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Paper des Monats
- ja (72)
Additive manufacturing of concrete structures is a novel and emerging technology. Freecontouring in civil engineering, which allows for entirely new designs, is a significant advantage. Inthe future, lower construction costs are expected with increased construction speeds and decreasingrequired materials and workers. However, architects and civil engineers rely on a certain quality ofexecution to fulfil construction standards. Although several techniques and approaches demonstratethe advantages, quality control during printing is highly challenging and rarely applied. Due to thecontinuous mixing process commonly used in 3D concrete printing, it is impossible to exclude varia-tions in the dry mixture or water content, and a test sample cannot be taken as a representative samplefor the whole structure. Although mortar properties vary only locally, a defect in one layer duringprinting could affect the entire integrity of the whole structure . Therefore, real-time process monitor-ing is required to record and document the printing process.At the Bundesanstalt für Materialforschung und -prüfung (BAM) a new test rig for the additive man-ufacturing of concrete is built. The primary purpose is measuring and monitoring the properties of amortar during the printing process.The following study investigates an approach for calculating yield stress and plastic viscosity based onexperimentally recorded pressure data. The calculations assume that fresh mortar behaves as a Bing-ham fluid and that the Buckingham-Reiner-equation is applicable. A test setup consisting of rigid pipeswith integrated pressure sensors at different positions is utilized.Monitoring the printing process with different sensors is crucial for the quality control of an ongoingprocess.
Various software products for the simulation of industrial X-ray radiography have been developed in recent years (e.g., aRTist 2, CIVA CT, Scorpius XLab, SimCT, Wilcore) and their application potential has been shown in numerous works. However, full systematic approaches to characterise a specific CT system for these simulation software products to obtain a truthful digital twin are still missing. In this contribution, we want to present two approaches to obtain realistic grey values in X-ray projections in aRTist 2 simulations based on measured projections. In aRTist 2, the displayed grey value of a pixel is based on the energy density incident on that pixel.
The energy density is calculated based on the X-ray tube spectrum, the attenuation between source and detector as well as an energy-dependent sensitivity curve of the detector. The first approach presented in this contribution uses the sensitivity curve as a free modelling parameter. We measured the signal response at different thicknesses of Al EN-AW6082 at different tube voltages (i.e., different tube spectra). We then regarded the grey values displayed by these projections as a data regression respectively an optimisation problem and obtained the sensitivity curve that is best able to reproduce the measured behaviour in aRTist 2. The resulting sensitivity curve does not necessarily hold physical meaning but is able to simulate the real system behaviour in the simulation software.
The second approach presented in this contribution is to estimate the sensitivity curve based on assumptions about the characteristics of the scintillation detector (e.g., scintillator material, scintillator thickness and signal processing characteristics). For this approach, a linear response function (linear relationship between the deposited energy per pixel and the resulting grey value) is assumed. If the detector characteristics, which affect the simulated deposited energy, are properly modelled, the slope and offset of the response function to match the measured grey values should be the same for different tube spectra. As the offset is constant and given by the grey values measured at no incident radiation, the slope is the remaining parameter to evaluate the success of the detector modelling. We therefore adapted the detector characteristics by changing the detector setup until the slope was nearly the same for all measured tube spectra. We are aware that the resulting parameters of the scintillator material and thickness might not be the real ones, but with those modelling parameters we are able to simulate realistic grey values in aRTist 2. Both of those approaches could potentially be a step forward to a full systematic approach for a digital twin of a real CT system in aRTist 2.
Homochirality is an obvious feature of life on Earth. On the other hand, extraterrestrial samples contain largely racemic compounds. The same is true for any common organic synthesis. Therefore, it has been a perplexing puzzle for decades how these racemates could have formed enantiomerically enriched fractions as a basis for the origin of homochiral life forms. Numerous hypotheses have been put forward as to how preferentially homochiral molecules could have formed and accumulated on Earth. In this article, it is shown that homochirality of the abiotic organic pool at the time of formation of the first self-replicating molecules is not necessary and not even probable. It is proposed to abandon the notion of a molecular ensemble and to focus on the level of individual molecules. Although the formation of the first self-replicating, most likely homochiral molecule, is a seemingly improbable event, on a closer look, it is almost inevitable that some homochiral molecules have formed simply on a statistical basis. In this case, the non-selective leap to homochirality would be one of the first steps in chemical evolution directly out of a racemic “ocean”. Moreover, most studies focus on the chirality of the primordial monomers with respect to an asymmetric carbon atom. However, any polymer with a minimal size that allows folding to a secondary structure would spontaneously lead to asymmetric higher structures (conformations). Most of the functions of these polymers would be influenced by this inherently asymmetric folding. Furthermore, a concept of physical compartmentalization based on rock nanopores in analogy to nanocavities of digital immunoassays is introduced to suggest that complex cell walls or membranes were also not required for the first steps of chemical evolution. To summarize, simple and universal mechanisms may have led to homochiral self-replicating systems in the context of chemical evolution. A homochiral monomer pool is deemed unnecessary and probably never existed on primordial Earth.
