Recycling potential of carbon fibres in the construction industry: From a technical and ecological perspective.

Even though carbon fibres (CFs) have been increasingly used, their end-of-life (EOL) handling presents a challenge. To address this issue, we evaluated the use of recycled CFs (rCFs), produced through pyrolysis, as rovings to be used in textile reinforced concrete structures. Mechanical processing (hammer mill) with varying machine settings was then used to assess EOL handling, considering the separation potential of rCFs and the length of separated rCFs. The results showed that rCF rovings can be separated from concrete with an average of 87 wt.-%, whereas the highest rCF length and separation yield were observed in different machine settings. In addition, a techno-environmental assessment on the mechanical process was performed to compare different machine settings. The machine settings with the highest yield of rCF rovings also had the highest fine fraction that cannot be further separated. Furthermore, life cycle assessment (LCA) was conducted covering three life cycles of CFs and an additional LCA for comparing rCF with virgin CF. LCA results revealed that CF reinforced plastic and concrete productions are the two main contributors to environmental impacts. The comparative LCA between virgin CF and rCF also showed that using rCF is environmentally advantageous, as virgin CF production causes 230% more global warming potential compared to rCF. Future studies assessing different allocation approaches, quantifying the quality of rCF, and its inclusion in LCA are relevant.

NIR-MFCO dataset: Near-infrared-based false-color images of post-consumer plastics at different material flow compositions and material flow presentations.

Determining mass-based material flow compositions (MFCOs) is crucial for assessing and optimizing the recycling of post-consumer plastics. Currently, MFCOs in plastic recycling are primarily determined through manual sorting analysis, but the use of inline near-infrared (NIR) sensors holds potential to automate the characterization process, paving the way for novel sensor-based material flow characterization (SBMC) applications. This data article aims to expedite SBMC research by providing NIR-based false-color images of plastic material flows with their corresponding MFCOs. The false-color images were created through the pixel-based classification of binary material mixtures using a hyperspectral imaging camera (EVK HELIOS NIR G2-320; 990 nm-1678 nm wavelength range) and the on-chip classification algorithm (CLASS 32). The resulting NIR-MFCO dataset includes n = 880 false-color images from three test series: (T1) high-density polyethylene (HDPE) and polyethylene terephthalate (PET) flakes, (T2a) post-consumer HDPE packaging and PET bottles, and (T2b) post-consumer HDPE packaging and beverage cartons for n = 11 different HDPE shares (0% - 50%) at four different material flow presentations (singled, monolayer, bulk height H1, bulk height H2). The dataset can be used, e.g., to train machine learning algorithms, evaluate the accuracy of inline SBMC applications, and deepen the understanding of segregation effects of anthropogenic material flows, thus further advancing SBMC research and enhancing post-consumer plastic recycling.

Optical sensors and machine learning algorithms in sensor-based material flow characterization for mechanical recycling processes: A systematic literature review.

Digital technologies hold enormous potential for improving the performance of future-generation sorting and processing plants; however, this potential remains largely untapped. Improved sensor-based material flow characterization (SBMC) methods could enable new sensor applications such as adaptive plant control, improved sensor-based sorting (SBS), and more far-reaching data utilizations along the value chain. This review aims to expedite research on SBMC by (i) providing a comprehensive overview of existing SBMC publications, (ii) summarizing existing SBMC methods, and (iii) identifying future research potentials in SBMC. By conducting a systematic literature search covering the period 2000 - 2021, we identified 198 peer-reviewed journal articles on SBMC applications based on optical sensors and machine learning algorithms for dry-mechanical recycling of non-hazardous waste. The review shows that SBMC has received increasing attention in recent years, with more than half of the reviewed publications published between 2019 and 2021. While applications were initially focused solely on SBS, the last decade has seen a trend toward new applications, including sensor-based material flow monitoring, quality control, and process monitoring/control. However, SBMC at the material flow and process level remains largely unexplored, and significant potential exists in upscaling investigations from laboratory to plant scale. Future research will benefit from a broader application of deep learning methods, increased use of low-cost sensors and new sensor technologies, and the use of data streams from existing SBS equipment. These advancements could significantly improve the performance of future-generation sorting and processing plants, keep more materials in closed loops, and help paving the way towards circular economy.

Recycling of polystyrene-based external thermal insulation composite systems - Application of combined mechanical and chemical recycling.

The material recycling of complex waste streams such as external thermal insulation composite systems (ETICS) is challenging, which is why their recycling in the sense of a circular economy is currently hardly established. Therefore, the combined mechanical and thermochemical recycling of ETICS based on expanded polystyrene (EPS) is investigated experimentally and by simulating full process chains in order to evaluate circular economy opportunities. Model ETICS as example for building and construction waste is pretreated mechanically, followed by either pyrolysis and / or gasification steps, and full mass and energy balances are derived. By the combined recycling, inorganic compounds can be separated to a large extent allowing a pre-concentrate generation. The plastic-rich pre-concentrate is converted into either pyrolysis oil with a high styrene monomer content of 51 wt% or to synthesis gas in the subsequent thermochemical conversions. The holistic approach enables a high carbon recycling rate between 53 and 68 wt%. In addition, the investigation reveals technology limitations and opportunities to be further developed and optimized.

