Analytische Chemie
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The rational design of next generation molecular and nanoscale reporters and the comparison of different emitter classes require the determination of the fluorometric key performance parameter fluorescence quantum yield (Φf), i.e., the number of emitted photons per number of absorbed photons. Main prerequisites for reliable Φf measurements, which are for transparent luminophore solutions commonly done relative to a reference, i.e., a fluorescence quantum yield standard of known Φf, are reliable and validated instrument calibration procedures to consider wavelength-, polarization-, and time-dependent instrument specific signal contributions, and sufficiently well characterized fluorescence quantum yield standards. As the standard’s Φf value directly contributes to the calculation of the sample’s Φf, its accuracy presents one of the main sources of uncertainty of relative Φf measurements. To close this gap, we developed a first set of 12 fluorescence quantum yield standards, which absorb and emit in the wavelength region of 330−1000 nm and absolutely determined their Φf values with two independently calibrated integrating sphere setups.
Criteria for standard selection and the configuration of these novel fluorescence reference materials are given, and the certification procedure is presented including homogeneity and stability studies and the calculation of complete uncertainty budgets for the certified Φf values. The ultimate goal is to provide the community of fluorescence users with available reference materials as a basis for an improved comparability and reliability of quantum yield data since the measurement of this spectroscopic key property is an essential part of the characterization of any new emitter.
Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.