570 Biowissenschaften, Biologie
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Due to its availability and minimal invasive harvesting human adipose tissue-derived extracellular matrix (dECM) is often used as a biomaterial in various tissue engineering and healthcare applications. Next to dECM, cell-derived ECM (cdECM) can be generated by and isolated from in vitro cultured cells. So far both types of ECM were investigated extensively toward their application as (bio)material in tissue engineering and healthcare. However, a systematic characterization and comparison of soft tissue dECM and cdECM is still missing. In this study, we characterized dECM from human adipose tissue, as well as cdECM from human adipose-derived stem cells, toward their molecular composition, structural characteristics, and biological purity. The dECM was found to exhibit higher levels of collagens and lower levels of sulfated glycosaminoglycans compared with cdECMs. Structural characteristics revealed an immature state of the fibrous part of cdECM samples. By the identified differences, we aim to support researchers in the selection of a suitable ECM-based biomaterial for their specific application and the interpretation of obtained results.
The detection and characterisation of oxide layers on metallic copper samples plays an important role for power electronic modules in the automotive industry. However, since precise identification of oxide layers by visual inspection is difficult and time consuming due to inhomogeneous colour distribution, a reliable and efficient method for estimating their thickness is needed. In this study, hyperspectral imaging in the visible wavelength range (425–725 nm) is proposed as an in-line inspection method for analysing oxide layers in real-time during processing of copper components such as printed circuit boards in the automotive industry. For implementation in the production line a partial least square regression (PLSR) model was developed with a calibration set of n = 12 with about 13,000 spectra per sample to determine the oxide layer thickness on top of the technical copper surfaces. The model shows a good prediction performance in the range of 0–30 nm compared to Auger electron spectroscopy depth profiles as a reference method. The root mean square error (RMSE) is 1.75 nm for calibration and 2.70 nm for full cross validation. Applied to an external dataset of four new samples with about 13,000 spectra per sample the model provides an RMSE of 1.84 nm for prediction and demonstrates the robustness of the model during real-time processing. The results of this study prove the ability and usefulness of the proposed method to estimate the thickness of oxide layers on technical copper. Hence, the application of hyperspectral imaging for the industrial process control of electronic devices is very promising.