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Ion-selective separators, are crucial and in high demand for maximizing the performance of lithium-sulfur (Li–S) batteries, which can conduct lithium ions while efficiently blocking polysulfides. However, commercial separators cannot effectively block the shuttle of polysulfides after multiple cycles due to their large porosity and easy dissolution, resulting in a reduced battery life. Herein, a covalent organic framework nanosheets (CON) ion-coated separator is prepared on the commercial separator. Due to the smaller pore size of CON-TFSI compared to polysulfides, the CON-TFSI modified separator can effectively block the polysulfide from migrating across the separator. By incorporating this innovative functional layer, Li–S batteries demonstrate outstanding performance. In a Li–S battery featuring a sulfur loading of 0.6 mg/cm2, it attains an initial discharge specific capacity of up to 891.9 mA h g−1, and exhibits the capacity retention of 54.6 % after 500 cycles at a current density of 0.2 C. This work offers a fresh perspective on the advancement of high-performance membranes for Li–S batteries.

Lithium–sulfur batteries have attracted extensive attention owing to their environmental friendliness, abundant reserves, high specific discharge capacity, and energy density. The shuttling effect and sluggish redox reactions confine the practical application of Li–S batteries. Exploring the new catalyst activation principle plays a key role in restraining polysulfide shuttling and improving conversion kinetics. In this respect, vacancy defects have been demonstrated to enhance the polysulfide adsorption and catalytic ability. However, inducing active defects has been mostly created by anion vacancies. In this work, an advanced polysulfide immobilizer and catalytic accelerator is developed by proposing FeOOH nanosheets with rich Fe vacancies (FeVs). The work provides a new strategy for the rational design and facile fabrication of cation vacancies to improve the performance of Li–S batteries.

Background: The psychology of university and college students is immature, they are thus more likely to suffer from depression due to the COVID-19 pandemic. The present study aims to investigate the self-reported depression status of Chinese university and college students and explore its influencing factors.Methods: We conducted a network-based online survey, and a total of 17,876 participants completed the questionnaire. Depression was measured by the Self-Rating Depression Scale (SDS). Univariate analysis and multivariate logistic analysis were performed to explore the influencing factors of self-reported depression symptoms.Results: The proportion of self-reported depression symptoms, mild self-reported depression symptoms, and moderate to severe (M/S) self-reported depression symptoms was 65.2, 53.7, and 11.5%, respectively. The mean score of self-reported depression was 54.8 ± 9.0. Female, personality type of partial introversion, junior college educational level, “moderate” or “high” self-perceived risk of infection, “moderately” or “highly” impacted by the outbreak, and being eager to go back to school were risk factors for M/S self-reported depression symptoms (p < 0.05). While, “moderate” or “high” concern about the outbreak, “moderate” or “high” satisfaction with pandemic prevention and control measures, and having health literacy on communicable diseases were protective factors for M/S self-reported depression symptoms (p < 0.05).Conclusion: The status of self-reported depression symptoms among university and college students was severer than expected, and the influencing factors were multifaceted. Government and school administrators should strengthen the dissemination of knowledge on disease prevention and control. Moreover, much attention should be paid to female and junior college students.

Abstract Background Few studies have investigated the associations between outdoor air pollution and outpatient visits for respiratory diseases (RDs) in general population. Methods We collected daily outpatient data of primary RDs from five hospitals in Jinan during January 2012 and December 2016, as well as daily measurements of air pollutants from the Jinan Environmental Monitoring Center and daily meteorological variables from the China Meteorological Data Sharing Service System. A generalized additive model (GAM) with quasi-Poisson regression was constructed to estimate the associations between daily average concentrations of outdoor air pollutants (PM2.5,PM10, SO2, NO2, CO and O3) and daily outpatient visits of RDs after adjusting for long-time trends, seasonality, the “day of the week” effect, and weather conditions. Subgroup analysis stratified by gender, age group and the type of RDs was conducted. Results A total of 1,373,658 outpatient visits for RDs were identified. Increases of 10 μg/m3 in PM2.5, PM10, NO2, CO and O3 were associated with0.168% (95% CI, 0.072–0.265%), 0.149% (95% CI, 0.082–0.215%), 0.527% (95% CI, 0.211–0.843%), 0.013% (95% CI, 0.003–0.023%), and 0.189% (95% CI, 0.032–0.347%) increases in daily outpatient visits for RDs, respectively. PM2.5 and PM10 showed instant and continuous effects, while NO2, CO and O3 showed delayed effects on outpatient visits for RDs. In stratification analysis, PM2.5 and PM10 were associated with acute RDs only. Conclusions Exposure to outdoor air pollutants including PM2.5, PM10, NO2, CO and O3 associated with increased risk of outpatient visits for RDs.