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Herein, we report a detailed study on creating heterojunction between graphitic carbon nitride (g-C3N4) and bismuth phosphate (BiPO4), enhancing the unpaired free electron mobility. This leads to an accelerated photocatalysis of 2,4-dichlorophenols (2,4-DCPs) under sunlight irradiation. The heterojunction formation was efficaciously conducted via a modest thermal deposition technique. The function of g-C3N4 plays a significant role in generating free electrons under sunlight irradiation. Together, the generated electrons at the g-C3N4 conduction band (CB) are transferred and trapped by the BiPO4 to form active superoxide anion radicals (?O2?). These active radicals will be accountable for the photodegradation of 2,4-DCPs. The synthesized composite characteristics were methodically examined through several chemical and physical studies. Due to the inimitable features of both g-C3N4 and BiPO4, its heterojunction formation, 2.5wt% BiPO4/g-C3N4 achieved complete 2,4-DCP removal (100%) in 90 min under sunlight irradiation. This is due to the presence of g-C3N4 that enhanced electron mobility through the formation of heterojunctions that lengthens the electron-hole pairs’ lifetime and maximizes the entire solar spectrum absorption to generate active electrons at the g-C3N4 conduction band. Thus, this formation significantly draws the attention for future environmental remediation, especially in enhancing the entire solar spectrum’s harvesting.
Graphical abstract 相似文献The spread of highly pathogenic avian influenza H5N1 has posed a major threat to global public health. Understanding the spatiotemporal outbreak characteristics and environmental factors of H5N1 outbreaks is of great significance for the establishment of effective prevention and control systems. The time and location of H5N1 outbreaks in poultry and wild birds officially confirmed by the World Organization for Animal Health from 2005 to 2019 were collected. Spatial autocorrelation analysis and multidistance spatial agglomeration analysis methods were used to analyze the global outbreak sites of H5N1. Combined with remote sensing data, the correlation between H5N1 outbreaks and environmental factors was analyzed using binary logistic regression methods. We analyzed the correlation between the H5N1 outbreak and environmental factors and finally made a risk prediction for the global H5N1 outbreaks. The results show that the peak of the H5N1 outbreaks occurs in winter and spring. H5N1 outbreaks exhibit aggregation, and a weak aggregation phenomenon is noted on the scale close to 5000 km. Water distance, road distance, railway distance, wind speed, leaf area index (LAI), and specific humidity were protective factors for the outbreak of H5N1, and the odds ratio (OR) were 0.985, 0.989, 0.995, 0.717, 0.832, and 0.935, respectively. Temperature was a risk factor with an OR of 1.073. The significance of these ORs was greater than 95%. The global risk prediction map was obtained. Given that the novel coronavirus (COVID-19) is spreading globally, the methods and results of this study can provide a reference for studying the spread of COVID-19.
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