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Paper des Monats
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Manufacturing and Application of Lightweight Aggregates from Construction and Demolition Waste
(2024)
The objective of the REALight project is the development of a thermal process to produce lightweight aggregates in pilot scale and the implantation of a method to recover gypsum. Beside construction and demolition waste, various industrial by‐products are studied as raw materials. The raw materials have so far been unused or used in applications with lower quality requirements. To prove the performance of the lightweight aggregates, their technical properties are tested and their use in different applications is studied, e.g., for lightweight mortars as well as lightweight concretes.
Nowadays geotechnical engineering firms have powerful software tools to extent their consult-ing business also into dynamic soil-structure interaction, which before has been restricted to a rather small community of specialized experts in this field, and they certainly do. This is par-ticularly true with respect to non-seismic sources, that is all kinds of human induced vibrations. Hence, there is a demand from clients as well as from contractors to have guidance on the re-quirements as well as the limits of numerical modelling of soil-structure interaction. From the literature as well as from relevant standards, recommendations for the numerical modelling of soil-structure interaction problems involving seismic actions are well known, e. g. ASCE/SEI 4-16. There are, however, some particularities when dealing with human-induced vibrations, which are absent in seismic analyses. For human-induced excitations very little specific guid-ance has been published in the past. A machine foundation on a homogeneous half space ex-cited by harmonic loads with excitation frequency between 4 Hz and 64 Hz has been ana-lysed by means of several commercially available software packages. Parametric studies have been performed to verify if recommendations for seismic soil-structure analyses are valid for non-seismic analyses as well. This paper provides details on the benchmark example and the most important conclusions from the undertaken parametric studies.
ISO 14837-32:2015 and DIN EN 1998-1/NA:2021 as well as prEN 1997-2:2022 allow for us-ing correlations between the results of in-situ soil penetration tests and shear wave velocity (or shear modulus) to determine soil properties to be used in dynamic analyses. While the ISO and prEN standards even provide some recommendations on specific correlations to be used, the DIN standard does not. Due to the statistical nature of such correlations their general applica-bility has to be verified. We collected data sets from test sites from Germany as well as New Zealand at which cone penetration tests (CPT) as well as seismic site investigation methods were conducted. These sites comprise sandy soils as well as clayey soils, mixed soils as well as glacial soils. We compare the results of several correlations between CPT results and shear wave velocity. The accuracy of such correlations is assessed with respect to the accuracy of seismic in-situ tests. It turns out that for clean sands such correlations between CPT and Vs have a similar order of variability as seismic in-situ tests conducted at the same site. The higher the fines portion of the soil, the higher the variability of the statistical correlations, and conse-quently the less the general applicability. For glacial soils and other special soil types usage of statistical correlations to determine dynamic soil properties is not recommended.
Addressing a strategy for publishing open and digital research data, this article presents the approach for streamlining and automating the process of storage and conversion of research data to those of semantically queryable data on the web. As the use case for demonstrating and evaluating the digitalization process, the primary datasets from low‐cycle‐fatigue testing of several copper alloys are prepared. The fatigue test ontology (FTO) and ckan.kupferdigital data management system are developed as two main prerequisites of the data digitalization process. FTO has been modeled according to the content of the fatigue testing standard and by reusing the basic formal ontology, industrial ontology foundry core ontology, and material science and engineering ontology. The ckan.kupferdigital data management system is also constructed in such a way that enables the users to prepare the protocols for mapping the datasets into the knowledge graph and automatically convert all the primary datasets to those machine‐readable data which are represented by the web ontology language. The retrievability of the converted digital data is also evaluated by querying the example competency questions, confirming that ckan.kupferdigital enables publishing open data that can be highly reused in the semantic web.
