Expanding the Toolbox of Simple, Cost-Efficient, and Automatable Methods for Quantifying Surface Functional Groups on NanoparticlesPotentiometric Titration

Measuring surface functional groups (FGs) on nanomaterials (NMs) is essential for designing dispersible and stable NMs with tailored and predictable functionality. FG screening and quantification also plays a critical role for subsequent processing steps, NM long-term stability, quality control of NM production, and risk assessment studies and enables the implementation of sustainable and safe-(r)-by-design concepts. This calls for simple and cost-efficient methods for broadly utilized FGs that can be ideally automated to speed up FG screening, monitoring, and quantification. To expand our NM surface analysis toolbox, focusing on simple methods and broadly available, cost-efficient instrumentation, we explored a NM-adapted pH titration method with potentiometric and optical readout for measuring the total number of (de)-protonable FGs on representatively chosen commercial and custom-made aminated silica nanoparticles (SiO₂ NPs). The accuracy and robustness of our stepwise optimized workflows was assessed by several operators in two laboratories and method validation was done by cross-comparison with two analytical methods relying on different signal generation principles. This included traceable, chemo-selective quantitative nuclear magnetic resonance spectroscopy (qNMR) and thermogravimetric analysis (TGA), providing the amounts of amino silanes released by particle dissolution and the total mass of the surface coatings. A comparison of the potentiometric titration results with the reporter-specific amounts of surface amino FGs determined with the previously automated fluorescamine (Fluram) assay highlights the importance of determining both quantities for surface-functionalized NMs. In the future, combined NM surface analysis with optical assays and pH titration will simplify quality control of NM production processes and stability studies and can yield large data sets for NM grouping that facilitates further developments in regulation and standardization.