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Reliable characterization of materials at the nanoscale regarding their physio-chemical properties is a challenging task, which is important when utilizing and designing nanoscale materials. Nanoscale materials pose a potential toxicological hazard to the environment and the human body. For this reason, the European Commission amended the REACH Regulation in 2018 to govern the classification of nanomaterials, relying on number-based distribution of the particle size.
Suitable methods exist for the granulometric characterization of monodisperse and ideally shaped nanoparticles. However, the evaluation of commercially available nanoscale powders is problematic. These powders tend to agglomerate, show a wide particle size distribution and are of irregular particle shape.
Zinc oxide, aluminum oxide and cerium oxide with particle sizes less than 100 nm were selected for the studies and different preparation methods were used comparatively.
First, the nanoparticles were dispersed in different dispersants and prepared on TEM-supported copper grids. Furthermore, individual powders were deposited on carbon-based self-adhesive pads. In addition, the samples were embedded by hot mounting and then ground and polished.
The prepared samples were investigated by scanning electron microscopy (including the transmission mode STEM-in-SEM) and Dynamic Light scattering. The software package ImageJ was used to segment the SEM images and obtain the particle sizes and shapes and finally the number-based particles size distribution with size expressed as various descriptors.
Colloidal metalorganic frameworks as novel biofunctional nanoparticles for immunoassay applications
(2017)
Metal-organic framework (MOF) colloids have unique features that render them ideal signalling agents for realizing advanced immunoassay-based detection systems. MOFs are porous coordination polymers of metal nodes and organic linkers. The pore size of MOFs can be engineered and tailored to allow specific host (MOF) and guest (analyte) interactions. The particle sizes of the colloidal MOF can be tailored by employing methods from colloidal chemistry in wet synthesis. The adaption of established Layer-by-Layer polyelectrolyte coating protocols [1] allows equipping colloidal MOF particles with a nanometer thin polyelectrolyte membrane. This polyelectrolyte membrane serves as an interface for antibody binding. These biofunctional MOF nanoparticles have shown a strong immuno-binding that is sufficient for solid state immunoassays. Our current research addresses the design of luminescence encoded colloidal particle libraries by adjusting the ratios of e.g. Terbium (green) and Europium (red) metal nodes in mixed lanthanide based MOF-76. These mixed lanthanide MOF-76 particles are envisioned to allow multiplexed immuno-detection of endocrine disruptors such as bisphenol A.
In addition we investigate the detection of analytes that do not allow the production of antibodies due to their inherent properties. Such “difficult analytes” have a strong hydrophobicity or are very small or highly toxic molecules. One example is the common plasticizer dioctylphthalate that is also a potent endocrine disruptor. MOF colloids can address this issue by specific host (MOF) : guest (analyte) interactions that result in analyte-specific colour change or exciplex-based fluorescence emission. Our overall aim is to develop methodologies that allow parallel sensing of two endocrine disruptors (e.g. bisphenol A & phthalates) by simultaneous immuno-detection and MOF:analyte specific interactions.
Due to the growing number of engineered nanomaterials (NM) the need for a reliable risk assessment for these materials is today bigger than ever before. Especially the nanomaterial’s surface or shell directly interacts with its environment and therefore is a crucial factor for NM’ toxicity or functionality.
Especially, titania is one of the NM with the greatest technological importance. It is used for a large number of applications and can be found in food, cosmetics, glasses, mirrors, paints to mention only a few. In 2012, experts estimate[d] the annual European nano-titania production or utilization at an amount of more than 10,000 t.
Great progress has been achieved in the area of NM investigation and characterization during the past decade. A variety of publications provide information about technological innovation as well as hazard potential, which means the potential risk on human health and ecosystems. However, enhanced data harmonization and well-defined standards for nanomaterial analysis, could significantly improve the reliability of such studies which often suffers from varying methods, parameters and sample preparations. To develop a suitable approach for the NM’s risk assessment, the ACEnano project aims at establishing a toolbox of verified methods. The size of this well-structured European project allows to handle even those big challenges like data harmonization and standardization.
Due to its powerful combination of superior surface sensitivity and lateral resolution down to the Nano regime, ToF-SIMS could become one of these toolbox methods. Supported by multivariate data analysis such as principal component analysis (PCA), the method can be used for sub-classification of nanomaterial families using slight differences in surface chemistry.
Here, we show a PCA supported classification of titania nanoparticles from various sources (NIST, JRC, BAM) with ToF-SIMS. Parameters like size, shell, pre-preparation and crystal system cause variance in the data and allow us to distinguish the species from each other. Moreover, this variance in the data also occurs and can be used for investigation when we compare our measurements of particle ensembles with those of grown titania films. The carefully selected and refined peaks allow a reasonable particle categorization and further a reliable allocation of blank feeds, which introduces a promising approach for NM characterization in the context of NM risk assessment.
This talk was given within the scope of the SIMS-22 conference in October 2019 in Kyoto (Japan). It deals with the surface analytical investigation of nanoparticles by PCS-assisted ToF-SIMS. This technique is applicable to core-shell nanoparticles, in order to distinguish a complete encapsulation from an incomplete encapsulation of the core by the shell material. Furthermore, the depletion process of organic nanoparticle coatings caused by UV-weathering is investigated. Finally, the significance of grouping studies for nanomaterials research and risk assessment is demonstrated.
