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2 2023

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Adsorption of quaternary ammonium salt hydrophobic modifiers on the α-quartz (001) surface : a density functional theory study

Liu Chunfu, Wang Weitao, Wang Han, Zhu Chenyu, Ren Bao

Physicochemical Problems of Mineral Processing

2023

Vol. 59, iss. 3

art. no. 170260

To investigate the adsorption mechanism of quaternary ammonium salt on the α-quartz (001) surface, the adsorption models of hydrophobic modifiers 1231, 1431, 1631 and 1831 were constructed and simulated using the density functional theory (DFT). Results indicate that the adsorption energy of quaternary ammonium salt increases with the increase of carbon chain length, and the adsorption energy reaches the maximum at 18 carbon atoms; however, the adsorption capacity of 1631 is weak owing to the carbon chain deflection. Based on the Mulliken bond population analysis, reagent 1831 has the strongest interaction with α-quartz (001) surface compared with 1231, 1431 and 1631; and during the adsorption process, charge transfer and electrostatic attraction occur between the reagent and α-quartz (001) surface with similar degrees of charge transfer observed. This study emphasizes that electrostatic attraction plays a key role in the adsorption process, while the week hydrogen bonding plays a secondary role.

Anonymous traffic classification based on three-dimensional Markov image and deep learning

Tang Xin, Li Huanzhou, Zhang Jian, Tang Zhangguo, Wang Han, Cai Cheng

Bulletin of the Polish Academy of Sciences. Technical Sciences

2023

Vol. 71, nr 4

art. no. e145676

Illegal elements use the characteristics of an anonymous network hidden service mechanism to build a dark network and conduct various illegal activities, which brings a serious challenge to network security. The existing anonymous traffic classification methods suffer from cumbersome feature selection and difficult feature information extraction, resulting in low accuracy of classification. To solve this problem, a classification method based on three-dimensional Markov images and output self-attention convolutional neural network is proposed. This method first divides and cleans anonymous traffic data packets according to sessions, then converts the cleaned traffic data into three-dimensional Markov images according to the transition probability matrix of bytes, and finally inputs the images to the output self-attention convolution neural network to train the model and perform classification. The experimental results show that the classification accuracy and F1-score of the proposed method for Tor, I2P, Freenet, and ZeroNet can exceed 98.5%, and the average classification accuracy and F1-score for 8 kinds of user behaviors of each type of anonymous traffic can reach 93.7%. The proposed method significantly improves the classification effect of anonymous traffic compared with the existing methods.