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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.
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.