Graphene oxide (GO) has attracted remarkable attention as a potential material in the fabrication of next-generation membranes with high water permeability and efficient purification. Herein, an easy and effective means of modifying GO lamellar with a phytic acid (PhA) molecule as both an inserter and a surface modifier was developed to fabricate high-performance GO-based membranes. As a result, the addition of PhA to the GO membrane enhanced hydrophilicity and enlarged the interlamellar spacing. The optimal GP-10 composite membrane displayed a high average pure water flux of 6.31 L m–2 h–1 bar–1 under an ultralow pressure nanofiltration condition, which was about 18.6 times higher than that of 0.34 L m–2 h–1 bar–1 for pure GO membrane. At the same time, it possessed the ability to reject different charged dye molecules with a rejection rate higher than 99.88%. In addition, the composite membrane also showed good structural stability under different pH conditions. This study not only provides a method to simply design GO-based membranes by introducing multifunctional small molecules but also sheds light on using such GO composite membranes in practical water separation applications.

Membrane separation is a central area of research in chemical engineering because of its important status as the key technology in chemical separation and water treatment processes. Recently, polymeric graphitic carbon nitride (g-C3N4) has emerged as a candidate material for membrane applications because of its appealing physiochemical properties and ease of fabrication. Herein we review the progress of g-C3N4-based membranes, including the peculiarity of two-dimensional (2D) g-C3N4, the tailoring of functionality, and integration into membrane stacks for practical applications in gas separation, water purification, pervaporation, photocatalytic water filtration, oil-in-water separation, and polymer electrolyte membranes. Moreover, possible development directions of g-C3N4-based membranes are discussed along with the current challenges and future perspectives.

Fabrication of an organic polymer nanofiltration with both high water permeability and high salt rejection is still a big challenge. Herein, a phytic acid (PhA)-modified graphene oxide (GO) was used...

The pandemic progression is a dynamic process, in which measures yield outcomes, and outcomes in turn influence subsequent measures and outcomes. Due to the dynamics of pandemic progression, it is challenging to analyse the long-term influence of an individual measure in the sequence on pandemic outcomes. To demonstrate the problem and find solutions, in this article, we study the first wave of the pandemic—probably the most dynamic period—in the Nordic countries and analyse the influences of the Swedish measures relative to the measures adopted by its neighbouring countries on COVID-19 mortality, general mortality, COVID-19 incidence, and unemployment. The design is a longitudinal observational study. The linear regressions based on the Poisson distribution or the binomial distribution are employed for the analysis. To show that analysis can be timely conducted, we use table data available during the first wave. We found that the early Swedish measure had a long-term and significant causal effect on public health outcomes and a certain degree of long-term mitigating causal effect on unemployment during the first wave, where the effect was measured by an increase of these outcomes under the Swedish measures relative to the measures adopted by the other Nordic countries. This information from the first wave has not been provided by available analyses but could have played an important role in combating the second wave. In conclusion, analysis based on table data may provide timely information about the dynamic progression of a pandemic and the long-term influence of an individual measure in the sequence on pandemic outcomes.