and protein expressions of inducible nitric oxide synthase (iNOS) were detected by RT-PCR and Western blotting. The production of TNF-α, IL-1β, and IL-6 after treatment with ORPH was measured using ELISA assay. In addition, NF-κB levels were also investigated by Western blot. The results showed that ORPH enhanced the phagocytosis of macrophage, increased productions of TNF-α, IL-1β, IL-6, and NO in RAW 264.7 cells, and upregulated the mRNA and protein expression of iNOS. Besides, NF-κB, levels in RAW 264.7 cells were elevated after ORPH treatment. These findings suggested that ORPH might stimulate macrophage activities by activating the NF-κB pathway

Association between Long-Term Changes in Dietary Percentage of Energy from Fat and Obesity: Evidence from over 20 Years of Longitudinal Data

Objectives: This study assessed the associations between long-term trajectories of percentage of energy from fat (PEF) and obesity among Chinese adults. Methods: Longitudinal data collected by the China Health and Nutrition Survey from 1991 to 2015 were analyzed. A body mass index ≥28.0 was defined as general obesity. Participants’ baseline PEF levels were categorized as lower than the recommendation of the Chinese Dietary Guideline (20%), meeting the recommendation (20−30%), and higher than the recommendation (>30%). Patterns of PEF trajectories were identified by latent class trajectory analysis for overall participants and participants in different baseline PEF groups, respectively. Cox proportional hazards regression models with shared frailty were used to estimate associations between PEF and obesity. Results: Data on 13,025 participants with 72,191 visits were analyzed. Four patterns of PEF trajectory were identified for overall participants and participants in three different baseline PEF groups, respectively. Among overall participants, compared with “Baseline Low then Increase Pattern” (from 12% to 20%), participants with “Baseline Normal-Low then Increase-to-High Pattern” (from 20% to 32%) had a higher hazard of obesity (hazard ratio (HR) and 95% confident interval (CI) at 1.18 (1.01−1.37)). Compared with the “Stable Pattern” group (stable at around 18% and 22%, respectively), participants with “Sudden-Increase Pattern” (from 18% to 30%) in the baseline group whose PEF levels were lower than the recommendation and those with “Sudden-Increase then Decrease Pattern” (rapidly increased from 25% to 40%, and then decreased) in the baseline group who met the recommendation had higher hazards of obesity (HRs and 95% CIs being 1.65 (1.13−2.41) and 1.59 (1.03−2.46), respectively). Conclusions: Adults with a trajectory that involved a sudden increase to a high-level PEF had a higher risk of general obesity. People should avoid increasing PEF suddenly

Analysis of the Spatial and Temporal Changes of NDVI and Its Driving Factors in the Wei and Jing River Basins

This study aimed to explore the long-term vegetation cover change and its driving factors in the typical watershed of the Yellow River Basin. This research was based on the Google Earth Engine (GEE), a remote sensing cloud platform, and used the Landsat surface reflectance datasets and the Pearson correlation method to analyze the vegetation conditions in the areas above Xianyang on the Wei River and above Zhangjiashan on the Jing River. Random forest and decision tree models were used to analyze the effects of various climatic factors (precipitation, temperature, soil moisture, evapotranspiration, and drought index) on NDVI (normalized difference vegetation index). Then, based on the residual analysis method, the effects of human activities on NDVI were explored. The results showed that: (1) From 1987 to 2018, the NDVI of the two watersheds showed an increasing trend; in particular, after 2008, the average increase rate of NDVI in the growing season (April to September) increased from 0.0032/a and 0.003/a in the base period (1987–2008) to 0.0172/a and 0.01/a in the measurement period (2008–2018), for the Wei and Jing basins, respectively. In addition, the NDVI significantly increased from 21.78% and 31.32% in the baseline period (1987–2008) to 83.76% and 92.40% in the measurement period (2008–2018), respectively. (2) The random forest and classification and regression tree model (CART) can assess the contribution and sensitivity of various climate factors to NDVI. Precipitation, soil moisture, and temperature were found to be the three main factors that affect the NDVI of the study area, and their contributions were 37.05%, 26.42%, and 15.72%, respectively. The changes in precipitation and soil moisture in the entire Jing River Basin and the upper and middle reaches of the Wei River above Xianyang caused significant changes in NDVI. Furthermore, changes in precipitation and temperature led to significant changes in NDVI in the lower reaches of the Wei River. (3) The impact of human activities in the Wei and Jing basins on NDVI has gradually changed from negative to positive, which is mainly due to the implementation of soil and water conservation measures. The proportions of areas with positive effects of human activities were 80.88% and 81.95%, of which the proportions of areas with significant positive effects were 11.63% and 7.76%, respectively. These are mainly distributed in the upper reaches of the Wei River and the western and eastern regions of the Jing River. These areas are the key areas where soil and water conservation measures have been implemented in recent years, and the corresponding land use has transformed from cultivated land to forest and grassland. The negative effects accounted for 1.66% and 0.10% of the area, respectively, and were mainly caused by urban expansion and coal mining

