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Traceable morphological and chemical characterization of nanomaterials with respect to the various possible sizes, size distributions, shapes, and concentrations of real-world nanoparticles (NPs) is a challenging task. Particularly for the nonspherical, non-monodisperse nanoparticles – as typically for most of the commercial particles, including their strong tendency to agglomerate, there is a lack of standard operation procedures providing accurate nanoparticle characterisation.
In the framework of the pre-standardisation framework of VAMAS (Versailles Project on Advanced Materials and Standards, www.vamas.org) two interlaboratory comparison (ILC) studies are being carried out under the Technical Working Area (TWA) 34 “Nanoparticle Populations”:i) Project #15 addresses the analysis of the size and shape distribution of TiO2 bipyramidal NPs by traceable imaging methods such as TEM, SEM, STEM-in-SEM, AFM as well as with SAXS as an ensemble method. This ILC is thought as the next level development of the case studies exemplified in the published ISO standards ISO 21363 and ISO 19749. It was agreed to complete the first ILC with the NPs already prepared according to the same procedure on a TEM grid, and, at a later stage, to carry out second ILCs with the same NPs distributed to the participants as liquid suspensions together with protocols for the uniform NP deposition on suited substrates - as developed and optimized within the European project nPSize. Once having good deposition protocols available, the door for automated image analysis gets opened. Corresponding image analysis protocols and reporting templates have been distributed to the ILC participants, too.
ii) Project #16: two spherical SiO2 NP samples with bi-modal size distributions in two nominal relative number concentrations were prepared and distributed also as liquid suspensions accompanied by sample preparation, measurement, and image analysis protocols and reporting templates. Here, the NP concentration is the primary parameter to be measured. For the imaging methods it is targeted to
measure the relative nanoparticle concentrations (relative populations of the two modes).
The results of all the participating laboratories, in both ILCs, compiled in comparative representations will be shown and discussed for the first time. The reduction of the measurement uncertainties associated to the size, shape and number-concentration results induced by the significant improvement of the sample preparation on substrates (as single particles with a high-density coverage), combined with welldefined image analysis procedures will be highlighted.
Following parts of the research project as proceeded at University of Birminngham are presented: Translocation, Experiments, Synthesis of DNA Structures, Antibody, Biomarker, Binding. Following works have been carried out at BAM: High Resolution Electron Microscopy, Dedicated Sample Preparation, Surface Analysis Methods.
In the course of decarbonizing the energy industry, cryogenic energy carriers as liquefied hydrogen (LH2) and liquefied natural gas (LNG) are seen as having great potential. In technical applications, the challenge is to keep these energy carriers cold for a long time. This is achieved in the road transport sector and also stationary applications by thermal super insulations (TSI) which based on double-walled tanks with vacuum and multilayer insulation (MLI) in the interspace. This study focuses on the behaviour of widely used combustible MLI in a fire scenario, at vacuum and atmospheric pressure conditions. The former corresponds to the typical design condition and the latter to the condition after an outer hull rapture of a tank. Furthermore, two fire scenarios were taken into account: a standard-oriented approach and a hydrocarbon fire-oriented approach. For the study, a test rig was applied that allows testing of TSI at industrial conditions and subsequent analysis of TSI samples. The test rig allows thermal loading and performance analysis of TSI samples at the same time. Comparing the tests, the samples degraded differently. However, no sudden failure of the entire MLI was observed in any test. These results are relevant for the evaluation of incidents with tanks for the storage of cryogenic fluids and can thus contribute to the improvement of TSI and the development of emergency measures for the protection of persons and infrastructures.
Analyzing materials composed of multiple elements with spectroscopic techniques such as X-ray Photoelectron Spectroscopy (XPS), Auger-Electron Spectroscopy (AES) or Electron Probe Microanalysis (EPMA), can be challenging due to spectral overlap. Moreover, each analytical method introduces its own set of challenges, e.g., the strong secondary fluorescence effect for neighbor elements in EPMA, thus, making the accurate elemental quantification in such materials difficult. When the material is available as thin film, additional constraints are inherently present. To provide a reference material for these analytical challenges high entropy alloys (HEAs) are excellent candidates. Currently, there is no thin film reference available containing more than 2 elements.
Unlike traditional alloys, which typically consist of one or two main elements and smaller amounts of secondary elements, HEAs are characterized by the presence of multiple principal elements in almost equal proportions. This unique composition results in a high degree of disorder at the atomic level, leading to exceptional mechanical, physical, and often unexpected properties. HEAs have garnered significant attention in materials science and engineering due to their potential applications in a wide range of industries, from aerospace and automotive to electronics and renewable energy.
For the preparation of a dedicated thin film reference material, we have chosen to prepare HEAs by magnetron sputtering, since it is not associated with elemental segregation that may occur due to the high configurational entropy of HEAs, which promotes atomic rearrangements. Our goal was to prepare films with a homogeneous thickness and defined chemical composition to be analyzed by various methods dedicated to surface analysis.
The material, consisting of titanium, chromium, manganese, iron, and nickel was deposited as film on silicon substrates - to our knowledge the first HEA thin film of this type reported in literature. A set of different thickness of the films was chosen, on the one hand to facilitate the analysis with different techniques, and on the other side to evaluate the limitations of the respective methods. The films morphology was characterized as prepared by SEM followed by the analysis of their 2D compositional homogeneity by EDX, XRF, ToF-SIMS and XPS. in-depth chemical composition was evaluated by ToF-SIMS and AES. The outcomes of analyzing the initial batches of films will be presented.
