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

Area of Science:

• Environmental Chemistry
• Geochemistry
• Surface Science

Background:

• Accurate modeling of mineral surface complexation is crucial for predicting contaminant fate and transport in environmental systems.
• Existing surface complexation models often lack robustness due to data variability and differing methodologies.
• Community-driven data integration offers a path to more reliable surface complexation parameters.

Purpose of the Study:

• To develop a comprehensive, community data-driven framework for surface complexation modeling of mineral potentiometric titrations.
• To generate robust protolysis constants for common iron oxides (ferrihydrite, goethite, hematite, magnetite).
• To evaluate the influence of model type and surface site density on model performance.

Main Methods:

• Compiled and integrated community potentiometric titration data for selected iron oxide minerals.
• Applied surface complexation modeling, comparing different electrostatic models (non-electrostatic, DDLM, CCM).
• Assessed the impact of surface site density (SSD) on protolysis constants (pK_a1, pK_a2).

Main Results:

• The developed framework successfully reconciled diverse potentiometric titration data, yielding representative protolysis constants.
• Non-electrostatic models showed poorer fits compared to diffuse double layer (DDLM) and constant capacitance models (CCM).
• A consistent trend was observed: pK_a1 decreased with increasing SSD, while pK_a2 increased, across all iron oxides.

Conclusions:

• The community data-driven framework enhances the robustness and reliability of surface complexation models.
• The generated protolysis constants improve predictions for metal sorption and reactive transport modeling.
• The framework is expandable and extensible, paving the way for comprehensive, updateable surface complexation databases.