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The wide use of nanoforms with at least one dimension below 100 nm in our daily life requires a detailed knowledge of their physicochemical properties which are needed for risk assessment or quality control. Therefore, a comprehensive characterization of these properties was considered as relevant including: chemical composition, crystallinity, particle size, particle shape, surface chemistry, and specific surface area (SSA).
We want to discuss, how Scanning Electron Microscopy (SEM), Electron Probe Microanalysis (EPMA) in the version with energy dispersive X-ray spectroscopy (EDS), Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) and X-ray Photoelectron Spectroscopy (XPS) can contribute to gain comprehensive insights into the nature of the nanoparticles. SEM results provide the particle size and shape (distribution). A quick identification of the main chemical elements present in the sample can be obtained with EDS, whereas XPS allows a more detailed chemical identification of the small nanoparticles below 20 nm or of the near-surface region of larger particles. ToF-SIMS is even much more surface-sensitive and leads to a deeper understanding of the surface chemistry of the nanoparticles. As exemplary samples, two Al-coated TiO2 samples in nanopowder form were chosen from the JRC repository, capped either with a hydrophilic or a hydrophobic organic shell.
A focus of our case study was to show, how reliable, reproducible and traceable data can be obtained. Therefore, each step in the workflow of sample investigation must be described in detail. For the most of these steps, well-established standards are available. Usually, the conditions of the particular measurements with each analysis method are saved as meta-data in the common file formats. But other factors like sample preparation and data reduction approaches may influence the result of the investigations in a significant manner and must be described often in a separate file (as a protocol) together with the data file. For sensitive materials like nanoobjects, the preparation of the sample influences the results crucially, e.g. measured as suspension or as powders. Furthermore, data reduction like selection of relevant peaks in spectra or particles in images, background subtraction, peak deconvolution, models for the quantification of the spectra must be considered in the interpretation of the results ideally with associated individual measurement uncertainties. Only a detailed description of all these factors allows to obtain a comprehensive characterization with reliable, reproduceable and traceable data. Examples of standardized procedures of measurement or on data reduction will be highlighted.
We thank for the funding from the European Unions’s Horizon 2020 for the project NanoSolveIt (grant agreement No. 814572) and for the project NANORIGO (grant agreement No. 814530).
Nanomaterials bring various benefits and have become a part of our daily lives. However, the risks emerging from nanotechnology need to be minimized and controlled at the regulatory level and therefore, there is a need for nanorisk governance. One of the prerequisites for successful nanorisk governance is the availability of high-quality data on nanomaterials and their impact with the human body and the environment. In recent decades, a countless number of publications and studies on nanomaterials and their properties have been produced due to the fast development of nanotechnology. Despite such a vast amount of data and information, there are certain knowledge gaps hindering an efficient nanorisk governance process. Knowing the state of the available data and information is an important requirement for any decision maker in dealing with risks. In the specific case of nanotechnology, where most of the risks are complex, ambiguous, and uncertain in nature, it is essential to obtain complete data and metadata, to fill knowledge gaps, and to transform the available knowledge into functional knowledge. This can become possible using a novel approach developed within the NANORIGO project (Grant agreement No. 814530) – the Knowledge Readiness Level (KaRL). In analogy to NASA’s Technology Readiness Levels (TRLs), we define KaRLs as a categorization system of data, information, and knowledge which enables transformation of data and information into functional knowledge for nanorisk governance. Our approach goes beyond the technical curation of data and metadata and involves quality and completeness filters, regulatory compliance requirements, nanorisk-related tools, and most importantly, human input (inclusion of all stakeholder groups). With the KaRL approach we also address key issues in nanotechnology such as societal and ethical concerns, circular economies and sustainability, the Green Deal, and the traceability of data, knowledge, and decisions. The KaRL approach could be used for nanorisk governance by a nanorisk governance council (NRGC), which is currently under development by three EU projects (NANORIGO, GOV4NANO, and RISKGONE).
Nanomaterials bring various benefits and have become a part of our daily lives. However, the risks emerging from nanotechnology need to be minimized and controlled at the regulatory level and therefore, there is a need for nanorisk governance. One of the prerequisites for successful nanorisk governance is the availability of high-quality data on nanomaterials and their impact with the human body and the environment. In recent decades, a countless number of publications and studies on nanomaterials and their properties have been produced due to the fast development of nanotechnology. Despite such a vast amount of data and information, there are certain knowledge gaps hindering an efficient nanorisk governance process. Knowing the state of the available data and information is an important requirement for any decision maker in dealing with risks. In the specific case of nanotechnology, where most of the risks are complex, ambiguous, and uncertain in nature, it is essential to obtain complete data and metadata, to fill knowledge gaps, and to transform the available knowledge into functional knowledge. This can become possible using a novel approach developed within the NANORIGO project (Grant agreement No. 814530) – the Knowledge Readiness Level (KaRL). In analogy to NASA’s Technology Readiness Levels (TRLs), we define KaRLs as a categorization system of data, information, and knowledge which enables transformation of data and information into functional knowledge for nanorisk governance. Our approach goes beyond the technical curation of data and metadata and involves quality and completeness filters, regulatory compliance requirements, nanorisk-related tools, and most importantly, human input (inclusion of all stakeholder groups). With the KaRL approach we also address key issues in nanotechnology such as societal and ethical concerns, circular economies and sustainability, the Green Deal, and the traceability of data, knowledge, and decisions. The KaRL approach could be used for nanorisk governance by a nanorisk governance council (NRGC), which is currently under development by three EU projects (NANORIGO, GOV4NANO, and RISKGONE).