Laser Powder Bed Fusion (PBF-LB/M) is a layer wise metal additive manufacturing (AM) technology, which enables significant advancements of component design, leading to potential efficiency and performance improvements. However, the thermal cycles inherent to the process comprising large localized thermal gradients and repeated melting and solidification cycles leads to the generation of high magnitude residual stresses. These residual stresses can be detrimental both during manufacturing of components and in subsequent application. Therefore, a deep understanding of the influence of process parameters on the residual stresses are crucial for efficient manufacturing and safe application. The experimental characterization of these residual stresses is therefore crucial and can provide a reliable baseline for simulations of both the process and applications.
Diffraction-based methods for residual stress analysis using penetrating neutrons and high energy X-rays enable non-destructive spatially resolved characterization of both surface and bulk residual stresses. However, the unique microstructural features inherent to the process can challenge some of our assumptions when using these methods. These challenges include the determination of a stress-free reference, the use of correct elastic constants (both SCEC and DEC) and the influence of surface roughness, texture, and porosity on residual stresses.
This presentation will detail recent insights and recommendations for the characterization of residual stresses in a range of PBF-LB/M metallic alloys (Fe, Ni, Al and Ti)
Graphene has been commercialized for over a decade. It is usually used in the form of suspensions or inks. In this study, we analyze the starting material for commercial functionalized graphene (FG) solutions and inks as well as their starting material (FG powders) using X-ray photoelectron spectroscopy (XPS), scanning electron microscope (SEM), energy-dispersive X-Ray spectroscopy (EDX), time of flight secondary ion mass spectrometry (ToF-SIMS) and Auger electron spectroscopy (AES). Graphene was functionalized with fluorine, oxygen, ammonia, and carboxylic acid.
Our results suggest a significant effect of water and commercial resins on the presence as well as the morphological behavior of graphene and associated functionalized group. For example, XPS analysis shows some significant differences between the solutions and the starting materials (powders). These changes can be explained by the location of the functionalization at the outer most surface as indicated by Chemello et al.
The determination of residual stress in additively manufactured materials is a challenge, even after decades from the establishment of the basics of residual stress analysis. This is due to the peculiar microstructure of such materials. In fact, researchers have discovered that conventional methods for the determination of RS in materials do not properly work for AM materials. In this tutorial, the basics of RS analysis will be explained, together with the basics of AM manufacturing techniques. The microstructure of the peculiar materials (AM) dealt with here will be elucidated. Successively, the necessary modifications to the conventional approaches to RS analysis will be explained and case studies will be displayed, for the attendant to touch with hands the peculiarities of the approaches. Finally, a few experimental and theoretical tips will be given on dos and don’ts for a correct determination of RS in AM materials.
Graphene has been commercialized for over a decade. It is usually used in the form of suspensions or inks. In this study, we analyze the starting material for commercial functionalized graphene (FG) solutions and inks as well as their starting material (FG powders) using X-ray photoelectron spectroscopy (XPS), scanning electron microscope (SEM), energy-dispersive X-Ray spectroscopy (EDX), time of flight secondary ion mass spectrometry (ToF-SIMS) and Auger electron spectroscopy (AES). Graphene was functionalized with fluorine, oxygen, ammonia, and carboxylic acid.
Our results suggest a significant effect of water and commercial resins on the presence as well as the morphological behavior of graphene and associated functionalized group. For example, XPS analysis shows some significant differences between the solutions and the starting materials (powders). These changes can be explained by the location of the functionalization at the outer most surface as indicated by Chemello et al.
Ensuring the purity of air and water is essential for the overall well-being of life on earth and the sustainability of the planet's diverse ecosystems. To achieve the goal of zero pollution, as outlined in the 2020 European Green Deal by the European Commission,[1] significant efforts are in progress. A key aspect of this commitment involves advancing more efficient and economically viable methods for treating wastewater. This includes the systematic monitoring of harmful pollutants such as heavy metals, microplastics, pesticides, and pharmaceuticals.
One example is the presence of the anti-inflammatory drug diclofenac in water systems, primarily originating from its use as a gel or lotion for joint pain treatment. Diclofenac contamination in surface waters has been detected at approximately 10 μg L-1 (0.03 μM)[2] which is not solely due to widespread usage but also because of the drug's resistance to microbial degradation. Conventional wastewater treatment plants (WWTPs), which rely on biodegradation, sludge sorption, ozone oxidation, and powdered activated carbon treatment, struggle to efficiently remove diclofenac from wastewater.[3],[4] For instance, to enable WWTPs to efficiently monitor and optimize their processes, it would be advantageous to develop on-site detection and extraction methods for persistent pharmaceutical residues in aqueous samples.
In this work, a sol-gel process was used to prepare Nile blue-doped silica nanoparticles (dSiO2-NPs) with a diameter of ca. 30 nm that were further functionalized to enable reversible-addition-fragmentation chain-transfer (RAFT) polymerization. To achieve fluorescence detection, a fluorescent monomer was used as a probe for diclofenac in ethyl acetate, generating stable complexes through hydrogen bond formation. The diclofenac/fluorescent monomer complexes were imprinted into thin molecularly imprinted polymer (MIP) shells on the surface of the dSiO2-NPs. Thus, the MIP binding behaviour could be easily evaluated by fluorescence titrations to monitor the spectral changes upon addition of the analyte. Doping the core substrate with Nile blue generates effective dual fluorescent signal transduction. This approach does not solely depend on a single fluorescence emission band in response to analyte recognition. Instead, it enables the fluorescent core to function as an internal reference, minimizing analyte-independent factors such as background fluorescence, instrumental fluctuation, and operational parameters.[5] Rebinding studies showed that the MIP particles have excellent selectivity towards the imprinted template and good discrimination against the competitor ibuprofen, with a discrimination factor of 2.5. Additionally, the limit of detection was determined to be 0.6 μM. Thus, with further optimization of the MIP, there is potential for the development of a MIP-based biphasic extract-&-detect fluorescence assay for simple, sensitive and specific sensing of diclofenac in aqueous samples down to the required concentrations of 0.03 μM.
