Imperial College London
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DrAndreasKafizas

Faculty of Natural Sciences, Department of Chemistry

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6752a.kafizas Website

 
 
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Location

 

301GMolecular Sciences Research HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Bullen:2020:10.1016/j.jcis.2020.06.119,
author = {Bullen, J and Kenney, J and Fearn, S and Kafizas, A and Skinner, S and Weiss, D},
doi = {10.1016/j.jcis.2020.06.119},
journal = {Journal of Colloid and Interface Science},
pages = {834--849},
title = {Improved accuracy in multicomponent surface complexation models using surface-sensitive analytical techniques: adsorption of arsenic onto a TiO2/Fe2O3 multifunctional sorbent},
url = {http://dx.doi.org/10.1016/j.jcis.2020.06.119},
volume = {580},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Many novel composite materials have been recently 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 modelling for the next generation of composite sorbents.
AU  - Bullen,J
AU  - Kenney,J
AU  - Fearn,S
AU  - Kafizas,A
AU  - Skinner,S
AU  - Weiss,D
DO  - 10.1016/j.jcis.2020.06.119
EP  - 849
PY  - 2020///
SN  - 0021-9797
SP  - 834
TI  - Improved accuracy in multicomponent surface complexation models using surface-sensitive analytical techniques: adsorption of arsenic onto a TiO2/Fe2O3 multifunctional sorbent
T2  - Journal of Colloid and Interface Science
UR  - http://dx.doi.org/10.1016/j.jcis.2020.06.119
UR  - https://www.sciencedirect.com/science/article/pii/S0021979720308705?via%3Dihub
UR  - http://hdl.handle.net/10044/1/81189
VL  - 580
ER  -
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