Improved accuracy in multicomponent surface complexation models using surface-sensitive analytical techniques: Adsorption of arsenic onto a TiO2/Fe2O3 multifunctional sorbent

JOURNAL OF COLLOID AND INTERFACE SCIENCE

卷:580

Article

Bullen, Jay C.¹  Kenney, Janice P. L.²  Fearn, Sarah³  Kafizas, Andreas^4,5  Skinner, Stephen³  Weiss, Dominik J.^1,5,6 

[1] Imperial Coll London, Fac Engn, Dept Earth Sci & Engn, London SW7 2BX, England

[2] MacEwan Univ, Dept Phys Sci, Edmonton, AB T5J 4S2P, Canada

[3] Imperial Coll London, Fac Engn, Dept Mat, London SW7 2BX, England

[4] Imperial Coll London, Dept Chem, White City Campus, London W12 OBZ, England

[5] Imperial Coll London, Grantham Inst, London SW7 2AZ, England

[6] Princeton Univ, E Quad, Civil & Environm Engn, Princeton, NJ 08544 USA

关键词: Arsenic;    Adsorption;    TiO2;    Iron oxide;    Composite;    Surface complexation model;    SCM;    Low energy ion scattering;    LEIS;    Surface analysis;   

DOI  :  10.1016/j.jcis.2020.06.119

来源: Elsevier

【 摘 要 】

Novel composite materials are increasingly developed for water treatment applications with the aim of achieving multifunctional behaviour, e.g. combining adsorption with light-driven remediation. The application of surface complexation models (SCM) is important to understand how adsorption changes as a function of pH, ionic strength and the presence of competitor ions. Component additive (CA) models describe composite sorbents using a combination of single-phase reference materials. However, predictive adsorption modelling using the CA-SCM approach remains unreliable, due to challenges in the quantitative determination of surface composition. In this study, we test the hypothesis that characterisation of the outermost surface using low energy ion scattering (LEIS) improves CA-SCM accuracy. We consider the TiO2/Fe2O3 photocatalyst-sorbents that are increasingly investigated for arsenic remediation. Due to an iron oxide surface coating that was not captured by bulk analysis, LEIS significantly improves the accuracy of our component additive predictions for monolayer surface processes: adsorption of arsenic(V) and surface acidity. We also demonstrate non-component additivity in multilayer arsenic(III) adsorption, due to changes in surface morphology/porosity. Our results demonstrate how surface-sensitive analytical techniques will improve adsorption models for the next generation of composite sorbents. (C) 2020 Elsevier Inc. All rights reserved.

【 授权许可】

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