The systematic transfer of solvent-free, additive flame retardant (FR) formulations from epoxy resins to glass fiber-reinforced epoxy composites (GFRECs) through prepregs is difficult. Additionally, obtaining data on their post-fire mechanics is often challenging. Utilizing melamine polyphosphate (MPP), ammonium polyphosphate (APP), and silane-coated ammonium polyphosphate (SiAPP) FRs with low-melting inorganic silicates (InSi) in an 8:2 proportion and 10% loading by weight in a diglycidyl ether of bisphenol A (DGEBA) resin, a systematic investigation of the processing properties, room-temperature mechanics, and temperature-based mechanics of the systems was performed. The resin was cured with a dicyandiamide hardener (DICY) and a urone accelerator. The results revealed no substantial impact of these FRs at the current loading on the resin's glass transition temperature or processability. However, the fire residues from cone calorimetry tests of the composites containing FRs were found to be only 15-20% of the thickness of the resins, implying a suppression of intumescence upon transfer. At room temperature, the decrease in the flexural modulus for the composites containing FRs was negligible. Exposure of the composites in a furnace at 400°C as a preliminary study before ignition tests was shown to cause significant flexural moduli reductions after 2.5 min of exposure and complete delamination after 3 min making further testing unviable. This study emphasizes the need for future research on recovering modes of action upon transfer of FR formulations from resins to composites. Based on the challenges outlined in this investigation, sample adaptation methods for post-fire analysis will be developed in a future study.
This data article presents a set of primary, analyzed, and digitalized mechanical testing datasets for nine copper alloys. The mechanical testing methods including the Brinell and Vickers hardness, tensile, stress relaxation, and low-cycle fatigue (LCF) testing were performed according to the DIN/ISO standards. The obtained primary testing data (84 files) mainly contain the raw measured data along with the testing metadata of the processes, materials, and testing machines. Five secondary datasets were also provided for each testing method by collecting the main meta- and measurement data from the primary data and the outputs of data analyses. These datasets give materials scientists beneficial data for comparative material selection analyses by clarifying the wide range of mechanical properties of copper alloys, including Brinell and Vickers hardness, yield and tensile strengths, elongation, reduction of area, relaxed and residual stresses, and LCF fatigue life. Furthermore, both the primary and secondary datasets were digitalized by the approach introduced in the research article entitled “Toward a digital materials mechanical testing lab” [1]. The resulting open-linked data are the machine-processable semantic descriptions of data and their generation processes and can be easily queried by semantic searches to enable advanced data-driven materials research.
The platinum group elements (PGEs) play an important role in reducing emissions from automotive vehicles through their use in catalytic convertors but also for catalysis in the pharmaceutical industry. The immense economic value of platinum (Pt), palladium (Pd) and rhodium (Rh) highlights the importance of highly accurate measurements. Therefore, there is a need for National Metrology Institutes (NMIs) and Designated Institutes (DIs) to demonstrate measurement capability in this space.
A pilot comparison (CCQM-P63) for precious metals in automotive catalyst took place in 2006, but with a limited number of institutes participating. Furthermore, this study was performed over 17 years ago. Therefore, there was a need to maintain existing capability and demonstrate new capability in a key comparison, in order to claim calibration and measurement capability claims (CMCs). With the core capability matrix, this study falls into the "Difficult to dissolve metals/metal oxides" which will support CMC categories 8 (Metal and metal alloys), 9 (Advanced materials) and 14 (Other materials).
Eleven NMIs and DIs participated in the Key Comparison CCQM-K160 Platinum Group Elements in Automotive Catalyst. Participants were requested to evaluate the mass fractions of Pt, Pd and Rh in mg/kg in an unused autocatalyst material (cordierite ceramic base). The Key Comparison Reference Values (KCRVs) and Degrees of Equivalence (DoEs) were calculated utilising the NIST Decision Tree for the measurands. The participants utilised a number of sample preparation and analytical methods including hot plate digestion, microwave digestion and sodium fusion, followed by either atomic absorption spectroscopy (AAS), inductively coupled plasma optical emission spectroscopy (ICP-OES) or inductively coupled plasma mass spectrometry (ICP-MS) detection. Several calibration techniques were used, namely external calibration, standard addition, isotope dilution mass spectrometry (IDMS) and an exact matching procedure. Additionally, one participant employed instrumental neutron activation analysis (INAA) with k0 standardisation which is a direct solid analysis method. The majority of participants claimed traceability to NIST primary calibrants or their own CRMs. Furthermore, several matrix CRMs were included or spiked samples for quality control. All institutes were required to determine the dry mass fraction using the stipulated protocol.
The NIST decision tree was implemented for the calculation of the KCRVs and DoEs. The participant results overall showed good agreement with the KCRV, despite the variety of dissolution procedures and measurement techniques for this highly complex matrix and challenging measurands. Successful participation in CCQM-K160 demonstrated measurement capabilities for the determination of mass fraction of Pt, Pd and Rh in the mg/kg range and will support broad scope CMC claims for a wide range of challenging matrices.
