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This study investigates the initial stage of the thermo-mechanical crystallization behavior for uni- and biaxially stretched polyethylene. The models are based on a mesoscale molecular dynamics approach. We take constraints that occur in real-life polymer processing into account, especially with respect to the blowing stage of the extrusion blow-molding process. For this purpose, we deform our systems using a wide range of stretching levels before they are quenched. We discuss the effects of the stretching procedures on the micro-mechanical state of the systems, characterized by entanglement behavior and nematic ordering of chain segments. For the cooling stage, we use two different approaches which allow for free or hindered shrinkage, respectively. During cooling, crystallization kinetics are monitored: We precisely evaluate how the interplay of chain length, temperature, local entanglements and orientation of chain segments influence crystallization behavior. Our models reveal that the main stretching direction dominates microscopic states of the different systems. We are able to show that crystallization mainly depends on the (dis-)entanglement behavior. Nematic ordering plays a secondary role.
In this study, we investigate the thermo-mechanical relaxation and crystallization behavior of polyethylene using mesoscale molecular dynamics simulations. Our models specifically mimic constraints that occur in real-life polymer processing: After strong uniaxial stretching of the melt, we quench and release the polymer chains at different loading conditions. These conditions allow for free or hindered shrinkage, respectively. We present the shrinkage and swelling behavior as well as the crystallization kinetics over up to 600 ns simulation time. We are able to precisely evaluate how the interplay of chain length, temperature, local entanglements and orientation of chain segments influences crystallization and relaxation behavior. From our models, we determine the temperature dependent crystallization rate of polyethylene, including crystallization onset temperature.
Ressourceneffiziente Optimierung von Hohlkörpern aus Kunststoff mittels Multiskalensimulation
(2017)
Ressourceneffiziente Optimierung von Hohlkörpern aus Kunststoff mittels Multiskalensimulation
(2017)
Die mechanischen Eigenschaften von extrusionsblasgeformten Kunststoffhohlkörpern hängen wesentlich von den vom Verarbeitungsprozess beeinflussten Materialeigenschaften ab. Ziel der dargestellten Untersuchung ist, prozessabhängige Materialkennwerte in Simulationsprogrammen zu berücksichtigen und damit deren Vorhersagegenauigkeit zu erhöhen. Hierzu ist die Schaffung einer Schnittstelle zwischen Prozess- und Bauteilsimulation notwendig. Darüber hinaus wird vorgestellt, wie Simulationen auf Mikroebene (molekulardynamische Simulationen) genutzt werden können, um Materialkennwerte ohne die Durchführung eines Realexperiments zu ermitteln.
The mechanical properties of plastic components, especially if they are made of semi-crystalline polymers, are considerably influenced by the process conditions. The degree of crystallization influences thermal and mechanical properties. Even more important is the orientation of molecules due to stretching of the polymer melt. Anisotropic material properties are the result of such orientations. Up to now all these effects are not considered within the simulation models of blow molded parts.
Users should always play a central role in the development of (software) solutions. The human-centered design (HCD) process in the ISO 9241-210 standard proposes a procedure for systematically involving users. However, due to its abstraction level, the HCD process provides little guidance for how it should be implemented in practice. In this chapter, we propose three concrete practical methods that enable the reader to develop usable security and privacy (USP) solutions using the HCD process. This chapter equips the reader with the procedural knowledge and recommendations to: (1) derive mental models with regard to security and privacy, (2) analyze USP needs and privacy-related requirements, and (3) collect user characteristics on privacy and structure them by user group profiles and into privacy personas. Together, these approaches help to design measures for a user-friendly implementation of security and privacy measures based on a firm understanding of the key stakeholders.
The analysis of used engine oils from industrial engines enables the study of engine wear and oil degradation in order to evaluate the necessity of oil changes. As the matrix composition of an engine oil strongly depends on its intended application, meaningful diagnostic oil analyses bear considerable challenges. Owing to the broad spectrum of available oil matrices, we have evaluated the applicability of using an internal standard and/or preceding sample digestion for elemental analysis of used engine oils via inductively coupled plasma optical emission spectroscopy (ICP OES). Elements originating from both wear particles and additives as well as particle size influence could be clearly recognized by their distinct digestion behaviour. While a precise determination of most wear elements can be achieved in oily matrix, the measurement of additives is performed preferably after sample digestion. Considering a dataset of physicochemical parameters and elemental composition for several hundred used engine oils, we have further investigated the feasibility of predicting the identity and overall condition of an unknown combustion engine using the machine learning system XGBoost. A maximum accuracy of 89.6% in predicting the engine type was achieved, a mean error of less than 10% of the observed timeframe in predicting the oil running time and even less than 4% for the total engine running time, based purely on common oil check data. Furthermore, obstacles and possibilities to improve the performance of the machine learning models were analysed and the factors that enabled the prediction were explored with SHapley Additive exPlanation (SHAP). Our results demonstrate that both the identification of an unknown engine as well as a lifetime assessment can be performed for a first estimation of the actual sample without requiring meticulous documentation.
