吸附
化学
菲
胞外聚合物
针铁矿
分馏
高岭石
蒙脱石
吸附
有机质
环境化学
溶解有机碳
色谱法
粘土矿物
有机化学
矿物学
地质学
细菌
生物膜
古生物学
作者
Yiqun Chen,Minli Wang,Xinwei Zhou,Heyun Fu,Xiaolei Qu,Dongqiang Zhu
出处
期刊:Chemosphere
[Elsevier BV]
日期:2020-09-14
卷期号:263: 128264-128264
被引量:39
标识
DOI:10.1016/j.chemosphere.2020.128264
摘要
Microbial extracellular polymeric substances (EPS) represent an important source of labile component in natural organic matter (NOM) pool. However, the sorption behavior of EPS to mineral surfaces and associated effects on sorption of hydrophobic organic contaminants (HOCs) are not well understood. Here, we systematically investigated the fractionation of EPS extracted from two different microbial sources (Gram-positive B. subtilis and Gram-negative E. coli) during sorption to montmorillonite, kaolinite, and goethite using collective characterization methods (SEM, electrophoretic mobility, FTIR, 1H NMR, UV–vis, fluorescence, and size exclusion chromatography). The peptide-like substances and acidic components with high aromaticity in B. subtilis EPS were more preferentially sorbed than those fractions in E. coli EPS by the three minerals, especially by goethite. Additionally, goethite sorbed more negatively charged and lower molecular weight fractions compared to montmorillonite. The presorption of EPS (1.68–3.79% organic carbon) on the three minerals increased the sorption distribution coefficient (Kd) of phenanthrene (a model apolar HOC) by 2.83–5.29 times, depending on the EPS-mineral complex. All the six examined EPS-mineral complexes exhibited approximately one order of magnitude larger organic carbon (OC)-normalized sorption coefficient (KOC) than the two pristine EPS, indicating that the sorptive interactions were pronouncedly facilitated by the sorbed EPS on mineral surfaces. Thus, the type and surface property of minerals as well as the biological source of EPS are key determinants of sorption fractionation of EPS on minerals and in turn affect sorption affinity of apolar HOCs to EPS-mineral complexes.
科研通智能强力驱动
Strongly Powered by AbleSci AI