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Nanomaterials are present in our everyday life. Paint coats, sunscreens, catalysts and additives for tyres are good examples for the use of such materials in mass-market products. The problem of the safety of nanomaterials is recognized as a problem for health and environment, which lead to the special registration of nanomaterials according to an annex of REACH as of 2020. But a great problem for the risk assessment of nanomaterials that several factors could influence the hazardous nature of them. Additional to composition, crystal structure, size and shape the surface properties of such particles belong to these parameters for risk assesment. The reason for the relevance of the surface is obvious: the smaller the particle, the higher is the share of the surface. Additionally, the surface is the region of the particle which interacts with the surrounding which is another crucial factor for the understanding the effect of a nanomaterial on health and environment. In the OECD Testing Programme on Manufactured Nanomaterials exists consequently an Endpoint 4.30 Surface Chemistry in Chapter 4. PHYSICAL AND CHEMICAL PROPERTIES. In summary, there is obviously a need for a correlation between surface chemical analytic data and toxicity. To fill in this gap, we present surface analytic results obtained with X-ray photoelectron spectroscopy and Time-of-Flight Secondary Ion Mass Spectrometry and correlate them with cytotoxic data gain by high-throughput screening experiments. It must be noted, that these experiments were done at the same set of titania materials taken from the JRC (Joint Research Centre of the European Union) Nanomaterials Repository. As material TiO2 was chosen due to its widespread use in consumer products, e.g. paint coats and sunscreens. With this new approach a better understanding of the influence of surface properties on the toxicity can be expected leading to a better risk assessment of these materials.
Die zunehmenden Nachweise von Kunststoffen in verschiedenen Umweltmedien erfahren in den letzten Jahren weltweit ein zunehmendes wissenschaftliches, gesellschaftliches und politisches Interesse. Besondere Aufmerksamkeit erfahren Kunststoffpartikel von 1-1.000 µm, sogenanntes Mikroplastik. Es wurde mittlerweile in Wasser, Böden, Luft und Biota nachgewiesen. Obwohl die Wirkungen auf die natürlichen Umweltfunktionen noch unklar sind und kontrovers diskutiert werden, ist allein durch die ubiquitäre Präsenz eine systematische Erfassung von Eintragsquellen und Verbleib der Partikel über alle Umweltmedien notwendig, um Maßnahmen zur Reduktion der Einträge zu veranlassen.
Für eine zuverlässige Erfassung sind medienübergreifende Verfahren zu entwickeln, welche die drei analytischen Schritte, Probennahme, Probenaufbereitung und Detektion beinhalten. Die Verfahren adressieren unterschiedliche Zielgruppen, welche verschiedene analytische Informationen fordern. Für die akademische Bewertung werden Verfahren mit hohem analytischen Aufwand favorisiert, welche möglichst detaillierte Information über Partikelsorte, -größe, -zahl und -oberflächenbeschaffenheit geben. Im Gegensatz dazu, fordern Anwender aus der Praxis schnelle und einfache Monitoring Verfahren, welche die Bestimmung eines Massengehaltes, bzw. eines Grenzwertes zulassen. Allen ist gemeinsam, dass ein hoher interdisziplinärer Anspruch besteht, der sowohl technische Voraussetzungen zur repräsentativen Probennahme und die Limitationen von Detektionsverfahren berücksichtigt, als auch die Fachkompetenz von Biologen, Umweltwissenschaften, Analytiker und Polymerwissenschaftler einschließt.
Verfahren zum Mikroplastiknachweis müssen auch verschieden Proben mit sehr unterschiedlichen Gehalten an natürlicher Matrix gerecht werden. So müssen allein für Wasser Verfahren entwickelt werden, die Mikroplastik verschiedener Form (sphärische Partikel, komplexe Bruchstücke, Fasern) aus Medien mit sehr variablen Partikelanteil (u.a. Trinkwasser, Oberflächenwasser, Abwasser) repräsentativ erfassen, sowie schnell und kostengünstig sind – einschließlich der notwendigen Probenaufbereitung. Die jeweiligen Detektionsverfahren müssen z.B. einzelne Partikel oder integrale Gehalte über drei Dekadenklassen für thermoplastische und insbesondere auch elastomere Partikelsorten erfassen können.