Electron microscopy applied in different operating modes, e.g., SEM, TEM or STEM-in-SEM, is the gold standard method to investigate the exact size and shape of individual nanoparticles. However, when fine nanoparticles with a non-monodisperse size distribution and non-spherical shapes are analysed, achieving an accurate result is challenging. Deviations in size measurements of more than 10% may occur. Understanding of the contrasts and sensitivities characteristic to the individual operating modes of an electron microscope is key in interpreting and evaluating quantitatively the measurement uncertainties needed for an eventual certification of specific nanoparticles via traceable results. Further, beyond the pure measurement, the other components in the analysis workflow with significant impact on the overall measurement uncertainties are the sample preparation and the image segmentation. In the present study the same areas of selected iron oxide fine nanoparticles (<25 nm) as reference nanomaterial (candidate) prepared on substrate for electron microscopy imaging are analysed correlatively with SEM, STEM-in-SEM and TEM with respect to their size and shape distribution. Individual significant measurement uncertainties are discussed, e.g., the sensitivity of secondary electron detectors of InLens-type to the surface morphology, particularly to the presence of an ultrathin organic coating or signal saturation effects on the particle edges, to electron beam exposure, to surface contamination, or the selection of the threshold for image segmentation. Another goal of this study is to establish a basis of analysis conditions which shall guarantee accurate results when both manual and particularly (semi-)automated segmentation approaches are applied. Advantages as well as limitations of the use of different electron microscopy operating modes, applied individually and correlatively, are highlighted.
The EU member states have set out ambitious long-term goals for deploying offshore wind energy. The installed offshore wind capacity is set to increase from 14.6 GW in 2021 to around 320 GW in 2050. This signifies the role of offshore wind energy as a major contributor to reaching the EU’s climate and energy goals. To ensure that the defined targets are met, a significant number of new wind farms has to be installed and existing wind farms reaching the end of their planned life need to be reused efficiently. Some of the relevant reuse alternatives are lifetime extension, repowering based on the existing support structures and repowering with new turbines.
As a basis of the decision-making regarding the reuse of existing offshore wind farm, the expected utility of each relevant option should be determined based on the associated expected rewards, costs and risks. The optimal concept maximizes the utility of the decision-maker and fulfills the existing constraints and requirements. To facilitate such a quantitative decision-making, models and methods have to be developed. In particular, models are required that enable predictions of (a) the condition and performance of the turbines and support structures and (b) the renumeration, costs and consequences of adverse events. These predictions have to consider (a) the governing uncertainties, (b) the available information from the planning, construction, installation and operating phase, (b) potential repair, retrofitting and strengthening schemes and (c) possible monitoring, inspection and maintenance regimes for the future operating phase.
Over the past years, several models, methods and tools have been developed at the Bundesanstalt für Materialforschung und -prüfung (BAM) to support the structural integrity management of offshore wind turbine substructures. These include:
(a) a prototype for reliability-based, system-wide, adaptive planning of inspections of welded steel structures in offshore wind farms,
(b) a method for monitoring and risk-informed optimization of inspection and maintenance strategies for jacket structures subject to fatigue, and
(c) a probabilistic cost model of inspection and maintenance of welded steel structures in offshore wind farms.
This contribution provides an overview on these works and discusses how they can be adapted and extended to support the decision-making regarding lifetime extensions and repowering of offshore wind farms.
Due to the advantages of Laser Powder Bed Fusion (PBF-LB), i.e., design freedom and the possibility to manufacture parts with filigree structures, and the considerable amount of knowledge available for 316L in its conventional variant, the mechanical behavior, and related microstructure-property relationships of PBF-LB/316L are increasingly subject of research. However, many aspects regarding the - application-relevant - mechanical behavior at high temperatures are not yet fully understood. Here, we present the results of an experimental study on the LCF behavior of PBF-LB/316L featuring a low defect population, which makes this study more microstructure-focused than most of the studies in the literature. The LCF tests were performed between room temperature (RT) and 600 °C. The mechanical response is characterized by strain-life curves, and hysteresis and cyclic deformation curves. The damage and deformation mechanisms are studied with X-ray computed tomography, and optical and electron microscopy. The PBF-LB/M/316L was heat treated at 450 °C for 4 h, and a hot‑rolled (HR) 316L variant with a fully recrystallized equiaxed microstructure was tested as a reference. Besides, selected investigations were performed after a subsequent heat treatment at 900 °C for 1 h. The PBF-LB/316L exhibits higher cyclic stresses than HR/316L for most of the fatigue life, especially at room temperature. At the smallest strain amplitudes, the fatigue lives of PBF-LB/M/316L are markedly shorter than in HR/316L. The main damage mechanisms are multiple cracking at slip bands (RT) and intergranular cracking (600 °C). Neither the melt pool boundaries nor the gas porosity have a significant influence on the LCF damage mechanism. The cyclic stress-strain deformation behavior of PBF-LB/M/316L features an initial hardening followed by a continuous softening. The additional heat treatment at 900 °C for 1 h led to decreased cyclic stresses, and a longer fatigue life.