To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.
The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
This research deals with the development of the Vickers hardness knowledge graph, mapping the example dataset in them, and exporting the data-mapped knowledge graph as a machine-readable Resource Description Framework (RDF). Modeling the knowledge graph according to the standardized test procedure and using the appropriate upper-level ontologies were taken into consideration to develop the highly standardized, incorporable, and industrial applicable models. Furthermore, the Ontopanel approach was utilized for mapping the real experimental data in the developed knowledge graphs and the resulting RDF files were successfully evaluated through the SPARQL queries.
AbstractThe correlation between altered extracellular pH and various pathological conditions, including cancer, inflammation and metabolic disorders, is well known. Bulk pH measurements cannot report the extracellular pH value at the cell surface. However, there is a limited number of suitable tools for measuring the extracellular pH of cells with high spatial resolution, and none of them are commonly used in laboratories around the world. In this study, a versatile ratiometric nanosensor for the measurement of extracellular pH was developed. The nanosensor consists of biocompatible polystyrene nanoparticles loaded with the pH-inert reference dye Nile red and is surface functionalized with a pH-responsive fluorescein dye. Equipped with a targeting moiety, the nanosensor can adhere to cell membranes, allowing direct measurement of extracellular pH at the cell surface. The nanosensor exhibits a sensitive ratiometric pH response within the range of 5.5–9.0, with a calculated pKa of 7.47. This range optimally covers the extracellular pH (pHe) of most healthy cells and cells in which the pHe is abnormal, such as cancer cells. In combination with the nanosensors ability to target cell membranes, its high robustness, reversibility and its biocompatibility, the pHe nanosensor proves to be well suited for in-situ measurement of extracellular pH, even over extended time periods. This pH nanosensor has the potential to advance biomedical research by improving our understanding of cellular microenvironments, where extracellular pH plays an important role.
Modern dry pressing of ceramic powders using spray‐dried granulates cannot avoid the occurrence of defects related to persisting inter‐ and intra‐granulate interstitial voids. These constitute the parent defect size population limiting the application of polycrystalline ceramics in high‐stress conditions. The mitigation of such defects could widen the range of application in technical and biomedical engineering, reduce the safety range for design, and extend the lifetime of components. Herein, the Weibull size‐effect on strength in size‐partitioned Yttria‐stabilized zirconias (YSZ) feedstocks is used to explore the viability of changing the density distribution of granulate sizes as an effective strategy to obtain a denser particle packing that could reduce the size distribution of strength‐limiting pressing defects. In a direct assessment of critical defect size using multiscale strength testing with a dataset of ≈1300 values, the success of such an approach in increasing the strength reliability for small volume components is demonstrated, along with its ultimate failure in altering the defect size distribution in sintered YSZ ceramics across several length scales. Finally, it is shown that granule morphology (spherical or dimpled) fails to affect the defect density and size distribution in YSZ ceramics.
A widespread recovery of waste heat requires a cost‐effective production of thermoelectric generators. Thermoelectric oxides are predestined for use at high temperatures. For manufacturing reasons, a multilayer generator design will be easily scalable and cost‐effective. To evaluate the potential of ceramic multilayer technology for that purpose, a multilayer of the promising thermoelectric oxides calcium cobaltite (Ca3Co4O9), calcium manganate (CMO, CaMnO3), and glass–ceramic insulation layers is fabricated. Cracks and reaction layers at the interfaces are observed in the microstructure. The compositions of these reaction layers are identified by energy‐dispersive X‐ray spectroscopy and X‐ray diffraction. Mechanical and thermal properties of all layers are compiled from literature or determined by purposeful sample preparation and testing. Based on this data set, the internal stresses in the multilayer after co‐firing are calculated numerically. It is shown that tensile stresses in the range of 50 MPa occur in the CMO layers. The reaction layers have only a minor influence on the level of these residual stresses. Herein, it is proven that the material system is basically suitable for multilayer generator production, but that the co‐firing process and the layer structure must be adapted to improve densification and reduce the tensile stresses in the CMO.
