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Reaction conditions have a huge impact on the resulting polymer properties, but capturing this requires understanding the correlation of the underlying kinetics, the polymer architecture, and polymer flow behavior. Long‐chain branched polymers created randomly by free‐radical polymerization, such as low‐density polyethylene (LDPE), show complex rheological behavior and are thus interesting in this context. A study applying a multiscale modeling approach is used to simulate varying reaction conditions and predict the structure of the resulting LDPE polymer and its flow properties. A significant effect on the molecular weight distribution, but also the viscosity and extensional flow behavior can be predicted. Higher conversions, for example, lead to broader molecular weight distributions, increased long‐chain branching degrees, and a higher branching complexity. Consequently, also higher viscosities and increased strain hardening are observed in extension. Additionally, miniplant experiments are performed to resemble the simulations and compare the results. The accordance of predictions and analytical results are very good and validate the model over a wide range of reaction conditions.

A three‐step modeling approach to obtain rheological properties from the kinetics of the metallocene‐catalyzed homopolymerization of ethene is developed. With that, linear as well as slightly branched high‐density polyethylene is investigated. Therefore, a deterministic kinetic model of the metallocene‐catalyzed polymerization of ethene is set up and coupled with a Monte Carlo algorithm to obtain molecular weight distributions and polymeric microstructures. The topological information is used as input for the rheology model to compute linear and non‐linear rheological properties.

Mixing in low‐density polyethylene (LDPE) reactors is crucial regarding process safety as well as process efficiency. The extreme process conditions of pressures up to 3000 bar make it difficult to get an insight into LDPE reactors. This problem is tackled by a combined experimental and modeling approach. A poly(methylmethacrylate) duplicate of a laboratory high‐pressure reactor is set up to measure residence times and visualize flow lines. Experimental results are then compared to a computational fluid dynamics model.

Well-defined mini-plant low density polyethylene samples were fractionated preparatively according to their crystallizability via preparative temperature rising elution fractionation and according to molecular weight via preparative solvent gradient fractionation (pSGF). Rheology of the fractions was measured in both the small amplitude oscillatory shear (SAOS) and the non-linear extension regimes. The linear and non-linear rheology of the pTREF fractions were dominated by molecular weight effects, while the impact of the higher degree of long chain branching for the pSGF fractions with higher molecular weights was observed in van Gurp–Palmen plots and in strain hardening behavior in the extensional rheology measurements. Additionally, the experimental fractionation process was mimicked via modelling. The branching topologies of the bulk samples were obtained by coupled kinetic and Monte Carlo calculations. These topologies were fractionated computationally and the result were used to predict the rheological behavior of the individual fractions by applying the BoB algorithm with no parameter adjustment. The experimental observed trends were predicted by the model and the overall agreement was acceptable. This study demonstrates, that polymer fractionation is possible on a preparative scale and allows for the polymer flow properties characterization of the individual fractions, a method that is highly relevant during processing. Moreover, the fractionation process is followed and understood from the modelling point of view.

A three‐step multiscale modeling approach to predict and thus control polymer properties, such as flow behavior and linear as well as non‐linear rheology, based on polymerization conditions, is developed and applied for long‐chain branched low‐density polyethylene (LDPE). The approach consists of i) a deterministic kinetic model for the description of conversion and average polymer characteristics, ii) a hybrid stochastic Monte Carlo model for the description of the polymeric microstructure, and iii) a rheology model for the evaluation of polymer melt flow properties. The modeling approach is validated via high‐pressure miniplant LDPE samples with a special focus on long‐chain branching. In the next step, the modeling approach can be successfully transferred to a tubular reactor of industrial scale. Due to its universality the approach opens up possible applications for other polymer and also copolymer systems.

The SARS-CoV-2 coronavirus has spread rapidly across Germany. Infections are likely to be under-recorded in the notification data from local health authorities on laboratory-confirmed cases since SARS-CoV-2 infections can proceed with few symptoms and then often remain undetected. Seroepidemiological studies allow the estimation of the proportion in the population that has been infected with SARS-CoV-2 (seroprevalence) as well as the extent of undetected infections. The ‘CORONA-MONITORING bundesweit’ study (RKI-SOEP study) collects biospecimens and interview data in a nationwide population sample drawn from the German Socio-Economic Panel (SOEP). Participants are sent materials to self-collect a dry blood sample of capillary blood from their finger and a swab sample from their mouth and nose, as well as a questionnaire. The samples returned are tested for SARS-CoV-2 IgG antibodies and SARS-CoV-2 RNA to identify past or present infections. The methods applied enable the identification of SARS-CoV-2 infections, including those that previously went undetected. In addition, by linking the data collected with available SOEP data, the study has the potential to investigate social and health-related differences in infection status. Thus, the study contributes to an improved understanding of the extent of the epidemic in Germany, as well as identification of target groups for infection protection.

Das Coronavirus SARS-CoV-2 hat sich in kurzer Zeit bundesweit ausgebreitet. In den Meldedaten der Gesundheitsämter zu laborbestätigten Infektionsfällen ist von einer Untererfassung des Infektionsgeschehens auszugehen, da Infektionen häufig unentdeckt bleiben, zum Beispiel weil sie symptomarm verlaufen. In seroepidemiologischen Studien kann der Bevölkerungsanteil mit durchgemachter SARS-CoV-2-Infektion (Seroprävalenz) wie auch der Umfang unentdeckter Infektionen abgeschätzt werden. In der Studie CORONA-MONITORING bundesweit (RKI-SOEP-Studie) werden Bioproben und Befragungsdaten in einer deutschlandweiten Bevölkerungsstichprobe des Sozio-oekonomischen Panels (SOEP) erhoben. Den Teilnehmenden werden Materialien zur selbstständigen Gewinnung einer Trockenblutprobe aus Kapillarblut des Fingers und einer Abstrichprobe aus Mund und Nase sowie ein Fragebogen postalisch zugesendet. Die zurückgesendeten Proben werden auf SARS-CoV-2-IgG-Antikörper und SARS-CoV-2-RNA zur Identifikation einer durchgemachten oder aktuellen Infektion untersucht. Die eingesetzten Methoden ermöglichen es, auch solche SARS-CoV-2-Infektionen zu erkennen, die bislang unentdeckt blieben. Durch die Verknüpfung mit bereits vorhandenen SOEP-Daten hat die Studie das Potenzial, auch soziale und gesundheitsbezogene Unterschiede im Infektionsstatus zu untersuchen. So kann die Studie zu einem verbesserten Verständnis des Ausmaßes der Epidemie in Deutschland wie auch zur Identifikation von Zielgruppen für den Infektionsschutz beitragen.