In this study, we demonstrate the applicability of nitrogen microwave inductively coupled atmospheric pressure mass spectrometry (MICAP-MS) for Ca, Fe, and Se quantification in human serum using isotope dilution (ID) analysis. The matrix tolerance of MICAP-MS in Na matrix was investigated, uncovering that high Na levels can suppress the signal intensity. This suppression is likely due to the plasma loading and the space charge effect. Moreover, 40Ca and 44Ca isotopic fractionation was noted at elevated Na concentration. Nine certified serum samples were analyzed using both external calibration and ID analysis. Overestimation of Cr, Zn, As, and Se was found in the results of external calibration, which might be resulted from C-induced polyatomic interference and signal enhancement, respectively. Further investigations performed with methanol showed a similar enhancement effect for Zn, As, and Se, potentially supporting this assumption. The mass concentrations determined with ID analysis show metrological compatibility with the reference values, indicating that MICAP-MS combined with ID analysis can be a promising method for precise Ca, Fe, and Se determination. Moreover, this combination reduces the influences of matrix effects, broadening the applicability of MICAP-MS for samples with complex matrix.
In materials science, traditional techniques for analyzing layered structures are essential for obtaining information about local structure, electronic properties and chemical states. While valuable, these methods often require high vacuum environments and have limited depth profiling capabilities. The grazing exit x-ray absorption near-edge structure (GE-XANES) technique addresses these limitations by providing depth-resolved insight at ambient conditions, facilitating in situ material analysis without special sample preparation. However, GE-XANES is limited by long data acquisition times, which hinders its practicality for various applications. To overcome this, we have incorporated Bayesian optimization (BO) into the GE-XANES data acquisition process. This innovative approach potentially reduces measurement time by a factor of 50. We have used a standard GE-XANES experiment, which serve as reference, to validate the effectiveness and accuracy of the BO-informed experimental setup. Our results show that this optimized approach maintains data quality while significantly improving efficiency, making GE-XANES more accessible to a wider range of materials science applications.
Quantitative microstructural analysis of XCT 3D images is key for quality assurance of materials and components. In this paper we implement a Graph Convolutional Neural Network (GCNN) architecture to segment a complex Al-Si Metal Matrix composite XCT volume (3D image). We train the model on a synthetic dataset and we assess its performance on both synthetic and experimental, manually-labeled, datasets. Our simple GCNN shows a comparable performance, measured via the Dice score, to more standard machine learning methods, but uses a greatly reduced number of parameters (less than 1/10 of parameters), features low training time, and needs little hardware resources. Our GCNN thus achieves a cost-effective reliable segmentation.
Silicon isotope fractionation during silicification is poorly understood and impedes our ability to decipher paleoenvironmental conditions from Si isotopes in ancient cherts. To investigate isotope fractionation during silica-for-carbonate replacement we analyzed the microscale Si and O isotope composition in different silica phases in a silicified zebra dolostone as well as their bulk δ18O and Δ’17O compositions. The subsequent replacement of carbonate layers is mimicked by decreasing δ18O and δ30Si. The textural relationship and magnitude of Si and O isotope fractionation is best explained by near-quantitative silica precipitation in an open system with finite Si. A Rayleigh model for silicification suggests positive Ɛ30/28Si during silicification, conforming with predictions for isotope distribution at chemical equilibrium from ab-initio models. Application of the modelled Ɛ30Si-T relationship yields silicification temperatures of approx. 50°C. To reconcile the δ18Ochert composition with these temperatures, the δ18O of the fluid must have been between -2.5 and -4 ‰, compositions for which the quartz phases fall close to the oxygen equilibrium fractionation line in three-isotope space. Diagenetic silica replacement appears to occur in O and Si isotopic equilibrium allowing reconstructions of temperatures of silicification from Si isotopes and derive the δ18O composition of the fluid – a highly desired value needed for accurate reconstructions of the temperature- and δ18O histories of the oceans.
