Application of community data to surface complexation modeling framework development: Iron oxide protolysis

Sol-Chan Han; Elliot Chang; Susanne Zechel; Frank Bok; Mavrik Zavarin

doi:10.1016/j.jcis.2023.06.054

Application of community data to surface complexation modeling framework development: Iron oxide protolysis

J Colloid Interface Sci. 2023 Oct 15:648:1015-1024. doi: 10.1016/j.jcis.2023.06.054. Epub 2023 Jun 10.

Authors

Sol-Chan Han¹, Elliot Chang¹, Susanne Zechel², Frank Bok², Mavrik Zavarin³

Affiliations

¹ Seaborg Institute, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550, United States.
² Institute of Resource Ecology, Actinide Thermodynamics Department (FWOA), Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstraße 400, 01328 Dresden, Germany.
³ Seaborg Institute, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550, United States. Electronic address: zavarin1@llnl.gov.

PMID: 37343488
DOI: 10.1016/j.jcis.2023.06.054

Abstract

This study presents a comprehensive community data-driven surface complexation modeling framework for simulating potentiometric titration of mineral surfaces. Compiled community data for ferrihydrite, goethite, hematite, and magnetite are fit to produce representative protolysis constants that can reproduce potentiometric titration data collected from multiple literature sources. Using this framework, the impact of surface complexation model type and surface site density (SSD) on the fit quality and protolysis constants can be readily evaluated. For example, the non-electrostatic model yielded a poor data fit compared to diffuse double layer model and constant capacitance models due to the absence of known surface charge effects. Regardless of the choice of iron oxide mineral, pK_a1 decreased with increasing SSD while the opposite tendency was observed for pK_a2. This newly developed framework demonstrates a method to reconcile community data-wide potentiometric titration data using Findable, Accessible, Interoperable, Reusable data principles to produce mineral protolysis constants that improve robustness of surface complexation models for applications in metal sorption and reactive transport modeling. The framework is readily expandable (as community data increase) and extensible (as the number of minerals increase). The framework provides a path forward for developing self-consistent, comprehensive, and updateable surface complexation databases for surface complexation and reactive transport modeling.

Keywords: Iron Oxide; Potentiometric Titration; Protolysis; Radionuclide; Sorption; Surface Complexation.