Application of Community Data to Surface Complexation Modeling Framework Development: Iron Oxide Protolysis

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 SCM type and surface site density (SSD) on the fit quality and protolysis constants can be readily evaluated. For example, the non-electrostatic model (NEM) yielded a poor
data fit compared to diffuse double layer model (DDLM) and constant capacitance models (CCM) due to the absence of known surface charge effects. Regardless of the choice of iron oxide mineral, pKa1 decreased with increasing SSD while the opposite tendency was observed for pKa2. This newly
developed framework demonstrates a method to reconcile community data-wide potentiometric titration data using FAIR 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.