Influence of long-term natural degradation processes on near-infrared spectra and sorting of post-consumer plastics.

The large-amount production and application of plastics since the 1950s has led to different environmental problems, and the production amount is still increasing. In 2015, 79 wt% of all plastic waste was accumulated in landfills or the natural environment. Due to their negative influence to the environment, the problems of landfilling and marine litter need urgent treatments. Accordingly, measures like excavation of landfill sites and ocean clean-ups were conducted to reduce their environmental influences and move further towards a closed loop of material cycles. For a possible recycling, the valuable material fractions need to be separated from other materials. Besides, to ensure a high-quality recycling and enable the different recycling processes of plastics in different degradation levels, it is necessary to separate degraded and non-degraded plastics. In this study, the possibility to classify and sort landfill and marine litter plastics is investigated. For this purpose, waste plastics from different origins (lightweight packaging (LWP) waste, landfill, and marine litter) were collected and analyzed with the state-of-the-art technology in sorting plants: near-infrared spectroscopy. With self-developed programs, the classification possibility and performance was determined. The classification accuracy of degraded plastics (from landfill and marine litter) is improved from > 75% to > 97% through adjusting the sorting recipe. Besides, the long-term degraded plastics under natural environment were able to be separated from LWP waste: the same kind of materials can be classified according to their origin (LWP or after long-term degradation), which makes a quality control possible and enables an extra treatment for degraded plastics.

Sensor-based particle mass prediction of lightweight packaging waste using machine learning algorithms.

Sensor-based material flow characterization (SBMC) promises to improve the performance of future-generation sorting plants by enabling new applications like automatic quality monitoring or process control. Prerequisite for this is the derivation of mass-based material flow characteristics from pixel-based sensor data, which requires known individual particle masses. Since particle masses cannot be measured inline, the prediction of particle masses of lightweight packaging (LWP) waste using machine learning (ML) algorithms is investigated. Five LWP material classes were sampled, preprocessed, and scanned on a custom-made test rig, resulting in a dataset containing 3D laser triangulation (3DLT) images, RGB images, and corresponding masses of n = 3,830 particles. Based on 66 extracted shape measurements, six ML models were trained for particle mass prediction (PMP). Their performance was compared with two state-of-the-art reference models using (i) material-specific mean particle masses and (ii) grammages. Obtained particle masses showed a high variation and significant differences between material classes and particle size classes. After feature selection, both reference models achieving R2-scores of (i) 0.422 ± 0.121 and (ii) 0.533 ± 0.224 were outperformed by all investigated ML models. A random forest regressor with an R2-score of 0.763 ± 0.091 and a normalized mean absolute error of 0.243 ± 0.050 achieved the most accurate PMP. In contrast to studies on primary raw materials, PMP of LWP waste is challenging due to influences of packaging design and post-consumer disposal behavior. ML algorithms are a promising approach for PMP that outperform state-of-the-art methods by 43% higher R2-scores.

Biochemical characterization of the recombinant human Nogo-A ectodomain.

Nogo-A is a physiologically relevant inhibitor of neuronal growth and regeneration in the myelin of the adult human central nervous system and has attracted considerable attention as a molecular target for the treatment of spinal cord injuries. To gain insight into the structural and functional properties of the large extramembrane region that is characteristic for the Nogo-A splice form of this member of the Reticulon family of membrane proteins, we cloned and expressed the region comprising residues 334-966 as a soluble homogeneous protein in the periplasm of Escherichia coli. SDS/PAGE, under nonreducing conditions, and a systematic substitution analysis of all six Cys residues of Nogo-A indicated that this domain forms two structural disulfide bonds among Cys residues 424, 464, 559 and 597, whereas the Cys residues at positions 699 and 912 seem to be dispensable for folding. The occurrence of a hot spot for host cell proteases and a limited proteolysis experiment suggest that the N-terminal region of Nogo-A up to residue 373 is structurally disordered. Although analytical gel permeation chromatography revealed a large apparent molecular size for the recombinant Nogo-A fragment, indicating oligomer formation, data from analytical ultracentrifugation and dynamic light scattering support a stable monomeric quaternary structure. Notably, the CD spectrum is indicative of a high content of random coil, such that Nogo-A exhibits--at least in part--features of a natively unfolded protein. Nevertheless, the protein fragment identified in this study, as well as its biochemical analysis, provide a promising starting point for future investigations to track down globular subdomains and functionally important regions as well as putative receptor-binding sites therein.