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Paper des Monats
- ja (71)
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.
The transition toward a hydrogen-based economy requires a widespread transport and distribution network, and repurposed natural gas pipelines are a viable option. An assessment of the hydrogen-induced degradation of pipeline steels is needed to inject H2 gas into the existing infrastructure safely. The conservative and standardized method consists of in-situ tensile tests in an autoclave filled with high-pressure hydrogen gas. A proposed alternative method involves
using a hollow specimen as containment volume and applying the gas pressure in the inner cavity.
This technique has lower costs and shorter test preparation time but is not standardized yet. This study aims to evaluate and compare the tensile properties of API 5L X65 pipeline steel in two states: vintage and modern. The influence of the surface roughness is investigated through parallel tests with drilled and reamed specimens. Hydrogen tests are compared with reference tests in an inert environment. A significant hydrogen-induced decrease in tensile properties is observed, and no significant difference between vintage and modern X65 can be drawn. The reduction in tensile properties is more significant in specimens with higher inner surface roughness. The evaluation of surface conditions appears crucial when assessing the HE susceptibility of hydrogen transport and storage equipment.
EPDM, HNBR and FKM materials were exposed at 150 ◦C to air under atmospheric pressure and to hydrogen at 50 bar for different ageing times. All measurements after hydrogen exposure were conducted on samples in degassed condition to assess irreversible effects resulting from that exposure and to compare them to those after ageing in air. Density, hardness, tensile properties, compression set, and hydrogen permeability of all samples were analysed. In both ageing environments, HNBR exhibited the most significant changes of material properties.
However, for both EPDM and HNBR, considerably less severe ageing effects were observed under hydrogen in comparison to ageing in air. On the other hand, FKM showed about the same low level of deterioration in both ageing environments but exhibited poor resistance against damage due to rapid gas decompression in hydrogen environment that can lead to seal failure. The obtained results may serve as a guidance toward a better understanding for design and utilisation of elastomeric materials in future hydrogen infrastructure components.
The classification of incineration bottom ash (IBA) as hazardous or non-hazardous according to ecotoxic hazard property HP14 is still under debate. In this context, only the compounds of Zn and Cu with the hazard statement code H410 are of relevance. With an approach based on the grouping of substances, it was shown that such substances are either readily water-soluble or slightly and sparingly soluble. The concentrations of readily soluble Cu and Zn compounds in IBA are far below the cut-off value of 0.1%. Slightly and sparingly soluble Zn and Cu compounds could be quantified in the first fraction of a four-step sequential extraction procedure. With the results from the complete sequence, the dimensionless synthesis toxicity index (STI) was calculated and was in the range of 494 to 1218 for the four investigated IBA samples. It was concluded that IBA can usually be classified as non-hazardous.
AbstractLithium‐ion batteries, essential for electronics and electric vehicles, predominantly use cathodes made from critical materials like cobalt. Sulfur‐based cathodes, offering a high theoretical capacity of 1675 mAh g−1 and environmental advantages due to sulfur's abundance and lower toxicity, present a more sustainable alternative. However, state‐of‐the‐art sulfur‐based electrodes do not reach the theoretical capacities, mainly because conventional electrode production relies on mixing of components into weakly coordinated slurries. Consequently, sulfur‘s mobility leads to battery degradation—an effect known as the “sulfur‐shuttle”. This study introduces a solution by developing a microporous, covalently‐bonded, imine‐based polymer network grown in situ around sulfur particles on the current collector. The polymer network (i) enables selective transport of electrolyte and Li‐ions through pores of defined size, and (ii) acts as a robust host to retain the active component of the electrode (sulfur species). The resulting cathode has superior rate performance from 0.1 C (1360 mAh g−1) to 3 C (807 mAh g−1). Demonstrating a high‐performance, sustainable sulfur cathode produced via a simple one‐pot process, our research underlines the potential of microporous polymers in addressing sulfur diffusion issues, paving the way for sulfur electrodes as viable alternatives to traditional metal‐based cathodes.
Transparent conductive oxides such as indium tin oxide (ITO) are standards for thin film electrodes, providing a synergy of high optical transparency and electrical conductivity. In an electrolytic environment, the determination of an inert electrochemical potential window is crucial to maintain a stable material performance during device operation. We introduce operando ellipsometry, combining cyclic voltammetry (CV) with spectroscopic ellipsometry, as a versatile tool to monitor the evolution of both complete optical (i.e., complex refractive index) and electrical properties under wet electrochemical operational conditions. In particular, we trace the degradation of ITO electrodes caused by electrochemical reduction in a pH-neutral, water-based electrolyte environment during electrochemical cycling. With the onset of hydrogen evolution at negative bias voltages, indium and tin are irreversibly reduced to the metallic state, causing an advancing darkening, i.e., a gradual loss of transparency, with every CV cycle, while the conductivity is mostly conserved over multiple CV cycles. Post-operando analysis reveals the reductive (loss of oxygen) formation of metallic nanodroplets on the surface. The reductive disruption of the ITO electrode happens at the solid–liquid interface and proceeds gradually from the surface to the bottom of the layer, which is evidenced by cross-sectional transmission electron microscopy imaging and complemented by energy-dispersive X-ray spectroscopy mapping. As long as a continuous part of the ITO layer remains at the bottom, the conductivity is largely retained, allowing repeated CV cycling. We consider operando ellipsometry a sensitive and nondestructive tool to monitor early stage material and property changes, either by tracing failure points, controlling intentional processes, or for sensing purposes, making it suitable for various research fields involving solid–liquid interfaces and electrochemical activity.