Analytische Chemie
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The primary crystallite size of titania powder relates to its properties in a number of applications. Transmission electron microscopy was used in this interlaboratory comparison (ILC) to measure primary crystallite size and shape distributions for a commercial aggregated titania powder. Data of four size descriptors and two shape descriptors were evaluated across nine laboratories. Data repeatability and reproducibility was evaluated by analysis of variance. One-third of the laboratory pairs had similar size descriptor data, but 83% of the pairs had similar aspect ratio data. Scale descriptor distributions were generally unimodal and were well-described by lognormal reference models. Shape descriptor distributions were multi-modal but data visualization plots demonstrated that the Weibull distribution was preferred to the normal distribution. For the equivalent circular diameter size descriptor, measurement uncertainties of the lognormal distribution scale and width parameters were 9.5% and 22%, respectively. For the aspect ratio shape descriptor, the measurement uncertainties of the Weibull distribution scale and width parameters were 7.0% and 26%, respectively. Both measurement uncertainty estimates and data visualizations should be used to analyze size and shape distributions of particles on the nanoscale.
The synthesis of TiO2 nanoplatelets with fluorine-containing reactants is carried out using Ti (IV) butoxide as precursor and concentrated HF as shape controller, the final product requires a working up in order to eliminate or at least to reduce the amount of residual fluorides, which is realized here by well-defined thermal treatment.
Qualitative investigation of the bulk elemental composition by means of EDX of TiO2 nanoparticles (NPs) has identified fluorine in case of the as-synthesized samples. EDX spectra of thermally treated products exhibit either a fluorine content close to the limit of detection. The latter holds also true for the reference sample, TiO2 NPs of bipyramidal shape and prepared by a different synthesis route. For differentiation whether fluorine is present in the bulk or at the surface of the TiO2 nanoplatelets, top-surface sensitive AES and ToF-SIMS has been applied. Secondary ions of fluorine are detected in ToF-SIMS spectra of all samples, but could be roughly quantified by measurement of same reference sample as for EDX, namely TiO2 nano-bipyramids. This revealed that the amount of fluorine within1 nm depth beneath the surface is reduced in the thermally treated specimen compared to the raw product down to a content about as low as in the reference sample. AES allows analyzing analysis of the first few nanometers from the top-surface of individual NPs by point analysis. An F KLL peak has been detected at the surface of samples of as-prepared TiO2 nanoplatelets under optimized measurement conditions, but was not detectable after their calcination, which is in agreement with ToF-SIMS results. Moreover, high resolution AES on single TiO2 nanoplatelets elucidated that the surface atomic layers surrounding the TiO2 nanopaltelet contain fluorides before thermal treatment of the NPs.
Self-assembly of TiO2 nanoparticles in multiple layers by layer-by-layer deposition has been selected of different deposition procedures usually applied for fabrication of TiO2 thin films with defined and homogeneous thickness on supports of interest for the large-scale applications. The substrates tested were: conductive (FTO) glass, silica glass and titanium alloy. The selected film fabrication technique consists of the deposition of alternating layers of oppositely charged, i.e. functionalized, TiO2 nanoparticle layers with wash steps in between. The controlled assembly of TiO2 nanoparticles on the supports surface requires both a proper functionalization of the supports to promote the adhesion of the TiO2 film to the substrates and proper functionalization of TiO2 nanoparticles to allow attachment to substrate and subsequent reaction between different NP layers. The current study focusses on the analytical control of the functionalization of the substrates with 3-Aminopropyltriethoxysilane (APTS) and glutaraldehyde (GA) by means of surface sensitive methods, XPS, Auger Electron Spectroscopy (AES) and ToF-SIMS. Chemical composition of surface of functionalized substrates shows differences in the degree and type of modification in dependence on substrate.
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.
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.
Nanoforms with at least one dimension below 100 nm have an important part to play in more and more areas of our daily life. Therefore, risk assessment of these materials is becoming increasingly important. In this context, the European Chemical Agency (ECHA) considered eleven physico-chemical properties as relevant, of which the following six are essential for the registration: chemical composition, crystallinity, particle size, particle shape, surface chemistry and specific surface area. Four of these priority properties can be obtained with electron microscopy and surface analytics like XPS and ToF-SIMS. The reliability of this data must be ensured, especially for their use for grouping and read across approaches. On the other hand, the “reproducibility” crisis has revealed major shortcomings in the reliability of published data.
In a case study, we show how the quality of the data can be ensured by using existing standards and protocols of each step in the workflow of sample characterization. As exemplary samples, two Al-coated TiO2 samples as nanopowders were selected from the JRC repository, capped either with a hydrophilic or a hydrophobic organic ultrathin shell. SEM results provided the size and shape of the nanoparticles, a first overview about the composition was obtained with EDS. XPS and ToF-SIMS supplied the surface chemistry, especially information about the shell and the coating of the particles. Standards and protocols of all steps of the analytical workflow including preparation and data reduction are discussed regarding reliable and reproducible data. Additionally, uncertainties for the different steps are specified.
Only such a detailed description of all these factors allows a comprehensive physico-chemical characterization of the nanoparticles with understanding of their potential risk assessment.
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.