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Ellipsometry has been an extremely successful and fast expanding method in the past decades along with other related techniques using polarisation sensitive measurements. Opening new fields of application for a successful measurement technique brings some requirements and issues that have to be solved. From a metrological point of view, ellipsometry has the problem that uncertainties are difficult to determine for model-based analysis techniques in general. In this presentation, we will explore how the usefulness of polarimetric methods like ellipsometry can be increased.
Ellipsometry as a method could profit from several current developments which will be discussed in this presentation:
• Standardisation initiatives on national and international level developing standards for best practice when using ellipsometry. A series of at least six standards is currently developed on national German and international level covering different levels of sample complexity.
• Projects on traceability of ellipsometry and structured surface spectrometry as well as new dielectric function database initiatives.
• Metadata handling and data ontology providing a better framework for exchange and collaborative use of research data.
We will also explore the quantification of measurement uncertainty using examples from projects in which BAM is involved. Examples will be presented of multilayer and non-ideal materials as well as the determination of layer properties for technical applications such as thin layer catalysts and complex polymers. The definition of reference materials will be discussed.
Driven by recent technological advances and the need for improved viral diagnostic applications, mass spectrometry-based proteomics comes into play for detecting viral pathogens accurately and efficiently. However, the lack of specific algorithms and software tools presents a major bottleneck for analyzing data from host-virus samples. For example, accurate species- and strain-level classification of a priori unidentified organisms remains a very challenging task in the setting of large search databases. Another prominent issue is that many existing solutions suffer from the protein inference issue, aggravated because many homologous proteins are present across multiple species. One of the contributing factors is that existing bioinformatic algorithms have been developed mainly for single-species proteomics applications for model organisms or human samples. In addition, a statistically sound framework was lacking to accurately assign peptide identifications to viral taxa. In this presentation, an overview is given on current bioinformatics developments that aim to overcome the above-mentioned issues using algorithmic and statistical methods. The presented methods and software tools aim to provide tailored solutions for both discovery-driven and targeted proteomics for viral diagnostics and taxonomic sample profiling. Furthermore, an outlook is provided on how the bioinformatic developments might serve as a generic toolbox, which can be transferred to other research questions, such as metaproteomics for profiling microbiomes and identifying bacterial pathogens.