Prediction of Henry Constants and Adsorption Mechanism of Volatile Organic Compounds on Multi-Walled Carbon Nanotubes by Using Support Vector Regression

doi:10.1088/0256-307X/33/4/048201

Chin. Phys. Lett.

2016, Vol. 33

Issue (04): 048201 DOI: 10.1088/0256-307X/33/4/048201

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY

Prediction of Henry Constants and Adsorption Mechanism of Volatile Organic Compounds on Multi-Walled Carbon Nanotubes by Using Support Vector Regression

Wen-De Cheng, Cong-Zhong Cai^**

State Key Laboratory of Coal Mine Disaster Dynamics and Control, Department of Applied Physics, Chongqing University, Chongqing 400044

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Wen-De Cheng, Cong-Zhong Cai 2016 Chin. Phys. Lett. 33 048201

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Abstract Support vector regression (SVR) combined with particle swarm optimization for its parameter optimization is employed to establish a model for predicting the Henry constants of multi-walled carbon nanotubes (MWNTs) for adsorption of volatile organic compounds (VOCs). The prediction performance of SVR is compared with those of the model of theoretical linear salvation energy relationship (TLSER). By using leave-one-out cross validation of SVR test Henry constants for adsorption of 35 VOCs on MWNTs, the root mean square error is 0.080, the mean absolute percentage error is only 1.19%, and the correlation coefficient $(R^{2})$ is as high as 0.997. Compared with the results of the TLSER model, it is shown that the estimated errors by SVR are all smaller than those achieved by TLSER. It reveals that the generalization ability of SVR is superior to that of the TLSER model. Meanwhile, multifactor analysis is adopted for investigation of the influences of each molecular structure descriptor on the Henry constants. According to the TLSER model, the adsorption mechanism of adsorption of carbon nanotubes of VOCs is mainly a result of van der Waals and interactions of hydrogen bonds. These can provide the theoretical support for the application of carbon nanotube adsorption of VOCs and can make up for the lack of experimental data.

Received: 30 October 2015 Published: 29 April 2016

PACS:	82.20.Wt	(Computational modeling; simulation)
	07.05.Tp	(Computer modeling and simulation)
	89.60.Ec	(Environmental safety)

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https://cpl.iphy.ac.cn/10.1088/0256-307X/33/4/048201 OR https://cpl.iphy.ac.cn/Y2016/V33/I04/048201

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