Die additive Fertigung mittels Schweißverfahren bietet große ökonomische Vorteile für eine ressourceneffiziente Bauteilherstellung. Offene Fragen bezüglich Homogenität, Anisotropie der Schweißgefüge und den damit verbundenen Bauteileigenschaften stehen einer wirtschaftlichen Verarbeitung oftmals im Wege. Finale Bauteilgeometrie und Oberflächengüte erfordern meist komplementäre subtraktive Fertigungsschritte. Werkstoffe für hochbelastbare Komponenten sind oftmals schwer spanbar. In einem Vorhaben der BAM und des ISAF wurde untersucht, wie die Modifikation der AM-Schweißzusätze und das ultraschallunterstützte Fräsen (US) die Zerspanungssituation verbessern. Der vorliegende Artikel stellt wesentliche Zusammenhänge zwischen Legierung, Gefüge und Zerspanung zweier schwer spanbarer Hochleistungslegierungen (FeNi und CoCr) dar. Großes Potenzial zeigte neben dem US die Modifikation mit Zr und Hf bei Zulegierung in das Schweißgut mittels Beschichtung von Massivdrähten bzw. Herstellung von Fülldrähten.
The strength of age-hardenable aluminum alloys is based on the controlled formation of nm-sized precipitates, which represent obstacles to dislocation movement. Transmission electron microscopy (TEM) is generally used to identify precipitate types and orientations and to determine their size. This geometric quantification (e.g., length, diameter) is often performed by manual image analysis, which is very time consuming and sometimes poses reproducibility problems. The present work aims at the digital representation of this characterization method by proposing an automatable digital approach. Based on DF-TEM images of different precipitation states of alloy EN AW-2618A, a modularizable digital workflow is described for the quantitative analysis of precipitate dimensions. The integration of this workflow into a data pipeline concept is also presented. The semantic structuring of data allows data to be shared and reused for other applications and purposes, which enables interoperability.
The determination of residual stress in additively manufactured materials is a challenge, even after decades from the establishment of the basics of residual stress analysis. This is due to the peculiar microstructure of such materials. In fact, researchers have discovered that conventional methods for the determination of RS in materials do not properly work for AM materials. In this tutorial, the basics of RS analysis will be explained, together with the basics of AM manufacturing techniques. The microstructure of the peculiar materials (AM) dealt with here will be elucidated. Successively, the necessary modifications to the conventional approaches to RS analysis will be explained and case studies will be displayed, for the attendant to touch with hands the peculiarities of the approaches. Finally, a few experimental and theoretical tips will be given on dos and don’ts for a correct determination of RS in AM materials.
The experimental determination of residual stress becomes more complicated with increasing complexity of the structures investigated. Unlike the conventional and most of the additive manufacturing (AM) fabrication techniques, laser powder bed fusion (PBF-LB) allows the production of complex structures without any additional manufacturing step. However, due to the extremely localized melting and solidification, internal stress-induced deformation and cracks are often observed. In the best case, significant residual stress is retained in the final structures as a footprint of the internal stress during manufacturing.
Here we report solutions to the most prevalent challenges when dealing with the diffraction-based determination of residual stress in AM structures, in particular the choice of the correct diffraction elastic constants. We show that for Nickel-based alloys, the diffraction elastic constants of AM material significantly deviate from their conventional counterparts. Furthermore, measurement strategies to overcome the hurdles appearing when applying diffraction-based techniques to complex-shaped lattice structures are presented: a) proper sample alignment within the beam, b) the proper determination of the residual stress field in a representative part of the structure (i.e., with an engineering meaning). Beyond the principal stress magnitude, the principal direcions of residual stress are discussed for different geometries and scan strategies, as they are relevent for failure criteria.
We show that the RS in the lattice struts can be considered to be uniaxial and to follow the orientation of the strut, while the RS in the lattice knots is more hydrostatic. Additionally, we show that strain measurements in at least seven independent directions are necessary for the correct estimation of the principal stress directions. The measurement directions should be chosen according to the sample geometry and to an informed choice on the possible strain field (i.e., reflecting the scan strategy).
We finally show that if the most prominent direction is not measured, the error in the calculated stress magnitude increases in such a manner that no reliable assessment of RS state can be made.
The influence of test temperature and frequency on the fatigue life of the alloy EN AW-2618A (2618A) was characterized. The overaged condition (T61 followed by 1000 h/230 °C) was investigated in load-controlled tests with a stress ratio of R = -1 and two test frequencies (0.2 Hz, 20 Hz) at room temperature and at 230°C, respectively. An increase in the test temperature reduces fatigue life, whereby this effect is more pronounced at lower stress amplitudes. Decreasing the test frequency in tests at high temperatures further reduces the service life.
The determination of residual stress in additively manufactured materials is a challenge, even after decades from the establishment of the basics of residual stress analysis. This is due to the peculiar microstructure of such materials. In fact, researchers have discovered that conventional methods for the determination of RS in materials do not properly work for AM materials. In this tutorial, the basics of RS analysis will be explained, together with the basics of AM manufacturing techniques. The microstructure of the peculiar materials (AM) dealt with here will be elucidated. Successively, the necessary modifications to the conventional approaches to RS analysis will be explained and case studies will be displayed, for the attendant to touch with hands the peculiarities of the approaches. Finally, a few experimental and theoretical tips will be given on dos and don’ts for a correct determination of RS in AM materials.