No matter whether you are doing X-ray diffraction or scattering, at wide or small angles, in a lab or at a synchrotron, you will need a bespoke sequence of up to twenty different corrections before it gets about right (at our latest count). Our library now does this automatically and reliably to get you the data you deserve – in absolute units and with uncertainty estimates – without the pain. This talk will highlight the development of the modular library, the sequence and its foundation, show its applicability to real-world datasets, and highlight a possible way forward
In X-ray fluorescence (XRF), a sample is excited with X-rays, and the resulting characteristic radiation is detected to detect elements quantitatively and qualitatively. Quantification is traditionally done in several steps:
1. Normalization of the data
2. Determination of the existing elements
3. Fit of the measured spectrum
4. Calculation of concentrations with fundamental parameters / MC simulations / standard based
The problem with standard based procedures is the availability of corresponding standards. The problem with the calculations is that the measured intensities for XRF measurements are matrix-dependent. Calculations must, therefore, be performed iteratively (= time consuming) in order to determine the chemical composition.
First experiments with gold samples have shown the feasibility of machine learning based quantification in principle. A large number of compositions were simulated (> 10000) and analyzed with a deep learning network. For first experiments, an ANN (Artificial Neural Network) with 3 hidden layers and 33x33x33 neurons was used. This network learned the mapping of spectra to concentrations using supervised learning by multidimensional regression. The input layer was formed by the normalized spectrum, and the output layer directly yielded the searched values. The applicability for real samples was shown by measurements on certified reference materials.
Synchrotron radiation sources with their unique properties in terms of intensity, polarization and adjustability offer a wide range of possibilities in materials research. A basic introduction about the creation and special properties of synchrotron radiation will be given. Examples of current work at BAMline, the high-energy measuring facility of the Federal Institute for Materials Research and Testing at the synchrotron BESSY, are used to illustrate the possibilities and limitations of existing measuring methods. It will be shown how the formation of corrosion layers can be tracked, how the abrasion of implants leads to the introduction of heavy metals into the surrounding bone matrix and how the detection of smallest impurities in gold is possible by optimizing the measuring conditions. Finally, an outlook at the hardware and software developments to be expected in the coming years is given.
Der Vortrag gibt den aktuellen Stand der Normung im Bereich der Oberflächen-technik insbesondere der Oberflächenanalytik und der Oberflächenmess- und -prüftechnik wider. Vorgestellt wird der Status technischer Berichte, Normenentwürfe und Normen u.a. zur Charakterisierung von funktionellen Glasoberflächen für bioanalytische Anwendungen, zur Messung der Schichtdicke von Nanomaterialien mittels Ellipsometrie, zur Kalibrierung von Interferometern und Interferenzmikroskopen für die Formmessung, zur Rockwelleindringprüfung zur Bewertung der Schicht-haftung, zu den Grundlagen der Ellipsometrie, zur Messung der Schichtdicke mittels Wirbelstromverfahren, zur Schichtpotentialmessung von Mehrfach-Nickelschichten, zu Filtern und Augenschutzgeräten gegen Laserstrahlung sowie zur Bestimmung der Schichthaftung mittels Zugversuch.
µ-FTIR spectroscopy is a widely used technique in microplastics research. It allows to simultaneously characterize the material of the small particles, fibers or fragments, and to specify their size distribution and shape. Modern detectors offer the possibility to perform two-dimensional imaging of the sample providing detailed information. However, datasets are often too large for manual evaluation calling for automated microplastic identification. Library search based on the comparison with known reference spectra has been proposed to solve this problem.
To supplement this ‘targeted analysis’, an exploratory approach was tested. Principal component analysis (PCA) was used to drastically reduce the size of the data set while maintaining the significant information. Groups of similar spectra in the prepared data set were identified with cluster analysis. Members of different clusters could be assigned to different polymer types whereas the variation observed within a cluster gives a hint on the chemical variability of microplastics of the same type. Spectra labeled according to the respective cluster can be used for supervised learning. The obtained classification was tested on an independent data set and results were compared to the spectral library search approach.