Demulsification of amphiphilic gemini ionic liquids and its demulsification mechanism.

This work aims to prepare two new amphiphilic and interfacial active gemini ionic liquids to treat crude oil and investigates its demulsification mechanism. Tetraethylene glycol was pretreated with thionyl chloride and used as a linker to connect succinimide or phthalimide, and then reacted with dodecyl benzene sulphonic acid to obtain the corresponding amphiphilic and interfacial active gemini ionic liquid STA or PTA, respectively. 1H nuclear magnetic resonance spectroscopy (1HNMR) and Fourier-transform infrared spectroscopy (FTIR) was used to determine the chemical structures. The demulsification tests showed the demulsification efficiency with 150 mg/L of STA or PTA at 60 °C for 30 min was 99.89% and 99.79%, respectively. Furthermore, the demulsification mechanism of STA and PTA were studied and the prominent demulsification ability of STA and PTA were attributed to the better interfacial activity and amphipathy which could destroy the asphaltenes interfacial film. These results showed that STA and PTA had excellent demulsification efficiency, which promised application in petroleum industry.

Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning.

BACKGROUND: Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. METHODS: First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. RESULTS: Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment. CONCLUSIONS: The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.