A damaging action of HNO3-55% only occurs in combination with its decomposition into nitrous gases, which can be caused by UV radiation. In a laboratory test, transparent HDPE jerrycans have been exposed to both UV radiation and 55 wt-% nitric acid solution at (41 ± 2)°C, for up to 20 days. For comparison, UV radiant exposure (21 days) and nitric acid exposure (up to 6 weeks) were performed separately, at nearly equal temperatures. The respective damages are compared with FTIR spectroscopy in ATR and HT-gel permeation chromatography (GPC) on a molecular level and with hydraulic internal pressure testing as a component test. For the used jerrycans, relevant oxidation can only be found after the combined exposure. The gradual increase in oxidative damage shows the good reproducibility of the lab exposure. The decomposition of nitric acid into nitrous gases by UV radiation – as well as the jerrycan oxidation – is also observed at lower HNO3 concentration (28 wt- %). Similar results are obtained after outdoor tests. Again, the damage occurs only after combined exposure, in contrast to the exposures to UV only and to HNO3 only, which were conducted in parallel. Outdoor exposures are most readily accepted as they represent possible end-use conditions. However, the reproducibility of these exposures is poor due to the large temporal variations in weather. There are also several safety risks, which is why the number of replicates remains limited. Since the outdoor and lab exposure tests show the same qualitative results, it is appropriate to conduct systematic studies in the laboratory. After 6 days of lab exposure, the oxidation damage is rated as critical, which corresponds to about 1/10 year in Central Europe, according to the UV radiant exposure. It should be noted that this amount can also occur in two sunny weeks.
A common engineering practice is to optimize the geometry of a structure by an iterative process, in which an objective function is minimized by systematically choosing the value of design variables and computing the value of the objective function many times.
However, regarding multiscale problems, this direct numerical approach is not feasible for a number of reasons.
The model has to take into account both the structural and unit cell (UC) scale, because the objective function to be minimized is a global quantity, while the parameters are related to the shape or material parameters of the UC.
In the absence of a clear separation of scales, the direct resolution of both scales in the numerical model leads to a significant increase in computational cost, which makes it impossible to repeatedly evaluate the model during the optimization process.
The main goal of this contribution is to overcome the aforementioned limitations and develop a new efficient computational framework for the optimal design of lattice structures.
To this end, parametric MOR is combined with DD methods.
The change in geometry of each UC within the lattice is modelled by introducing a transformation that maps the reference to the physical domain.
In offline simulations, suitable localized approximation spaces, that account for the change in geometry of the UC and its neighbours, are constructed using the concept of oversampling and random sampling.
These are then integrated into a dual DD framework to derive a robust and scalable solver, i.e. a fast-to-evaluate reduced order model (ROM).
The ROM is validated on the example of optimizing a concrete arc or pre-stressed multi beam structure minimizing its mass.
Optical glasses and glass ceramics are present in many devices often used in our daily routine, such as the mobile phones and tablets. Since the 1960´s with the development of glass lasers, and more recently, within the search for efficient W-LEDs, sensors and solar converters, this class of materials has experienced extreme research progress. In order to tailor a material for such applications, it is very important to understand and characterize optical properties such as refractive index, transmission window, absorption and emission cross sections, quantum yields, etc. These properties can often be tuned by appropriate compositional choice and post-synthesis processing. In this lecture we will discuss the optical properties of glasses and glass ceramics, relevant to that end.
Non-Destructive Testing (NDT) of type 4 composite pressure vessels can be seen as key to better understanding the behaviour of the composite structure and the impact of the manufacturing process on its quality. In this presentation, NDT methods used in the “Trustworthy Hydrogen” project are introduced. A brief discussion of the observed phenomena is provided.
Society is currently confronted with two global challenges, climate change and sustainable development. This reality reverberates amongst the leading nations of the world and is articulated as a priority by the United Nations through the Framework Convention on Climate Change and its seventeen Sustainable Development Goals. In 2016, under the Paris Accord, Mission Innovation, MI, emerged as a global response to climate change and developed eight innovation challenges to mitigate its effect, including Clean Energy Materials, IC6. This innovation challenge focused its efforts on accelerating the development and deployment of clean energy materials by more than a factor of ten through Materials Acceleration Platforms, MAPs – autonomous, self-driving materials laboratories and renewed itself under the current mandate as Materials for Energy, M4E.
Self-driving labs deploy artificial intelligence, robotic automation and high-performance simulation and modeling in a closed loop system of material synthesis and characterization. An international ecosystem for accelerated materials discovery has been established and finds applications in many enabling materials technologies, including nanomaterials. The importance of nanomaterials to catalysis for hydrogen production and carbon dioxide conversion as well as energy storage in batteries is well known. In this work, the international efforts under Materials for Energy will be elaborated including the development of MINERVA - MAP for Intelligent Nanomaterial synthesis Enabled by Robotics for Versatile Applications. MINERVA was specifically built to include the specialized equipment required for the synthesis, characterization and closed-loop optimization of various nano- and advanced materials, ranging from simple inorganic (silica, metal, metal oxide) or polymeric nanoparticles to more complex core-shell architectures and materials with well-defined porosity or surface chemistry. Currently, we are investigating materials for applications in antimicrobial and antibiofouling surface coatings, sensor materials, as well as the reproducible synthesis of reference materials with this platform.
Material Acceleration Platforms (MAPs) represent a transformative approach to the development of resilient and sustainable technology value chains. These platforms can identify candidate chemistries and structures via simulations, and database searches and leverage machine learning-based rapid screening to accelerate the discovery and deployment of novel materials, thereby addressing critical challenges in modern technology sectors.
Incorporating high-fidelity advanced characterization in the early phases of material development is crucial for early de-risking. Advanced characterization techniques, such as X-ray diffraction, advanced electrochemical and spectroscopic techniques provide comprehensive insights into the structural, chemical, and physical properties of materials. Long-term testing further contributes to the de-risking process by evaluating the durability and stability of materials under various environmental and operational conditions. Early identification of potential degradation mechanisms enables the refinement of material compositions and processing methods, ultimately leading to the development of more resilient materials.
