DLVO理论
化学
粘土矿物
离子强度
沉积(地质)
伊利石
化学工程
环境工程
矿物学
工程类
沉积物
地质学
胶体
物理化学
古生物学
水溶液
作者
Xinyao Ye,Cheng Zhou,Ming Wu,Yanru Hao,Guoping Lu,Bill X. Hu,Lei Xiang,Qu-Sheng Li,Jianfeng Wu,Jichun Wu
出处
期刊:Water Research
[Elsevier]
日期:2022-08-13
卷期号:223: 118978-118978
被引量:42
标识
DOI:10.1016/j.watres.2022.118978
摘要
Microplastics are widely detected in the soil-groundwater environment, which has attracted more and more attention. Clay mineral is an important component of the porous media contained in aquifers. The transport experiments of polystyrene nanoparticles (PSNPs) in quartz sand (QS) mixed with three kinds of clay minerals are conducted to investigate the effects of kaolinite (KL), montmorillonite (MT) and illite (IL) on the mobility of PSNPs in groundwater. Two-dimensional (2D) distributions of DLVO interaction energy are calculated to quantify the interactions between PSNPs and three kinds of clay minerals. The critical ionic strengths (CIS) of PSNPs-KL, PSNPs-MT and PSNPs-IL are 17.0 mM, 19.3 mM and 21.0 mM, respectively. Experimental results suggest KL has the strongest inhibition effect on the mobility of PSNPs, followed by MT and IL. Simultaneously, the change of ionic strength can alter the surface charge of PSNPs and clay minerals, thus affecting the interaction energy. Experimental and model results indicate both the deposition rate coefficient (k) and maximum deposition (Smax) linearly decrease with the logarithm of the DLVO energy barrier, while the mass recovery rate of PSNPs (Rm) exponentially increases with the logarithm of the DLVO energy barrier. Therefore, the mobility and associated kinetic parameters of PSNPs in complex porous media containing clay minerals can be predicted by 2D distributions of DLVO interaction energy. These findings could help to gain insight into understanding the environmental behavior and transport mechanism of microplastics in the multicomponent porous media, and provide a scientific basis for the accurate simulation and prediction of microplastic contamination in the groundwater system.
科研通智能强力驱动
Strongly Powered by AbleSci AI