Improved accuracy in multicomponent surface complexation models using surface-sensitive analytical techniques: adsorption of arsenic onto a TiO2/Fe2O3 multifunctional sorbent
| Autor: | Andreas Kafizas, Janice P.L. Kenney, Jay C. Bullen, Sarah Fearn, Dominik J. Weiss, Stephen J. Skinner |
|---|---|
| Přispěvatelé: | Engineering and Physical Sciences Research Council, The Royal Society |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
adsorption model
Surface analysis iron oxide Sorbent Materials science Composite number Surface complexation model chemistry.chemical_element Composite 02 engineering and technology surface-sensitive techniques 010402 general chemistry 01 natural sciences 09 Engineering Arsenic Biomaterials Colloid and Surface Chemistry Adsorption TiO(2) arsenic remediation TiO2 LEIS Porosity Chemical Physics 02 Physical Sciences Low energy ion scattering water remediation 021001 nanoscience & nanotechnology 6. Clean water 0104 chemical sciences Surfaces Coatings and Films Electronic Optical and Magnetic Materials SCM Surface coating Chemical engineering Low-energy ion scattering chemistry Ionic strength xps 0210 nano-technology 03 Chemical Sciences |
| Popis: | 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/Fe2O3photocatalyst-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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|