Early upscaling attempts are integral to assessing the feasibility of material leads generated through machine learning-based rapid screening to evaluate the scalability of synthesis and processing techniques. This step is critical for identifying potential challenges in manufacturing, such as issues related to reproducibility, yield, and cost-effectiveness. Process design has to be a major part of the MAP-based material design to cope with the increasing share of secondary raw materials in supply chains.
This presentation will briefly summarize possible strategies to address these issues and provide deep-dives on best practices. As the demand for advanced materials continues to grow, MAPs will play an increasingly vital role in driving technological advancements and addressing global challenges.
Übersicht über den Aktuellen Stand der Durchführungsmaßnahmen der EU zur Ökodesign-Richtlinie sowie der delegierten Rechtsakte zum EU Energielabel. Der Fokus liegt auf die Änderungen bzgl. gesetzlicher Anforderungen, die Überwachung durch die Vollzugsbehörden und mögliche diesbezügliche Informationsquellen.
Übersicht über die Umsetzung des EU Green New Deal, des Circular Economy Action Plan sowie der Sustainable Product Initiative. Ebenfalls erfolgt eine Zusammenfassung der Auswirkungen der Änderungen im EU-Produktrecht auf die Revision des geltenden Rechts und das Zusammenspiel zwischen Rechtssetzung und Normung.
Überblick über den Paradigmenwechsel der EU-Rechtsetzung zu Produkten, sowie die Erweiterung des Geltungsbereiches auf Zirkularitätsaspekte (Circular Economy). Der Überblick wird anhand des Beispieles Produktlebensdauer erklärt und die Vorteile eines Zusammenspiels zwischen der Legislative und Normungsarbeit erörtert.
Following the new paradigm of materials development, design and optimization, digitalization is the main goal in materials sciences (MS) which imposes a huge challenge. In this respect, the quality assurance of processes and output data as well as the interoperability between applications following FAIR (findability, accessibility, interoperability, reusability) principles are to be ensured. For storage, processing, and querying of data in contextualized form, Semantic Web Technologies (SWT) are used since they allow for machine-understandable and human-readable knowledge representations needed for data management, retrieval, and (re)use.
The project ‘platform MaterialDigital’ (PMD) is part of an initiative that aims to bring together and support interested parties from both industrial and academic sectors in a sustainable manner in solving digitalization tasks and implementing digital solutions. Therefore, the establishment of a virtual material data space and the systematization of the handling of hierarchical, process-dependent material data are focused. Core points to be dealt with are the development of agreements on data structures and interfaces implemented in distinct software tools and to offer users specific added values in their projects. Furthermore, the platform contributes to a standardized description of data processing methods in materials research. In this respect, selected MSE methods are ontologically represented which are supposed to serve as best practice examples with respect to knowledge representation and the creation of knowledge graphs used for material data.
Accordingly, this presentation shows the efforts taken within the PMD project to store data in accordance with a testing standard compliant ontological representation of a tensile test of metals at room temperature (ISO 6892-1:2019-11). This includes the path from developing an ontology in accordance with the respective standard up to connecting the ontology and data. The semantic connection of the ontology and data leads to interoperability and an enhanced ability of querying. For further enhanced reusability of data and knowledge from synthesis, production, and characterization of materials, the PMD core ontology (PMDco) was developed as mid-level ontology in the field of MSE. The semantic connection of the tensile test ontology (TTO) to the PMDco leads to enhanced expressivity and interoperability.
Moreover, as a best practice example, generation and acquisition of test data semantically connected to the ontology (data mapping) was realized by applying an electronic laboratory notebook (ELN). Corresponding tensile tests were performed by materials science students at university. This enabled a fully digitally integrated experimental procedure that can be transferred to other test series and experiments. In addition to facilitating the acquisition, analysis, processing, and (re)usability of data, this also raises the awareness of students with respect to data structuring and semantic technologies in the sense of education and training. The entire data pipeline is further seamlessly integrable in a laboratory information management system (LIMS). More specifically, the integration of semantic conceptualization and knowledge graphs may become essential parts in LIMS as this would be very beneficial. Therefore, some first approaches of SWT integration in LIMS will also be presented briefly.
The digital era has led to a significant increase in innovation in scientific research across diverse fields and sectors. Evolution of data-driven methodologies lead to a number of paradigm shifts how data, information, and knowledge is produced, understood, and analyzed. High profile paradigm shifts in the field of materials science (MS) include exploitative usage of computational tools, machine learning algorithms, and high-performance computing, which unlock novel avenues for investigating materials. In these presentations, we highlight prototype solutions developed in the context of the Platform MaterialDigital (PMD) project that addresses digitalization challenges. As part of the Material Digital Initiative, the PMD supports the establishment of a virtual materials data space and a systematic handling of hierarchical processes and materials data using a developed ontological framework as high priority work items. In particular, the mid-level ontology PMDco and its augmentation through application-specific ontologies are illustrated. As part of the conclusion, a discussion encompasses the evolutionary path of the ontological framework, taking into account standardization efforts and the integration of modern AI methodologies such as natural language processing (NLP). Moreover, demonstrators illustrated in these presentations highlight: The integration and interconnection of tools, such as digital workflows and ontologies, Semantic integration of diverse data as proof of concept for semantic interoperability, Improved reproducibility in image processing and analysis, and Seamless data acquisition pipelines supported by an ontological framework. In this context, concepts regarding the application of modern research data management tools, such as electronic laboratory notebooks (ELN) and laboratory information management systems (LIMS), are presented and elaborated on. Furthermore, the growing relevance of a standardized adoption of such technologies in the future landscape of digital initiatives is addressed. This is supposed to provide an additional basis for discussion with respect to possible collaborations.