Establishment and research of combined forecasting model of carbon

This paper analyzes the influencing factors of carbon dioxide emissions from four aspects: Population, economy, industrial structure and energy, then from the carbon emissions, economic development, industrial structure, energy consumption structure to show the status quo of carbon emissions in Hubei Province. Based on the analysis of the influencing factors, the main influencing factors of carbon emission are population, regional gross product and coal consumption The multivariate linear regression model and the polynomial curve model are established and the error analysis is carried out. The combination weight coefficients of two single models are obtained through the linear programming model and the combination forecasting model is established, finally, the corresponding countermeasures to reduce carbon emissions are put forward

Long-Term Variability of Vegetation Cover and Its Driving Factors and Effects over the Zuli River Basin in Northwest China

Vegetation information is a critical factor in regional environment management under climate change. In this study, a typical arid and semi-arid watershed on the Loess Plateau, the Zu Li River Basin (ZRB), was selected to study the long-term changes in vegetation cover and its drivers and impacts. Unlike existing normalized vegetation index (NDVI) products, which have coarse spatial resolution and short time horizons, this study used the 30 m Landsat dataset analyzed in the Google Earth Engine (GEE) to generate high-resolution and long-term NDVI data, which are the most ideal for monitoring vegetation dynamics using long-time-series data products. The results showed that the annual mean maximum NDVI (normalized vegetation index) in the ZRB increased during 1987–2021, with a significant (p p p < 0.05) negatively correlated with runoff coefficient and sand content, indicating that vegetation cover was an important reason for the decrease in runoff coefficient and sand content

Temporal and Spatial Variation of NDVI and Its Driving Factors in Qinling Mountain

Qinling Mountains is the north–south boundary of China’s geography; the vegetation changes are of great significance to the survival of wildlife and the protection of species habitats. Based on Landsat products in the Google Earth Engine (GEE) platform, Pearson’s correlation coefficient method, and classification and regression models, this study analyzed the changes in NDVI (Normalized Difference Vegetation Index) in the Qinling Mountains in the past 38 years and the sensitivity of its driving factors. Finally, residual analysis method and accumulate slope change rate are used to identify the impact of human activities and climate change on NDVI. The research results show the following: (1) The NDVI value in most areas of Qinling Mountains is at a medium-to-high level, and 99.76% of the areas correspond to an increasing trend of NDVI, and the significantly increased area accounts for more than 20%. (2) From 1981 to 2019, the NDVI of the Qinling Mountains increased from 0.63 to 0.78, showing an overall upward trend, and it increased significantly after 2006. (3) Sensitivity analysis results show that the western high-altitude area of Qinling Mountain area dominated by grassland is mainly affected by precipitation. The central and southeastern parts of the Qinling Mountains are significantly affected by temperature, and they are mainly distributed in areas dominated by forest. (4) The contribution rates of climate change and human activities to NDVI are 36.04% and 63.96%, respectively. Among them, the positive impact of human activities on the NDVI of the Qinling Mountains accounted for 99.85% of the area. The area with significant positive effect accounted for 36.49%. The significant negative effect area accounts for only 0.006%, mainly distributed in urban areas and coal mining areas