The European Commission has recently announced two guiding principles for EU product policy: First, product policy shall ensure that the performance of front-runner products in terms of sustainability becomes the norm, and second, the effectiveness of the current Ecodesign legislative framework is going to be significantly improved. Within this paper, already existing front-runner approaches and recent and ongoing product policy-making processes were reviewed. Based on the results, an EU front-runner approach is outlined. The presented approach (i) refers to performance levels of the best products already available on the market, (ii) aggregates information in existing databases, and (iii) works semi-automated. Together, all three attributes have a high potential to facilitate and accelerate the specification of appropriate minimum requirements for products at the EU level. This way, EU policymakers can deliver on the core objectives of the Ecodesign legislative framework much better. The basic mechanism and its legal entrenchment of the approach are illustrated for the energy efficiency of energy-related products. In addition, the Front-Runner Approach can be applied to any product group in the scope of the upcoming Ecodesign for Sustainable Products Regulation and to a wide range of product-related minimum requirements, such as durability, reparability, or recycled content. The study’s objective is to suggest a tailor-made and dynamic approach to keep the EU product legislation up to date using innovative technology based on the investigation of current regulations and identify the gap. Experiences from three international case studies suggest that a front-runner approach to setting energy-performance standards can drive innovation and reduce energy consumption via promoting energy-efficient products; transparency about available products is one of the key factors and can be established by a database. The EU front-runner approach comprises extending the existing energy label database (or making use of the digital product passport) and introducing a legislative procedure that triggers changes in the energy efficiency requirements in the specific EU regulations if the database shows that a certain threshold value is reached. Challenges such as limited EU staff capacities and opportunities such as increased dynamic are discussed.
The LOBSTER (Deringer et al., 2011;Maintz et al., 2013 ,2016 ;Nelson et al., 2020 ) software aids in extracting quantum-chemical bonding information from materials by projecting the plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. LobsterEnv, a module implemented in pymatgen (Ong et al., 2013) by some of the authors of this package, facilitates the use of quantum-chemical bonding information obtained from LOBSTER calculations to identify neighbors and coordination environments. LobsterPy is a Python package that offers a set of convenient tools to further analyze and summarize the LobsterEnv outputs in the form of JSONs that are easy to interpret and process. These tools enable the estimation of (anti) bonding contributions, generation of textual descriptions, and visualization of LOBSTER computation results. Since its first release, both LobsterPy and LobsterEnv capabilities have been extended significantly. Unlike earlier versions, which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs) (Dronskowski & Blöchl, 1993), both can now also analyze Crystal Orbital Overlap Populations (COOP) (Hughbanks & Hoffmann, 1983) and Crystal Orbital Bond Index (COBI) (Müller et al., 2021). Extracting the information about the most important orbitals contributing to the bonds is optional, and users can enable it as needed. Additionally, bonding-based features for machinelearning (ML) studies can be engineered via the sub-packages “featurize” and “structuregraphs”. Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. LobsterPy has been used to produce the results in Ngo et al. (2023), Chen et al. (2024), Naik et al. (2023), and it is also part of Atomate2 (2023) bonding analysis workflow for generating bonding analysis data in a format compatible with the Materials Project (Jain et al., 2013) API.
We present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into compute jobs that can be stitched together into complex workflows. Jobflow fully supports dynamic workflows where the full acyclic graph of compute jobs is not known until runtime, such as compute jobs that launch
other jobs based on the results of previous steps in the workflow. The results of all Jobflow compute jobs can be easily stored in a variety of filesystem- and cloud-based databases without the data storage process being part of the underlying workflow logic itself. Jobflow has been intentionally designed to be fully independent of the choice of workflow manager used to dispatch the calculations on remote computing resources. At the time of writing, Jobflow
workflows can be executed either locally or across distributed compute environments via an adapter to the FireWorks package, and Jobflow fully supports the integration of additional workflow execution adapters in the future.