RESUMO
This study presents a novel method for producing acicular aragonite using argon oxygen decarburization (AOD) slag while controlling the reaction temperature, reaction time, stirring speed, and the magnesium-tocalcium stoichiometric ratio. This approach provides steel plants with an opportunity to decrease their CO2 emissions and promote efficient resource utilization and CO2 storage through the production of high-quality value-added products. The experimental results showed that reaction temperature was the most significant factor affecting the carbonation efficiency of AOD slag, followed by reaction time, stirring speed, CO2 partial pressure, and the liquid-to-solid ratio (L/S). The study also found that elevated temperature and prolonged reaction duration favored the preferential precipitation of aragonite. Additionally, raising the temperature and the magnesium-tocalcium stoichiometric ratio was shown to enhance the formation of aragonite, affecting its crystal growth orientation and dimensions. The optimal combination of reaction parameters for the preparation of acicular aragonite was found to be the reaction time of 8 h, the magnesium-tocalcium stoichiometric ratio of 0.8, the reaction temperature of 120 °C, and the stirring speed of 200 r·min-1. Under these conditions, the resulting acicular aragonite exhibited excellent overall uniformity, a large aspect ratio, and a smooth crystal surface, with a content of 91.49 %, a single crystal length ranging from 9.86 to 32.6 µm, and a diameter ranging from 0.63 to 2.15 µm. This study provides valuable insights into the efficient production of acicular aragonite from steel slag while reducing CO2 emissions and promoting the sustainable use of resources.
RESUMO
The rapid development of Internet in recent years has led to a proliferation of social media networks as people who can gather online to share information, knowledge, and opinions. However, the network public opinion tends to generate strongly misleading and a large number of messages can cause shocks to the public once major emergencies appear. Therefore, we need to make correct prediction regarding and timely identify a potential crisis in the early warning of network public opinion. In view of this, this study fully considers the features of development and the propagation characteristics, so as to construct a network public opinion early warning index system that includes 4 first-level indicators and 13 second-level indicators. The weight of each indicator is calculated by the "CRITIC" method, so that the comprehensive evaluation value of each time point can be obtained and the early warning level of internet public opinion can be divided. Then, the Back Propagation neural network based on Genetic Algorithm (GA-BP) is used to establish a network public opinion early warning model. Finally, the major public health emergency, COVID-19 pandemic, is taken as a case for empirical analysis. The results show that by comparing with the traditional classification methods, such as BP neural network, decision tree, random forest, support vector machine and naive Bayes, GA-BP neural network has a higher accuracy rate for early warning of network public opinion. Consequently, the index system and early warning model constructed in this study have good feasibility and can provide references for related research on internet public opinion.
RESUMO
We have developed a mild, convenient and efficient synthesis of highly functionalized (Z)-ß-enamino ketones from readily available 3,4-dihydroisoquinoline imines and ynones through an aza-Michael/hydrolysis cascade reaction. This method is also suitable for the preparation of (Z)-ß-enamino esters using alkynoates as starting materials. Complex fully substituted pyrroles can be constructed from the obtained (Z)-ß-enamino ketones. It is an attractive alternative approach for the preparation of highly functionalized enamino ketones, esters and pyrroles.
