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Due to the extremely high specific surface area of nanoparticles and corresponding potential for adsorption, the results of surface analysis can be highly dependent on the history of the particles, particularly regarding sample preparation and storage. The sample preparation method has, therefore, the potential to have a significant influence on the results. This report describes an interlaboratory comparison (ILC) with the aim of assessing which sample preparation methods for ToF-SIMS analysis of nanoparticles provided the most intra- and interlaboratory consistency and the least amount of sample contamination. The BAM reference material BAM-P110 (TiO2 nanoparticles with a mean Feret diameter of 19 nm) was used as a sample representing typical nanoparticles. A total of 11 participants returned ToF-SIMS data,in positive and (optionally) negative polarity, using sample preparation methods of “stick-and-go” as well as optionally “drop-dry” and “spin-coat.” The results showed that the largest sources of variation within the entire data set were caused by adventitious hydrocarbon contamination or insufficient sample coverage, with the spin-coating protocol applied in this ILC showing a tendency toward insufficient sample coverage; the sample preparation method or the participant had a lesser influence on results.
Nanoparticles have gained increasing attention in recent years due to their potential and application in different fields including medicine, cosmetics, chemistry, and their potential to enable advanced materials. To effectively understand and regulate the physico-chemical properties and potential adverse effects of nanoparticles, validated measurement procedures for the various properties of nanoparticles need to be developed. While procedures for measuring nanoparticle size and size Distribution are already established, standardized methods for analysis of their surface chemistry are not yet in place, although the influence of the surface chemistry on nanoparticle properties is undisputed. In particular, storage and preparation of nanoparticles for surface analysis strongly influences the analytical results from various methods, and in order to obtain consistent results, sample preparation must be both optimized and standardized. In this contribution, we present, in detail, some standard procedures for preparing nanoparticles for surface analytics. In principle, nanoparticles can be deposited on a suitable substrate from suspension or as a powder. Silicon (Si) Wafers are commonly used as substrate, however, their cleaning is critical to the process. For sample preparation from suspension, we will discuss drop-casting and spin-coating, where not only the cleanliness of the substrate and purity of the suspension but also its concentration play important roles for the success of the preparation methodology.
For nanoparticles with sensitive ligand shells or coatings, deposition as powders is more suitable, although this method requires particular care in fixing the sample.
The oxides, hydroxides, and oxo-hydroxides of iron belong to the most abundant materials on earth. They also feature a wide range of practical applications. In many environments, they can undergo facile phase transformations and crystallization processes.
Water appears to play a critical role in many of these processes. Despite numerous attempts, the role of water has not been fully revealed yet. We present a new approach to study the influence of water in the crystallization and phase transformations of iron oxides.
The approach employs model-type iron oxide films that comprise a defined homogeneous nanostructure. The films are exposed to air containing different amounts of water reaching up to pressures of 10 bar. Ex situ analysis via scanning electron microscopy, Transmission electron microscopy, selected area electron diffraction, and X-ray diffraction is combined with operando near-ambient pressure X-ray photoelectron spectroscopy to follow water-induced changes in hematite nd ferrihydrite. Water proves to be critical for the nucleation of ematite domains in ferrihydrite, the resulting crystallite orientation, and the underlying crystallization mechanism.
Spectroscopy in Catalysis
(2020)
Knowledge-based catalyst development is always an interaction between preparation, analysis and catalytic testing. Only if these three factors fit together can success be expected. For the analytic side of this triangle, spectroscopic methods play a crucial role. Whereas with diffraction, scattering and microscopy, decisive insights into the structure and morphology of the catalysts can be obtained, spectroscopy produces new knowledge about the chemical nature of the catalyst, e.g., its bonding and valence states.
Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines.
Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational ‘safe-by-design’ approaches to facilitate NM commercialization.