Im Projekt GlasDigital sollen digitale Werkzeuge für die Hochdurchsatzentwicklung neuartiger Glaswerkstoffe erarbeitet werden. Dies soll durch die Kombination robotischer Syntheseverfahren mit selbstlernenden Maschinen und ihre Einbindung in eine Ontologie-basierte digitale Infrastruktur realisiert werden.
Steel tension members with hollow sections are used in applications such as trusses. To ensure the fire resistance, the steel must be prevented from heating up too quickly. A suitable solution is the application of intumescent coatings. However, the approval-based scope of application of these products generally does not include steel tension members with hollow section. This is mainly due to the lack of a corresponding European test and assessment standard. To investigate the characteristics to be considered when testing these structural members and assessing the contribution of the intumescent coating to the fire resistance, several mechanically loaded fire tests were carried out using a special tension test furnace. The fire tests have shown that intumescent coatings can provide a significant improvement in the fire resistance of steel tension members with hollow section. However, this specific application generally places particularly high demands on the performance of the intumescent coating. This is partly due to the four-sided fire exposure and the mainly filigree cross sections, which both result in high section factors. In addition, there is an increased crack formation due to the tensile load. As a result, the thermal protection of the tested intumescent coating on the tension members decreased compared to columns with the same section factor and fire exposure. Consequently, the presence of a mechanical load in the fire test as well as the type of loading affect the thermal protective performance of the intumescent coating and shall be considered.
Glasses stand out by their wide and continuously tunable chemical composition and large variety of unique shaping techniques making them a key component of modern high technologies. Glass development, however, is still often too cost-, time- and energy-intensive. The use of robotic melting systems embedded in an ontology-based digital environment is intended to overcome these problems in future.
For this purpose, a robotic high throughput glass melting system is equipped with novel inline sensors for process monitoring, machine learning (ML)-based, adaptive algorithms for process monitoring and optimization, novel tools for high throughput glass analysis and ML-based algorithms for glass design. This includes software tools for data mining as well as property and process modelling. The presentation provides an overview of how all these tools merge into a digital infrastructure and illustrates their usability using examples.
All infrastructural parts were developed by a consortium consisting of the Fraunhofer ISC in Würzburg, the Friedrich-Schiller-University Jena (OSIM), the Clausthal University of Technology (INW) and the Federal Institute for Materials Research and Testing (BAM, Division Glasses) as part of a joint project of the German research initiative MaterialDigital.
Digital Transformation in Materials Science: Insights From the Platform MaterialDigital (PMD)
(2024)
The digital era has led to a significant increase in innovation in scientific research across diverse fields and sectors. Evolution of data-driven methodologies lead to a number of paradigm shifts how data, information, and knowledge is produced, understood, and analyzed. High profile paradigm shifts in the field of materials science (MS) include exploitative usage of computational tools, machine learning algorithms, and high-performance computing, which unlock novel avenues for investigating materials. In these presentations, we highlight prototype solutions developed in the context of the Platform MaterialDigital (PMD) project that addresses digitalization challenges. As part of the Material Digital Initiative, the PMD supports the establishment of a virtual materials data space and a systematic handling of hierarchical processes and materials data using a developed ontological framework as high priority work items. In particular, the mid-level ontology PMDco and its augmentation through application-specific ontologies are illustrated. As part of the conclusion, a discussion encompasses the evolutionary path of the ontological framework, taking into account standardization efforts and the integration of modern AI methodologies such as natural language processing (NLP). Moreover, demonstrators illustrated in these presentations highlight: The integration and interconnection of tools, such as digital workflows and ontologies, Semantic integration of diverse data as proof of concept for semantic interoperability, Improved reproducibility in image processing and analysis, and Seamless data acquisition pipelines supported by an ontological framework. In this context, concepts regarding the application of modern research data management tools, such as electronic laboratory notebooks (ELN) and laboratory information management systems (LIMS), are presented and elaborated on. Furthermore, the growing relevance of a standardized adoption of such technologies in the future landscape of digital initiatives is addressed. This is supposed to provide an additional basis for discussion with respect to possible collaborations.
Gläser zeichnen sich durch eine breite und kontinuierlich abstimmbare chemische Zusammensetzung sowie einzigartige Formgebungstechniken aus, was sie oft zur Schlüsselkomponente moderner Hochtechnologien macht. Die Glasentwicklung ist jedoch oft noch zu kosten-, zeit- und energieintensiv. Der Einsatz von robotergestützten Schmelzsystemen, eingebettet in eine Ontologie-basierte digitale Umgebung, soll diese Probleme in Zukunft überwinden. Im Rahmen der BMBF Forschungsinitiative MaterialDigital unternimmt das Verbundprojekt GlasDigital „Datengetriebener Workflow für die beschleunigte Entwicklung von Glas“ erste Schritte in diese Richtung. Das Projektkonsortium, an dem das Fraunhofer ISC in Würzburg, die Friedrich-Schiller-Universität Jena (OSIM), die Technische Universität Clausthal (INW) und die Bundesanstalt für Materialforschung und -prüfung (BAM, Fachgruppe Glas) beteiligt sind, will alle wesentlichen Basiskomponenten für eine beschleunigte datengetriebene Glasentwicklung zusammenführen. Zu diesem Zweck wird ein robotergestütztes Hochdurchsatz-Glasschmelzsystem mit neuartigen Inline-Sensoren zur Prozessüberwachung, auf maschinellem Lernen (ML) basierenden adaptiven Algorithmen zur Prozessüberwachung und -optimierung, neuartigen Werkzeugen für die Hochdurchsatz-Glasanalyse sowie ML-basierten Algorithmen zum Glasdesign, Data Mining sowie Eigenschafts- und Prozessmodellierung ausgestattet. Der Vortrag gibt einen Überblick darüber, wie all diese Komponenten miteinander verzahnt sind, und veranschaulicht ihre Nutzbarkeit anhand einiger Beispiele.
