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A reinforcement of concrete structures is needed because of the low tensile strength of concrete. Corrosion of the usually used steel reinforcments cause issues during time. Therfore, alternative reinforcements produced from non-corosive materials - like polymer fibers - become more interesting. A polymer fiber has to reach high mechanical properties and a good bonding ability to concrete. Both properties can be influenced by the selection of polymer and the used production process. Two polymers, polypropylene and aliphatic polyketone are tested within this study. First mono-material fibers of each material are produced and tested, later the materials are combined in a core-shell fiber. All fibers are produced by standard extrusion or coextrusion and a later drawing process. The mechanical properties are determined by tensile tests. The calculation of interfacial shear strength from single fiber pull-out tests allows an evaluation of the bonding ability. Additionally, fiber surface before and after pull-out is examined using reflected light microscopy. Contact angle measurements are done to evaluate possible influences of the surface energy and polarity. Density measurements are used to compare weight potential of the different mono-material and core-shell fibers. The results show good mechanical properties for all fiber materials. The interfacial shear strength is ≈ 2-3 times higher for fibers with polyketone compared to the ones with polypropylene at the fibers surface, which can be explained by higher surface energy and polarity of the polyketone compared to polypropylene and different surface deformation during pull-out. Lower densities are reached by fibers containing polypropylene.
This research examines the potential outputs, outcomes, and impacts of the German Act on Corporate Due Diligence Obligations in Supply Chains (LkSG) on the smallholder cocoa farmers in West Africa. The study primarily relies on a literature review and an impact pathway to conduct a systematic analysis to identify the potential effects of the LkSG on smallholder cocoa farmers. The findings indicate that some, but not all of the risks addressed by the LkSG align with those faced by smallholder cocoa farmers and their families. Additionally, the research also reveals weaknesses, particularly in managing environmental risks, which the LkSG does not adequately cover. Our findings show that in the short- and medium-term, the LkSG has no potential effects on smallholder cocoa farmers. Furthermore, the potential positive impacts of the law on smallholder cocoa farmers will take a long time to realize, as the LkSG considers primarily tier-1 suppliers. Companies in Germany might reassess their supply chains to strive for an LkSG-risk-free supply chain, which could in the long term have sustained impacts on smallholder cocoa farmers. However, we recommend a comprehensive risk analysis of the cocoa supply chain to enhance the human rights of cocoa farmers.
Environmental pollution by plastics is a global issue of increasing concern. However, microplastic analysis in complex environmental matrices, such as soil samples, remains an analytical challenge. Destructive mass-based methods for microplastic analysis do not determine plastics’ shape and size, which are essential parameters for reliable ecological risk assessment. By contrast, nondestructive particle-based methods produce such data but require elaborate, time-consuming sample preparation. Thus, time-efficient and reliable methods for microplastic analysis are needed. The present study explored the potential of frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) for rapidly and reliably identifying as well as differentiating plastics and natural materials from terrestrial environments. We investigated the fluorescence spectra of ten natural materials from terrestrial environments, tire wear particles, and eleven different transparent plastic granulates <5 mm to determine the optimal excitation wavelength for identification and differentiation via FD-FLIM under laboratory conditions. Our comparison of different excitation wavelengths showed that 445 nm excitation exhibited the highest fluorescence intensities. 445 nm excitation was also superior for identifying plastic types and distinguishing them from natural materials from terrestrial environments with a high probability using FD-FLIM. We could demonstrate that FD-FLIM analysis has the potential to contribute to a streamlined and time-efficient direct analysis of microplastic contamination. However, further investigations on size-, shape-, color-, and material-type detection limitations are necessary to evaluate if the direct identification of terrestrial environmental samples of relatively low complexity, such as a surface inspection soil, is possible.
Microplastic pollution of the environment has been extensively studied, with recent studies focusing on the prevalence of microplastics in the environment and their effects on various organisms. Identification methods that simplify the extraction and analysis process to the point where the extraction can be omitted are being investigated, thus enabling the direct identification of microplastic particles. Currently, microplastic samples from environmental matrices can only be identified using time-consuming extraction, sample processing, and analytical methods. Various spectroscopic methods are currently employed, such as micro Fourier-transform infrared, attenuated total reflectance, and micro Raman spectroscopy. However, microplastics in environmental matrices cannot be directly identified using these spectroscopic methods. Investigations using frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) to identify and differentiate plastics from environmental materials have yielded promising results for directly identifying microplastics in an environmental matrix. Herein, two artificially prepared environmental matrices that included natural soil, grass, wood, and high-density polyethylene were investigated using FD-FLIM. Our first results showed that we successfully identified one plastic type in the two artificially prepared matrices using FD-FLIM. However, further research must be conducted to improve the FD-FLIM method and explore its limitations for directly identifying microplastics in environmental samples.
Previous research on identifying plastic types and differentiating plastics from environmental material is promising by utilizing the specific fluorescence lifetime, but the evaluation still has to be automated. Therefore, an automated Gaussian analysis is developed for evaluating frequency-domain fluorescence lifetime images of plastics and environmental materials. Furthermore, we applied a “Multilayer Perceptron” and “Random Forest Classifier” to the data resulting from the Gaussian analysis of the frequency domain fluorescence lifetime imaging microscopy data. The classification results show high F1-scores, whereby the best “Multilayer Perceptron” and “Random Forest Classifier” achieved an F1-score of 90%. Thus, identifying and differentiating plastics and environmental materials is possible by applying a “Multilayer Perceptron” or “Random Forest Classifier” to the Gaussian-analyzed imaged fluorescence lifetime data.
Environmental pollution by plastics is an increasing problem. However, state-of-the-art methods have significant disadvantages in detecting and identifying plastics directly in an environmental matrix. In this study, we propose a blob detection algorithm in combination with a neural network for fast and automated identification of plastics and non-plastics in a single fluorescence lifetime image. Therefore an artificial environmental matrix is prepared that contains soil, grass, spruce and HDPE (high density polyethylene) particles. Several FD-FLIM (frequency domain fluorescence lifetime imaging microscopy) images are taken, and the detection algorithm and the neural network are applied. We successfully demonstrated the suitability of the thresholding algorithm and the binary classification of the HDPE particles directly in the environmental matrix.
Existing plastic analysis techniques such as Fourier transform infrared spectroscopy and Raman spectroscopy are problematic because samples must be anhydrous and identification can be hindered by additives. This article describes a new approach that has been successfully demonstrated in which plastics can be classified by neural networks that are trained, validated, and tested by frequency domain fluorescence lifetime imaging microscopy measurements.
The FD-FLIM technique has a high potential for automated post-consumer wood sorting. A problem of analysing post-consumer wood fluorescence properties is the uncertainty of the post-consumer wood category as the origin of the samples are unknown. In this study, the fluorescence properties of actual post-consumer wood is compared with prepared wood samples. The post-consumer wood samples display slightly different fluorescence intensities and lifetimes due to environmental influences and a higher sample diversity. For improved training of evaluation algorithms for post-consumer wood sorting, the prepared sample set should be extended or the post-consumer wood should be additionally analysed in the laboratory.