Operating an ozone-evolving PEM electrolyser in tap water: A case study of water and ion transport
(2022)
While PEM water electrolysis could be a favourable technique for in situ sanitization with ozone, its application is mainly limited to the use of ultrapure water to achieve a sufficient long-time stability. As additional charge carriers influence the occurring transport phenomena, we investigated the impact of different feed water qualities on the performance of a PEM tap water electrolyser for ozone evolution. The permeation of water and the four most abundant cations (Na+, K+, Ca2+, Mg2+) is characterised during stand-by and powered operation at different charge densities to quantify underlying transport mechanisms. Water transport is shown to linearly increase with the applied current (95 ± 2 mmol A−1 h−1) and occurs decoupled from ion permeation. A limitation of ion permeation is given by the transfer of ions in water to the anode/PEM interface. The unstabilized operation of a PEM electrolyser in tap water leads to a pH gradient which promotes the formation of magnesium and calcium carbonates and hydroxides on the cathode surface. The introduction of a novel auxiliary cathode in the anolytic compartment has shown to suppress ion permeation by close to 20%.
Monitoring the content of dissolved ozone in purified water is often mandatory to ensure the appropriate levels of disinfection and sanitization. However, quantification bears challenges as colorimetric assays require laborious off-line analysis, while commercially available instruments for electrochemical process analysis are expensive and often lack the possibility for miniaturization and discretionary installation. In this study, potentiometric ionic polymer metal composite (IPMC) sensors for the determination of dissolved ozone in ultrapure water (UPW) systems are presented. Commercially available polymer electrolyte membranes are treated via an impregnation-reduction method to obtain nanostructured platinum layers. By applying 25 different synthesis conditions, layer thicknesses of 2.2 to 12.6 µm are obtained. Supporting radiographic analyses indicate that the platinum concentration of the impregnation solution has the highest influence on the obtained metal loading. The sensor response behavior is explained by a Langmuir pseudo-isotherm model and allows the quantification of dissolved ozone to trace levels of less than 10 µg L−1. Additional statistical evaluations show that the expected Pt loading and radiographic blackening levels can be predicted with high accuracy and significance (R2adj. > 0.90, p < 10−10) solely from given synthesis conditions.
Scratch assays enable the study of the migration process of an injured adherent cell layer in vitro. An apparatus for the reproducible performance of scratch assays and cell harvesting has been developed that meets the requirements for reproducibility in tests as well as easy handling. The entirely autoclavable setup is divided into a sample translation and a scratching system. The translational system is compatible with standard culture dishes and can be modified to adapt to different cell culture systems, while the scratching system can be adjusted according to angle, normal force, shape, and material to adapt to specific questions and demanding substrates. As a result, a fully functional prototype can be presented. This system enables the creation of reproducible and clear scratch edges with a low scratch border roughness within a monolayer of cells. Moreover, the apparatus allows the collection of the migrated cells after scratching for further molecular biological investigations without the need for a second processing step. For comparison, the mechanical properties of manually performed scratch assays are evaluated.
Power train models are required to simulate hence predict energy consumption of vehicles. Efficiencies for different components in power train are required. Common procedures use digitalised shell models (or maps) to model the efficiency of Internal Combustion Engines (ICE) and manual gearboxes (MG). Errors are connected with these models and affect the accuracy of the calculation. The accuracy depends on the configuration of the simulation, the digitalisation of the data and the data used. This paper evaluates these sources of error. The understanding of the source of error can improve the results of the modelling by more than eight percent.
Green IT is one of the most recent buzzwords in the IT business. Often it is considered as the key to all future IT problems by one half of IT experts and as an overestimated hype by the other half. Green IT aims to integrate information and communication technologies into a business wide sustainability strategy without any productivity drawbacks. It includes measures and tactics to reduce the IT's environmental impact e.g. by reducing the power consumption and using energy from renewable sources, and by using technologies such as virtualisation.
Evaluation is of crucial importance and should meet professional standards in its design. In practice, organizational peculiarities and available resources characterize the search for the "right" approach. When used as a quality development tool, internal or self-evaluation should primarily be useful. It should generate information to answer organizational questions and provide results as a basis for discussion in decision-making processes.
For research in audiovisual interview archives often it is not only of interest what is said but also how. Sentiment analysis and emotion recognition can help capture, categorize and make these different facets searchable. In particular, for oral history archives, such indexing technologies can be of great interest. These technologies can help understand the role of emotions in historical remembering. However, humans often perceive sentiments and emotions ambiguously and subjectively. Moreover, oral history interviews have multi-layered levels of complex, sometimes contradictory, sometimes very subtle facets of emotions. Therefore, the question arises of the chance machines and humans have capturing and assigning these into predefined categories. This paper investigates the ambiguity in human perception of emotions and sentiment in German oral history interviews and the impact on machine learning systems. Our experiments reveal substantial differences in human perception for different emotions. Furthermore, we report from ongoing machine learning experiments with different modalities. We show that the human perceptual ambiguity and other challenges, such as class imbalance and lack of training data, currently limit the opportunities of these technologies for oral history archives. Nonetheless, our work uncovers promising observations and possibilities for further research.