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Until now, automation in nanomaterial research has been largely focused on the automated synthesis of engineered nanoparticles (NPs) including the screening of synthesis parameters and the automation of characterization methods such as electron microscopy. Despite the rapidly increasing number of NP samples analyzed due to increasing requirements on NP quality control, increasing safety concerns, and regulatory requirements, automation has not yet been introduced into workflows of analytical methods utilized for screening, monitoring, and quantifying functional groups (FGs) on NPs. To address this gap, we studied the potential of simple automation tools for the quantification of amino surface groups on different types of aminated NPs, varying in size, chemical composition, and optical properties, with the exemplarily chosen sensitive optical fluorescamine (Fluram) assay. This broadly applied, but reportedly error-prone assay, which utilizes a chromogenic reporter, involves multiple pipetting and dilution steps and photometric or fluorometric detection. In this study, we compared the influence of automated and manual pipetting on the results of this assay, which was automatically read out with a microplate reader. Special emphasis was dedicated to parameters like accuracy, consistency, achievable uncertainties, and speed of analysis and to possible interferences from the NPs. Our results highlight the advantages of automated surface FG quantification and the huge potential of automation for nanotechnology. In the future, this will facilitate process and quality control of NP fabrication, surface modification, and stability monitoring and help to produce large data sets for nanomaterial grouping approaches for sustainable and safe-by-design, performance, and risk assessment studies.
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