Age-hardenable aluminum alloys undergo precise heat treatments to yield nanometer-sized precipitates that increase their strength and durability by hindering the dislocation mobility. Tensile tests provide mechanical properties, while microstructure evaluation relies on transmission electron microscopy (TEM), specifically the use of dark-field TEM images for precise dimensional analysis of the precipitates. However, this manual process is time consuming, skill dependent, and prone to errors and reproducibility issues.
Our primary goal is to digitally represent these processes while adhering to FAIR principles. Ontologies play a critical role in facilitating semantic annotation of (meta)data and form the basis for advanced data management. Publishing raw data, digital workflows, and ontologies ensures reproducibility.
This work introduces innovative solutions to traditional bottlenecks and offers new perspectives on digitalization challenges in materials science. We support advanced data management by leveraging knowledge graphs and foster collaborative and open data ecosystems that potentially revolutionize materials research and discovery.
Spectroscopy Lectures
(2024)
As a guest professor of FUNGLASS, I delivered 3 lectures on spectroscopy to the Graduate School Program, the postdoctoral fellows and other researchers: 1) Introduction to spectroscopy applied to solid state materials (with focus on glass and glass ceramics); 2) Vibrational spectroscopy (Infrared and Raman); 3) Electron Paramagnetic Resonance
lasses stand out by their wide and continuously tunable chemical composition and large variety of unique shaping techniques making them a key component of modern high technologies. Glass development, however, is still often too cost-, time- and energy-intensive. The use of robotic melting systems embedded in an ontology-based digital environment is intended to overcome these problems in future. As part of the German research initiative MaterialDigital, the joint project GlasDigital takes first steps in this direction. The project consortium involves the Fraunhofer ISC in Würzburg, the Friedrich Schiller University Jena (OSIM), the Clausthal University of Technology (INW), and the Federal Institute for Materials Research and Testing (BAM, Division Glasses) and aims to combine all main basic components required for accelerated data driven glass development. For this purpose, a robotic high throughput glass melting system is equipped with novel inline sensors for process monitoring, machine learning (ML)-based, adaptive algorithms for process monitoring and optimization, novel tools for high throughput glass analysis and ML-based algorithms for glass design, including software tools for data mining as well as property and process modelling. The talk gives an overview how all these tools are interconnected and illustrates their usability with some examples.
GlasDigital
(2023)
When it comes to dental treatments, success is not only measured by attained functionality but, to a large extent, the associated aesthetics. This can become challenging for certain restorations and implants due to the complex optical characteristic of a tooth, which reflects, absorbs, diffuses, transmits, and even emits light. Thus, to get acceptable aesthetic results, favourable shade matching of ceramic restorations and implants should be achieved by strict control of optical response, which translates into a materials design question. Optical response is affected by several factors such as the composition, crystalline content, porosity, additives, grain size and the angle of incidence of light on the dental ceramics. The properties to be characterized are colour (and its stability), translucency, opalescence, refractive index, and fluorescence. Several techniques can be applied for the characterization of these properties and in this presentation, an overview will be given. Moreover, particular emphasis will be given on the capacitation of less familiarized public to UV-Vis absorption and photoluminescence (PLE) spectroscopies that are versatile and widely employed for functional and structural characterization of glasses and glass ceramic materials.
Suitable material solutions are of key importance in designing and producing components for engineering systems – either for functional or structural applications. Materials data are generated, transferred, and introduced at each step along the complete life cycle of a component. A reliable
materials data space is therefore crucial in the digital transformation of an industrial branch.
A great challenge in establishing a materials data space lies in the complexity and diversity of materials science and engineering. It must be able to handle data from different knowledge areas over several magnitudes of length scale.
The Platform MaterialDigital (PMD) is expected to network a large number of repositories of materials data, allowing the direct contact of different stakeholders as materials producers, testing labs, designers and end users. Following the FAIR principles, it will promote the semantic
interoperability across the frontiers of materials classes. In the frame of a large joint initiative, PMD works intensively together with currently near 20 research consortia in promoting this exchange (www.material-digital.de).
In this presentation we will describe the status of our Platform MaterialDigital. We will also present in more detail the activities of GlasDigital, one of the joint projects mentioned above dealing with the digitalization of glass design and manufacturing.
(https://www.bam.de/Content/EN/Projects/GlasDigital/glasdigital.html)
Glasses stand out by their wide and continuously tunable chemical composition and large variety of unique shaping techniques making them a key component of modern high technologies. Glass development, however, is still often too cost-, time- and energy-intensive. The use of robotic melting systems embedded in an ontology-based digital environment is intended to overcome these problems in future. As part of the German research initiative MaterialDigital, the joint project GlasDigital takes first steps in this direction. The project consortium involves the Fraunhofer ISC in Würzburg, the Friedrich Schiller University Jena (OSIM), the Clausthal University of Technology (INW), and the Federal Institute for Materials Research and Testing (BAM, Division Glasses) and aims to combine all main basic components required for accelerated data driven glass development. For this purpose, a robotic high throughput glass melting system is equipped with novel inline sensors for process monitoring, machine learning (ML)-based, adaptive algorithms for process monitoring and optimization, novel tools for high throughput glass analysis and ML-based algorithms for glass design, including software tools for data mining as well as property and process modelling. The talk gives an overview how all these tools are interconnected and illustrates their usability with some examples.