The analysis of nanomaterials is currently an important task - especially in case of risk assessment – as the properties of these material class are not well understood. The rather high surface area of these objects renders their interactions significantly different to their corresponding bulk. Thus, the surface’s chemical composition must be investigated to get a better understanding and prediction of the nanomaterials’ behavior. ToF-SIMS and XPS have proven to be powerful tools to determine the general chemical composition. The superior surface sensitivity of ToF-SIMS furthermore allows us to study mainly the utmost atomic layers and thus gives us an idea of the interactions involved.
Here, we present initial data on the analysis of Hyflon®-polystyrene core-shell nanoparticles which can be used as a model system due to the known preparation and a rather good chemical as well as physical separation of core and shell. Furthermore, results on Au nanoparticles with and without an antibody shell are presented. Principle component analysis (PCA) will be used to detect the influence of sample preparation and for a better separation of different samples. ToF-SIMS imaging is desired to be implemented for single particle detection as well.
The analysis of nanomaterials is currently an important task - especially in case of risk assessment – as the properties of these material class are not well understood and their growing use in everyday life. The rather high surface area of these objects renders their interactions significantly different to their corresponding bulk. Thus, the surface’s chemical composition must be investigated to get a better understanding and prediction of the nanomaterials’ behaviour. ToF-SIMS and XPS have proven to be powerful tools to determine the general chemical composition. The superior surface sensitivity of ToF-SIMS furthermore allows us to study mainly the utmost atomic layers and thus gives us an idea of possible interactions involved. Supported by multivariate data analysis such as principal component analysis (PCA), the method can also be used for sub-classification of different materials using slight differences in surface chemistry.
Here, we present data of the analysis of Hyflon®-polystyrene core-shell nanoparticles which can be used as a model system due to the known preparation and a rather good chemical as well as physical separation of core and shell. Principle component analysis (PCA) will be used to detect the influence of sample preparation and for a better separation of different samples. This is achieved by measurement of a statistically relevant set of samples for every particle sample. We acquired surface spectra under static SIMS conditions with Bi32+ and analysed the resulting spectra by PCA. The carefully selected and refined peaks allow a reasonable categorization and further a reliable allocation of blank feeds. In detail, the fluorine containing, organic fragments are an indication for a heterogeneous shell that has errors. Furthermore, results on Au nanoparticles with and without an antibody shell are presented. ToF-SIMS imaging is desired to be implemented for single particle detection as well.
The analysis of nanomaterials is current an important task - especially in case of risk assessment, as the properties of these material class are not well understood currently. The rather high surface area of these objects renders their interactions significantly different to their corresponding bulk. Thus, the surfaces chemical composition has to be investigated to get a better understanding and prediction of the nanomaterials' behavior. ToF-SIMS has proven as a powerful tool to determine said chemical composition. Its superior surface sensitivity allows us to study mainly the utmost atomic layer and therefore gives us an idea of the interactions involved. Here, we show first result from the validation of the method for the analysis of polystyrene and gold nanoparticles. ToF-SIMS will be compared to other methods like XPS, T-SEM or REM. Furthermore, principle component analysis (PCA) will be used to detect the influence of different sample preparation performed by an innovative microfluidic device. ToF-SIMS imaging is desired to be implemented for single particle detection as well.
The analysis of nanomaterials is currently an important task - especially in case of risk assessment – as the properties of these material class are not well understood. The rather high surface area of these objects renders their interactions significantly different to their corresponding bulk. Thus, the surface’s chemical composition must be investigated to get a better understanding and prediction of the nanomaterials’ behavior. ToF-SIMS and XPS have proven to be powerful tools to determine the general chemical composition. The superior surface sensitivity of ToF-SIMS furthermore allows us to study mainly the utmost atomic layer and thus gives us an idea of the interactions involved. Here, we present initial data on the analysis of Hyflon®-polystyrene core-shell nanoparticles which can be used as a model system due to the known preparation and a rather good chemical as well as physical separation of core and shell. Furthermore, principle component analysis (PCA) will be used to detect the influence of sample preparation and for a better separation of different samples. ToF-SIMS imaging is desired to be implemented for single particle detection as well.
A combined methodical approach is tested for the first time with respect to the characterization of the inner structure of porous TiO2 layers infiltrated with ruthenium molecular sensitizer as typically used in modern dye sensitized solar cells (DSSC). Their performance is directly linked to the surface area ‘offered’ by the pore Network to the dye. The micrometer thick layer employed was manufactured by screen-printing of a starting TiO2 powder constituted of shape-controlled, bipyramidal anatase nanoparticles (NPs) on FTO/glass substrates. The
analytical methods exploited in our study are Focused Ion Beam (FIB) slicing followed by 3D reconstruction as well as the new approach transmission Kikuchi diffraction (TKD) technology in the scanning electron microscope (SEM). While the first method results in the visualization of the 3D pore network within the TiO2 layer, the second one can be successfully applied to analyze the crystal orientation of grains (i.e. NPs in our case) in nanometer resolution. Moreover, size and shape distribution of the TiO2 NPs within the layer can be extracted.
SEM in transmission mode and atomic force microscopy (AFM) have been used to verify the dimensional data obtained by the new combined methodical approach. Its analytical benefits but also the challenges and limitations
are highlighted.