Quantum dots (QDs) are remarkable semiconductor nanoparticles, whose optical properties are strongly size-dependent. Therefore, the real-time monitoring of crystal growth pathway during synthesis gives an excellent opportunity to a smart design of the QDs luminescence. In this work, we present a new approach for monitoring the formation of QDs in aqueous solution up to 90 °C, through in situ luminescence analysis, using CdTe as a model system. This technique allows a detailed examination of the evolution of their light emission. In contrast to in situ absorbance analysis, the in situ luminescence measurements in reflection geometry are particularly advantageous once they are not hindered by the concentration increase of the colloidal suspension. The synthesized particles were additionally characterized using X-ray diffraction analysis, transition electron microscopy, UV-Vis absorption and infrared spectroscopy. The infrared spectra showed that 3-mercaptopropionic acid (MPA)-based thiols are covalently bound on the surface of QDs and microscopy revealed the formation of CdS. Setting a total of 3 h of reaction time, for instance, the QDs synthesized at 70, 80 and 90 °C exhibit emission maxima centered at 550, 600 and 655 nm. The in situ monitoring approach opens doors for a more precise achievement of the desired emission wavelength of QDs.
The glass system (50NaPO3–20BaF2–10CaF2–20GdF3)-xTbCl3 with x = 0.3, 1, 3, 5, and 10 wt % was investigated. We successfully produced transparent glass ceramic (GC) scintillators with x = 1 through a melt-quenching process followed by thermal treatment. The luminescence and crystallization characteristics of these materials were thoroughly examined using various analytical methods. The nanocrystallization of Tb3+-doped Na5Gd9F32 within the doped fluoride-phosphate glasses resulted in enhanced photoluminescence (PL) and radioluminescence (RL) of the Tb3+ ions. The GC exhibited an internal PL quantum yield of 33 % and the integrated RL intensity across the UV-visible range was 36 % of that reported for the commercial BGO powder scintillator. This research showcases that Tb-doped fluoridephosphate GCs containing nanocrystalline Na5Gd9F32 have the potential to serve as efficient scintillators while having lower melting temperature compared to traditional silicate and germanate glasses.
Upconverting nanoparticles are essential in modern photonics due to their ability to convert infrared light to visible light. Despite their significance, they exhibit limited brightness, a key drawback that can be addressed by combining them with plasmonic nanoparticles. Plasmon-enhanced upconversion has been widely demonstrated in dry environments, where upconverting nanoparticles are immobilized, but constitutes a challenge in liquid media where Brownian motion competes against immobilization.
This study employs optical tweezers for the three-dimensional manipulation of an individual upconverting nanoparticle, enabling the exploration of plasmon-enhanced upconversion luminescence in water. Contrary to expectation, experiments reveal a long-range (micrometer scale) and moderate (20%) enhancement in upconversion luminescence due to the plasmonic resonances of gold nanostructures. Comparison between experiments and numerical simulations evidences the key role of Brownian motion. It is demonstrated how the three-dimensional Brownian fluctuations of the upconverting nanoparticle lead to an “average effect” that explains the magnitude and spatial extension of luminescence enhancement.
The Great Pacific Garbage Patch, a significant collection of plastic introduced by human activities, provides an ideal environment to study bacterial lifestyles on plastic substrates. We proposed that bacteria colonizing the floating plastic debris would develop strategies to deal with the ultravioletexposed substrate, such as the production of antioxidant pigments. We observed a variety of pigmentation in 67 strains that were directly cultivated from plastic pieces sampled from the Garbage Patch. The genomic analysis of four representative strains, each distinct in taxonomy, revealed multiple pathways for carotenoid production. These pathways include those that produce
less common carotenoids and a cluster of photosynthetic genes. This
cluster appears to originate from a potentially new species of the Rhodobacteraceae family. This represents the first report of an aerobic anoxygenic photoheterotrophic bacterium from plastic biofilms. Spectral analysis showed that the bacteria actively produce carotenoids, such as betacarotene and beta-cryptoxanthin, and bacteriochlorophyll a. Furthermore, we discovered that the genetic ability to synthesize carotenoids is more common in plastic biofilms than in the surrounding water communities. Our findings suggest that plastic biofilms could be an overlooked source of bacteria-produced carotenoids, including rare forms. It also suggests that photoreactive molecules might play a crucial role in bacterial biofilm communities in surface water.
This research was aimed at comparing the fire characteristics of different types of pepper in the context of explosion prevention. The following characteristics were studied: explosion pressure Pmax and Kst at selected concentrations, ignition temperature of the deposited dust layer from the hot surface, and minimum ignition energy. The comparison of the chemical properties of the used types of pepper was performed using TG/DSC. The results of the measurements suggest that different types of peppers exhibit different explosion characteristics. Each sample reached the maximum value of the explosion pressure and rate of pressure rise at different concentrations. The volume of the explosion chamber used also influenced the explosion characteristics. It is a consequence of the fact that the explosion characteristics strongly depend on the mechanism of action of a particular igniter. The minimum effect on the safety characteristics was observed when measuring the minimum ignition energy and the minimum ignition temperature of the dust layer from the hot surface. The results of the measurements suggest that different types of peppers exhibit different explosion characteristics. This information should then be considered in explosion prevention.
AbstractThis research aims to detect and analyze critical internal and surface defects in metal components manufactured by powder bed fusion of metals using a laser beam (PBF‐LB/M). The aim is to assess their impact on the fatigue behavior. Therefore, a combination of methods, including image processing of micro‐computed tomography (CT) scans, fatigue testing, and machine learning, was applied. A workflow was established to contribute to the nondestructive assessment of component quality and mechanical properties. Additionally, this study illustrates the application of machine learning to address a classification problem, specifically the categorization of pores into gas pores and lack of fusion pores. Although it was shown that internal defects exhibited a reduced impact on fatigue behavior compared with surface defects, it was noted that surface defects exert a higher influence on fatigue behavior. A machine learning algorithm was developed to predict the fatigue life using surface defect features as input parameters.
The synergistic behavior of different silicon compounds is investigated in flame retardant biocomposites with aluminum trihydroxide (ATH) as the main flame retardant. The paper shows a new approach towards sustainable biocomposites through the implementation of thermoplastic starch (TPS), leather fibers from industrial waste streams, and non-hazardous flame retardants and synergists. In these multicomponent systems, the different components address different modes of action in the fire scenario. When ATH is partially substituted by glass frits or layered silicates, fire performance is enhanced without changing the total amount of filler in the polymer. In a biocomposite with 25 phr of fiber and 90 phr of ATH, substituting 5 phr of ATH for layered silicates increased the LOI from 31.5 vol % to 34.8 vol %, decreased the peak of heat release by 20%, and increased the UL 94 rating from V-1 to V-0.
Soybean oil takes around half of the vegetable oil resources in the world, increasing in importance constantly. Besides, soy oil plants have experienced numerous accidents due to the coexistence of soy flour and hexane (as a solvent) in the extraction process, thus creating a hazardous environment. This study aims to find the maximum pressure, the maximum rate of pressure rise, and the minimum ignition energy of soy flour−hexane mixtures through specific experiments by varying the concentration of fuels in air and ignition mechanism (chemical igniters or exploding wires). The results have shown that soy flour alone is hard to ignite, whereas adding hexane even in small amounts increases the hazard and the severity of the explosions considerably. Eventually, the substitution of hexane with a greener and safer extraction agent should be of utmost focus.
Machine learning-assisted equivalent circuit identification for dielectric spectroscopy of polymers
(2024)
Polymers have become indispensable across fields of application, and understanding their structure–property relationships and dynamic behaviour is essential for performance optimization. Polymer membranes, particularly ion exchange membranes, play a crucial role in renewable energy conversion technologies, fuel cells, solar energy conversion, and energy storage. In this context, broadband dielectric spectroscopy (BDS) offers a powerful, non-destructive approach to investigate the electrical response and relaxation dynamics of polymers. These properties are investigated by parametrizing the system’s impedance response in terms of a network of circuit elements, i.e. the electrical equivalent circuit (EEC), whose impedance resembles the one of the system under investigation. However, the determination of the EEC from BDS data is challenging due to system complexity, interdependencies of circuit elements, and researcher biases. In this work, we propose a novel approach that incorporates a convolutional neural network (CNN) model to predict the EEC topology. By reducing user bias and enhancing data analysis, this approach aims to make BDS accessible to both experienced users and those with limited expertise. We show that the combination of machine learning and BDS provides valuable insights into the dynamic behaviour of polymer membranes, thus facilitating the design and characterization of tailored polymers for various applications. We also show that our model outperforms state-of-the-art machine learning methods with a top-5 accuracy of around 80% for predicting the circuit topology and a parameter fitting error as low as 0.05%.