TY - CONF A1 - Charmi, Amir A1 - Heimann, Jan A1 - Duffner, Eric A1 - Hashemi, Seyedreza A1 - Prager, Jens T1 - Application of deep learning for structural health monitoring of a composite overwrapped pressure vessel undergoing cyclic loading N2 - Structural health monitoring (SHM) using ultrasonic-guided waves (UGWs) enables continuous monitoring of components with complex geometries and provides extensive information about their structural integrity and their overall condition. Composite overwrapped pressure vessels (COPVs) used for storing hydrogen gases at very high pressures are an example of a critical infrastructure that could benefit significantly from SHM. This can be used to increase the periodic inspection intervals, ensure safe operating conditions by early detection of anomalies, and ultimately estimate the remaining lifetime of COPVs. Therefore, in the digital quality infrastructure initiative (QI-Digital) in Germany, an SHM system is being developed for COPVs used in a hydrogen refueling station. In this study, the results of a lifetime fatigue test on a Type IV COPV subjected to many thousands of load cycles under different temperatures and pressures are presented to demonstrate the strengths and challenges associated with such an SHM system. During the cyclic testing up to the final material failure of the COPV, a sensor network of fifteen surface-mounted piezoelectric (PZT) wafers was used to collect the UGW data. However, the pressure variations, the aging process of the COPV, the environmental parameters, and possible damages simultaneously have an impact on the recorded signals. This issue and the lack of labeled data make signal processing and analysis even more demanding. Thus, in this study, semi-supervised, and unsupervised deep learning approaches are utilized to separate the influence of different variables on the UGW data with the final aim of detecting and localizing the damage before critical failure. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Anomaly detection KW - Damage localization KW - Deep learning KW - Structural health monitoring KW - Ultrasonic-guided waves PY - 2024 AN - OPUS4-60745 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wilke, Olaf T1 - PTR-MS as a tool for the determination of formaldehyde, ammonia and volatile organic sulfur compounds together with VOC N2 - The use of proton-transfer-reaction mass spectrometry (PTR-MS) for the determination of formaldehyde, ammonia and volatile sulfur organic compounds is described and suggestions are made for the application of stationary PTR-MS in a test laboratory. One advantage of PTR-MS compared to TD-GC-MS (thermal desorption gas chromatography mass spectrome-try) is the faster online measurement. Another advantage is the simultaneous determination of formaldehyde, ammonia, very volatile organic compounds (VVOC) and volatile organic com-pounds (VOC) which normally needs four different kinds of sampling. Coupling the PTR-MS with (multiple) air sample canisters or (multiple) emission test chambers would enable a quick analysis of indoor air and material emissions. T2 - Indoor Air Conference 2024 CY - Honolulu, HI, USA DA - 07.07.2024 KW - Proton-transfer-reaction mass spectrometry PY - 2024 AN - OPUS4-60746 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Resch-Genger, Ute A1 - Tavernaro, Isabella A1 - Abram, Sarah-Luise A1 - Andresen, Elina A1 - Matiushkina, Anna T1 - Quantifying the number of total and accessible functional groups on nanomaterials N2 - Inorganic and organic functional nanomaterials (NM) of different size, shape, chemical composition, and surface chemistry are relevant for many key technologies of the 21st century. Decisive for most applications of NM are their specific surface properties, which are largely controlled by the chemical nature and number of ligands and functional groups (FG on the NM surface. The surface chemistry can strongly affect the physicochemical properties of NM, their charge, hydrophilicity/hydrophobicity, reactivity, stability, and processability and thereby their impact on the environment and biological species as well as their possible risk for human health. Thus, reliable, validated, and eventually standardized analytical methods for the characterization of NM surface chemistry, i.e., the chemical identification, quantification, and accessibility of FG and surface ligands 1,2] flanked by interlaboratory comparisons, control samples, and reference materials, 2 ,3 are of considerable importance for process and quality control of NM production and function. This is also important for the safe use of NM the design of novel NM, and sustainable concepts for NM fabrication. Here, we provide an overview of analytical methods for FG analysis and quantification and highlight method and material related challenges for selected NM. Analytical techniques address ed include electrochemical titration methods, optical assays, nuclear magnetic resonance (NMR) and vibrational (IR) spectroscopy, and X ray based and thermal analysis methods. Criteria for method classification and evaluation include the need for a signal generating label, provision of either the total or derivatizable number of FG, and suitability for process and production control. T2 - AUC - Analytical Ultracentrifugation CY - Nuremberg, Germany DA - 22.07.2024 KW - Nanoparticle KW - Particle KW - Microparticle KW - Silica KW - Quantum dot KW - Polymer KW - Surface group KW - Luminescence KW - Quality assurance KW - Synthesis KW - Surface modification KW - ILC KW - Optical assay KW - Functional group KW - Ligand KW - qNMR KW - Conductometry KW - Potentiometry KW - Standardization KW - Reference product KW - Reference material PY - 2024 AN - OPUS4-60749 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -