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Paper des Monats
- ja (68)
Protein modifications such as oligomerization and tyrosine nitration alter the immune response to allergens and may contribute to the increasing prevalence of allergic diseases. In this mini-review, we summarize and discuss relevant findings for the major birch and grass pollen allergens Bet v 1 and Phl p 5 modified with tetranitromethane (laboratory studies), peroxynitrite (physiological processes), and ozone and nitrogen dioxide environmental conditions). We focus on tyrosine nitration and the formation of protein dimers and higher oligomers via dityrosine cross-linking and the immunological effects studied.
The digitalization of quality control processes and the underlying data infrastructures for safety relevant components, such as hydrogen pressure vessels, plays a significant role in the transition towards Industry 4.0. In the current safety regulations for hydrogen pressure vessels, there is no established concept for structural health monitoring. The development of a reliable structural health monitoring methodology for monitoring the structural integrity of pressure vessels enables a fast-forward transition from personnel- and costintensive recurring inspections, a.k.a. periodic maintenance, to predictive maintenance. In the work presented; we investigated the application of ultrasonic guided wave propagation to monitor and assess the condition of Type IV composite overwrapped pressure vessel (COPV). A sensor network of fifteen piezo-electric wafers is placed on the carbon fibre reinforced composite cylinder. Five different artificial damage configurations are created by gluing two different weight blocks on three different locations. The database containing measured guided wave data sets is enriched by two different boundary conditions. We utilized an open-source software, openBIS labnotebook, to store and analyse experimental datasets. The guided wave ultrasonic signals were investigated and analysed by using commonly used ultrasonic features (e.g., amplitude, frequency, time of flight) as well as non-traditional time-series features (kurtosis, skewness, variance). The features were used to calculate damage index and the detection performance for the results has been evaluated. The results suggest that both traditional and non-traditional features assume significant importance in artificial damage detection. The future works will additionally involve the impacts of operational conditions, such as periodic pressure variations temperature loadings as well as material degradations.
The application of composite overwrapped pressure vessels (COPV) to store hydrogen and other compressed gases, especially when operating at high pressures, imposed the need for an innovative and reliable approach to ensure the safe operation of the system. Continuous structural health monitoring (SHM) based on ultrasonic guided waves (GWs) is a promising approach due to the ability of the wave to propagate for long distances and go around complex structures, moreover the high sensitivity to various failure modes such as delamination, matrix cracking and debonding. In this study, we scrutinize the behavior of (GWs) within the COPV using a network of PZT elements that are used for excitation and sensing. A laser doppler vibrometer (LDV) was also used to scan the surface of the vessel in various directions. L(0,1) and L(0,2) were observed in the captured signal. The L(0, 2) appeared to be a dominant mode in the COPV and was capable of propagating along the entire length and maintaining a good signal-to-noise ratio. The L(0,2) mode maintained the same phase velocity when it is captured at various excitation angles (0, 45, and 90 degrees). The reduced effect of the orthotropy of the materials on the propagating waves is an important result as it will reduce the complexity in data processing when performing damage identification.
(1) Background: The desire to avoid autograft harvesting in implant dentistry has prompted an ever-increasing quest for bioceramic bone substitutes, which stimulate osteogenesis while resorbing in a timely fashion. Consequently, a highly bioactive silicon containing calcium alkali orthophosphate (Si-CAP) material was created, which previously was shown to induce greater bone cell maturation and bone neo-formation than β-tricalcium phosphate (β-TCP) in vivo as well as in vitro. Our study tested the hypothesis that the enhanced effect on bone cell function in vitro and in sheep in vivo would lead to more copious bone neoformation in patients following sinus floor augmentation (SFA) employing Si-CAP when compared to β-TCP. (2) Methods: The effects of Si-CAP on osteogenesis and Si-CAP resorbability were evaluated in biopsies harvested from 38 patients six months after SFA in comparison to β-TCP employing undecalcified histology, histomorphometry, and immunohistochemical analysis of osteogenic marker expression. (3) Results: Si-CAP as well as β-TCP supported matrix mineralization and bone formation. Apically furthest away from the original bone tissue, Si-CAP induced significantly higher bone formation, bone-bonding (bone-bioceramic contact), and granule resorption than β-TCP. This was in conjunction with a higher expression of osteogenic markers. (4) Conclusions: Si-CAP induced higher and more advanced bone formation and resorbability than β-TCP, while β-TCP’s remarkable osteoconductivity has been widely demonstrated. Hence, Si-CAP constitutes a well-suited bioactive graft choice for SFA in the clinical arena.
It is shown that preconditioning of experimental X‐ray computed tomography (XCT) data is critical to achieve high‐precision segmentation scores. The challenging experimental XCT datasets and deep convolutional neural networks (DCNNs) are used that are trained with low‐resemblance synthetic XCT data. The material used is a 6‐phase Al–Si metal matrix composite‐reinforced with ceramic fibers and particles. To achieve generalization, in our past studies, specific data augmentation techniques were proposed for the synthetic XCT training data. In addition, two toolsets are devised: (1) special 3D DCNN architecture (3D Triple_UNet), slicing the experimental XCT data from multiple views (MultiView Forwarding), the i.S.Sy.Da.T.A. iterative segmentation algorithm, and (2) nonlocal means (NLM) conditioning (filtering) for the experimental XCT data. This results in good segmentation Dice scores across all phases compared to more standard approaches (i.e., standard UNet architecture, single view slicing, standard single training, and NLM conditioning). Herein, the NLM filter is replaced with the deep conditioning framework BAM SynthCOND introduced in a previous publication, which can be trained with synthetic XCT data. This leads to a significant segmentation precision increase for all phases. The proposed methods are potentially applicable to other materials and imaging techniques.
The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy.
We report the selective ring opening copolymerisation (ROCOP) of
oxetane and phthalic thioanhydride by a heterobimetallic Cr(III)K
catalyst precisely yielding semi-crystalline alternating poly(esteralt-
thioesters) which show improved degradability due to the
thioester links in the polymer backbone.
LIBS‐ConSort: Development of a sensor‐based sorting method for construction and demolition waste
(2023)
AbstractA joint project of partners from industry and research institutions approaches the challenge of construction and demolition waste (CDW) sorting by investigating and testing the combination of laser‐induced breakdown spectroscopy (LIBS) with near‐infrared (NIR) spectroscopy and visual imaging. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3‐cotaining building materials (gypsum, aerated concrete, etc.)Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic / technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates.The objective of this paper is to present current progress and results of the test stand development combining LIBS with NIR spectroscopy and visual imaging. In the future, this laboratory prototype will serve as a fully automated measurement setup to allow real‐time classification of CDW on a conveyor belt.
Im Rahmen des BMBF-geförderten Forschungsprojekts ReMin – REALight werden in einem Projektteam aus Industriepartnern und Forschungseinrichtungen unter Koordination der Bauhaus-Universität Weimar leichte Gesteinskörnungen aus Bau- und Abbruchabfällen und industriellen Nebenprodukten auf thermischem Weg hergestellt. Die Aufgabe der Bundesanstalt für Materialforschung und -prüfung (BAM) ist es, diese im Drehrohrofen erzeugten Blähgranulate für gebundene Anwendungen in Beton und Mörtel umfassend zu charakterisieren. Gemeinsam mit der Opus Denkmalpflege GmbH wird die Applikation feiner, gebrochener Blähgranulate in leichten Steinergänzungsmörteln für die Restaurierung von Sandsteinobjekten untersucht. Hierzu wird ein zweischichtiges Mörtelsystem bestehend aus einem Füll- und einem Deckmörtel entwickelt. Erste Ergebnisse der Rezepturentwicklung verschiedener kalkhaltiger Leichtmörtel mit gebrochenen Blähgranulaten werden vorgestellt. Die neuen Leichtmörtel sind hinsichtlich der entwickelten Druckfestigkeit gleichwertig gegenüber herkömmlichen Restauriermörteln.
Steelmaking slag is a by-product of steel production, of which 4.5 Mt were produced in 2020 in Germany alone. It is mainly used in road construction, earthwork and hydraulic engineering. A smaller part is returned to the metallurgical cycle, used as fertiliser or landfilled.
With this use, iron oxides still contained in steelmaking slag are lost. In addition, the possibility of producing higher-grade products from steelmaking slag is foregone. In recent decades, many researchers have investigated the production of Portland cement clinker and crude iron from basic oxygen furnace slags (BOFS) via a reductive treatment. Carbothermal treatment of liquid BOFS causes a reduction of iron oxides to metallic iron, which separates from the mineral phase due to its higher density. Simultaneously, the chemical composition of the reduced slag is adapted to that of Portland cement clinker.
In this study, German BOFS was reduced in a small-scale electric arc furnace using petrol coke as a reducing agent. The resulting low-iron mineral product has a similar chemical composition to Portland cement clinker and was rich in the tricalcium silicate solid solution alite (Ca3SiO5). Based on its chemical and mineralogical composition, similar to that of Portland cement clinker, the reduced BOFS has the potential to react comparably. In our study, the reduced BOFS produced less hydration heat than OPC, and its hydraulic reaction was delayed. However, adding gypsum has shown to accelerate the hydration rate of the reduced BOFS compared to that known from the calcium silicates of Portland cement clinker.
Further research to improve the hydraulic properties of the reduced slag is essential. If successful, producing a hydraulic binder and crude iron from BOFS has economic and ecological benefits for both the cement and steel industries.
Trace element (TE) analysis of pyrite via LA-ICP-MS can be used to reconstruct the conditions of pyrite formation in complex mineral systems. The Carpentaria province in northern Australia is host to some of the world’s highest value Zn-Pb (+Ag, Cu) deposits. The genesis of many of these deposits is controversial, with competing models of single-vs. multi-stage ore formation. In this study, LA-ICP-MS data of paragenetically constrained pyrite from the George Fisher Zn-Pb-Ag deposit has been analysed to investigate the chemistry of different stages of ore formation. Pyrite from correlative unmineralized host rocks has also been analysed to investigate evidence of distal hydrothermal anomalism. All LA-ICP-MS data have been statistically evaluated (principal component analysis) and interpreted together with whole rock lithogeochemical data of the same samples. Pre-ore diagenetic pyrite is compositionally similar to other Proterozoic diagenetic pyrite, with some evidence of minor hydrothermal anomalism that with further analysis could help define distal alteration. Pyrite from the different ore stages are compositionally distinct, consistent with a multi-stage system. Ore stage 1 pyrite exceeds background contents of Co, Cu, Zn, As, Ag, Sb, Tl, and Pb and has elevated Co/Ni ratios, whereas only Ni and Co are above background abundances in ore stage 2 and 3 pyrite, of which only ore stage 3 pyrite has high Co/Ni ratios. Ore stage 1 pyrite has a similar composition to hydrothermal pyrite in the undeformed northern Carpentaria CD-type deposits and was likely syn-diagenesis. Ore stage 2 was syn-deformation, and resulted in replacement and recrystallization of pre-existing pyrite, and the expulsion of incompatible TEs. Ore stage 3 formed via a later Cu-Zn-Pb mineralizing event that resulted in a new geochemically distinct generation of Co-rich pyrite. Overall, this study demonstrates the value of paragenetically-constrained pyrite TE data for refining genetic models in complex sediment hosted mineral systems.
Molten sulfur is found in various subaerial volcanoes. However, limited records of the pools and flows of molten sulfur have been reported: therefore, questions remain regarding the physicochemical processes behind this phenomenon. A suite of new sulfur flows, some of which active, was identified at the Lastarria volcano (northern Chile) and studied using satellite imagery, in situ probing, and temperature and video recording. This finding provides a unique opportunity to better understand the emplacement mechanisms and mineral and chemical compositions of molten sulfur, in addition to gaining insight into its origin. Molten sulfur presented temperatures of 124–158°C, with the most prolonged sulfur flow reaching 12 m from the source. Photogrammetric tools permitted the identification of levees and channel structures, with an estimated average flow speed of 0.069 m/s. Field measurements yielded a total volume of 1.45 ± 0.29 m3 of sulfur (equivalent to ∼2.07 tons) mobilized during the January 2019 event for at least 408 min. Solidified sulfur was composed of native sulfur with minor galena and arsenic- and iodine-bearing minerals. Trace element analysis indicated substantial enrichment of Bi, Sb, Sn, Cd, as well as a very high concentration of As (>40.000 ppm). The January 2019 molten sulfur manifestations in Lastarria appear to be more enriched in As compared to the worldwide known volcanoes with molten sulfur records, such as the Shiretoko-Iozan and Poás volcanoes. Furthermore, their rheological properties suggest that the “time of activity” in events such as this could be underestimated as flows in Lastarria have moved significantly slower than previously thought. The origin of molten sulfur is ascribed to the favorable S-rich chemistry of fumarolic gases and changes in host rock permeability (fracture opening). Molten sulfur in Lastarria correlates with a peak in activity characterized by high emissions of SO2 and other acid species, such as HF and HCl, in addition to ground deformation. Consequently, molten sulfur was framed within a period of volcanic unrest in Lastarria, triggered by changes in the magmatic-hydrothermal system. The appearance of molten sulfur is related to physicochemical perturbations inside the volcanic system and is perhaps a precursor of eruptive activity, as observed in the Poás and Turrialba volcanoes.
Synthesis of Li ion battery materials via ball milling has been a huge area of growth, leading to new high-capacity electrode materials, such as a number of promising disordered rocksalt (DRS) phases. In prior work, it was generally assumed that the synthesis was facilitated simply by local heating effects during the milling process. In this work, we show that ball milling Li2MoO4 leads to a phase transformation to the high pressure spinel polymorph and we report electrochemical data for this phase. This observation of the formation of a high pressure polymorph shows that local heating effects alone cannot explain the phase transformation observed (phenakite to spinel) and so indicates the importance of other effects. In particular, we propose that when the milling balls collide with the material, the resulting shockwaves exert a localised pressure effect, in addition to local heating. To provide further support for this, we additionally report ball milling results for a number of case studies (Li2MnO3, Li2SnO3, Nb2O5) which reinforces the conclusion that local heating alone cannot explain the phase transformations observed. The work presented thus provides greater fundamental understanding of milling as a synthetic pathway and suggests potential strategies to prepare such samples without milling (e.g., doping to create internal chemical pressure). In addition, we suggest that further research is needed into the effect of the use of milling as a route to smaller particles, since we believe that such milling may also be affecting the surface structure of the particles through the influence of the shockwaves generated.
This data article introduces a dataset comprising 1630 alkali-activated concrete (AAC) mixes, compiled from 106 literature sources. The dataset underwent extensive curation to address feature redundancy, transcription errors, and duplicate data, yielding refined data ready for further data-driven science in the field of AAC, where this effort constitutes a novelty. The carbon footprint associated with each material used in the AAC mixes, as well as the corresponding CO2 footprint of every mix, were approximated using two published articles. Serving as a foundation for future expansions and rigorous data applications, this dataset enables the characterization of AAC properties through machine learning algorithms or as a benchmark for performance comparison among different formulations. In summary, the dataset provides a resource for researchers focusing on AAC and related materials and offers insights into the environmental benefits of substituting traditional Portland concrete with AAC.
In recent decades, the number of components in concrete has grown, particularly in formulations aimed at reducing carbon footprints. Innovations include diverse binders, supplementary cementitious materials, activators, concrete admixtures, and recycled aggregates. These developments target not only the enhancement of material properties but also the mitigation of the ecological and economic impacts of concrete — the most extensively used material by humankind. However, these advancements also introduce a greater variability in the composition of raw materials. The material’s behavior is significantly influenced by its nanoscale properties, which can pose challenges in accurate characterization. Consequently, there’s an increasing need for experimental tuning of formulations. This is accompanied by a more inconsistent composition of raw materials, which makes an experimental tuning of formulations more and more necessary. However, the increased complexity in composition presents a challenge in finding the ideal formulation through trial and error. Inverse design (ID) techniques offer a solution to this challenge by allowing for a comprehensive search of the entire design space to create new and improved concrete formulations. In this publication, we introduce the concept of ID and demonstrate how our open-source app “SLAMD” provides all necessary steps of the workflow to adapt it in the laboratory, lowering the application barriers. The intelligent screening process, guided by a predictive model, leads to a more efficient and effective data-driven material design process resulting in reduced carbon footprint and improved material quality while considering socio-economic factors in the materials design.
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
Titanium and its alloys are known to allow the straightforward laser-based manufacturing of ordered surface nanostructures, so-called high spatial frequency laser-induced periodic surface structures (HSFL). These structures exhibit sub-100 nm spatial periods – far below the optical diffraction limit. The resulting surface functionalities are usually enabled by both, topographic and chemical alterations of the nanostructured surfaces. For exploring these effects, multi-method characterizations were performed here for HSFL processed on Ti–6Al–4V alloy upon irradiation with near-infrared ps-laser pulses (1030 nm, ≈1 ps pulse duration, 1–400 kHz) under different laser scan processing conditions, i.e., by systematically varying the pulse repetition frequency and the number of laser irradiation passes. The sample characterization involved morphological and topographical investigations by scanning electron microscopy (SEM), atomic force microscopy (AFM), tactile stylus profilometry, as well as near-surface chemical analyses hard X-ray photoelectron spectroscopy (HAXPES) and depth-profiling time-of-flight secondary ion mass spectrometry (ToF-SIMS). This provides a quantification of the laser ablation depth, the geometrical HSFL characteristics and enables new insights into the depth extent and the nature of the non-ablative laser-induced near-surface oxidation accompanying these nanostructures. This allows to answer the questions how the processing of HSFL can be industrially scaled up, and whether the latter is limited by heat-accumulation effects.
Although the use of noble metal catalysts can increase the efficiency of hydrogen evolution reaction, the process is still limited by the characteristics of the metal-hydrogen (M−H) bond, which can be too strong or too weak, depending on the metal employed. Studies revealed that the hydrogen affinity for the metal surface (i.e. H absorption/desorption) is regulated also by the potential at the metal nanoparticles. Through controlled periodic illumination (CPI) of a series of metal/TiO2 suspensions, here we demonstrated that an increase of the HER efficiency is possible for those photodeposited metals which have a Tafel slope below 125 mV. Two possible explanations are here reported, in both of them the M−H interaction and the metal covering level play a prominent role, which also depend on the prevailing HER mechanism (Volmer-Heyrovsky or Volmer-Tafel).
Even after autoclaving for 2 weeks in the temperature range of 120 to 200 °C, CEM I paste with a water-to-cement ratio of 0.5 still contains some low-crystalline C-S-H. The conversion of low-crystalline C-S-H to crystalline is faster with increasing temperature. The observed low-crystalline and crystalline C-S-H phases (reinhardbraunsite, jaffeite) indicate that the sampleshave not reached equilibrium yet under these experimental conditions. However, there is a good agreement between experiment and thermodynamic modelling for the other solids indicating that the used datasets are suitable for this application.
AbstractIn this study, we present a screening method based on molecular absorption spectrometry to study PFAS uptake and fate in plants. To evaluate the suitability of this method we analyzed plant extracts with molecular absorption spectrometry (MAS) as well as liquid chromatography–tandem mass spectrometry (LC–MS/MS) for mass balance studies (w(F)). French bean plants (Phaseolus vulgaris) were grown on soil spiked using eight PFAS substances that vary in chain length and functional group composition. Specifically, these include three short-chained (C4–C5), five long-chained (C7–C10) carboxylic acids, one sulfonic acid and one sulfonic amide moieties. To investigate substance-specific PFAS uptake systematically, PFAS were spiked as single substance spike. Additionally, we studied one mixture of the investigated substances in equal proportions regarding w(F) and four PFAS mixtures of unknown composition. After 6 weeks, the plants were separated into four compartments. We analyzed the four compartments as well as the soil for extractable organically bound fluorine (EOF) by high resolution-continuum source-graphite furnace-molecular absorption spectrometry (HR-CS-GFMAS) as well as for sum of ten target-PFAS by LC–MS/MS. All three short-chained PFAS perfluorobutanoic acid (PFBA), perfluorobutanoic sulfonic acid (PFBS) and perfluoropentanoic acid (PFPeA) were determined in high concentrations mainly in the fruits of the investigated plants while long-chained PFAS perfluorononanoic acid (PFNA) and perfluorodecanoic acid (PFDA) were mainly determined in roots. PFBS was determined in remarkably high concentrations in leaves compartment by both quantification methods. Overall, comprehensive results of single substance spikes were in good agreement for both methods except for a few cases. Hence, two phenomena were identified: for mixed PFAS spikes of unknown composition huge differences between EOF and sum of target PFAS were observed with systematically higher EOF values. Overall, both methods indicate comparable results with MS being more reliable for known PFAS contamination and MAS being more valuable to identify PFAS exposure of unknown composition.
Graphical Abstract