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In this lecture, the safety assessment of hydrogen gas storage systems is presented using the example of composite pressure vessels. The main element is a probabilistic approval approach based on five steps. Firstly, the testing and evaluation of properties at the beginning of life are shown. Secondly, methods for artificial aging and the effect on the residual strength are presented. Thirdly, testing against dedicated accidents is introduced. Fourthly, effects on the surveillance of production quality are discussed. Finally, degradation and the end of life are estimated. Background information and examples are given for each step. The assessment presented is a method applicable for many safety-related systems.
In this lecture, the safety assessment of hydrogen gas storage systems is presented using the example of composite pressure vessels. The main element is a probabilistic approval approach based on five steps. Firstly, the testing and evaluation of properties at the beginning of life are shown. Secondly, methods for artificial aging and the effect on the residual strength are presented. Thirdly, testing against dedicated accidents is introduced. Fourthly, effects on the surveillance of production quality are discussed. Finally, degradation and the end of life are estimated. Background information and examples are given for each step. The assessment presented is a method applicable for many safety-related systems.
In this lecture, the safety assessment of hydrogen gas storage systems is presented using the example of composite pressure vessels. The main element is a probabilistic approval approach based on five steps. Firstly, the testing and evaluation of properties at the beginning of life are shown. Secondly, methods for artificial aging and the effect on the residual strength are presented. Thirdly, testing against dedicated accidents is introduced. Fourthly, effects on the surveillance of production quality are discussed. Finally, degradation and the end of life are estimated. Background information and examples are given for each step. The assessment presented is a method applicable for many safety-related systems.
Manipulating ceramic powder compacts and ceramic suspensions (slurries) within their volume with light requires a minimum transparency of the materials. Compared to polymers and metals, ceramic materials are unique as they offer a wide electronic band gap and thus a wide optical window of transparency. The optical window typically ranges from below 0.3 µm up to 5µm wavelength. Hence, to penetrate with laser light into the volume of a ceramic powder compound its light scattering properties need to be investigated and tailored. In the present study we introduce the physical background and material development strategies to apply two-photon-polymerization (2PP) for the additive manufacture of filigree structures within the volume of ceramic slurries.
In this lecture, the safety assessment of hydrogen gas storage systems is presented using the example of composite pressure vessels. The main element is a probabilistic approval approach based on five steps. Firstly, the testing and evaluation of properties at the beginning of life are shown. Secondly, methods for artificial aging and the effect on the residual strength are presented. Thirdly, testing against dedicated accidents is introduced. Fourthly, effects on the surveillance of production quality are discussed. Finally, degradation and the end of life are estimated. Background information and examples are given for each step. The assessment presented is a method applicable for many safety-related systems.
In this presentation safety aspects of hydrogen transportation are discussed. At first, the regulatory background and level of safety are presented. In the second part, the modelling of consequence due to sudden rupture of pressure receptacles is explained. Finally, the results are used to define a limit for consequence to enable an acceptable and safe transport of hydrogen.
In order to be able to manipulate ceramic powder compacts and ceramic suspensions (slurries) within their volume with light, a minimum transparency of the materials is required. Compared to polymers and metals, ceramic materials are characterized by the fact that they have a wide electronic band gap and therefore a wide optical window of transparency. The optical window generally ranges from less than 0.3 µm to 5 µm wavelength. In order to focus light into the volume of a ceramic powder compact, its light scattering properties must therefore be tailored. In this study, we present the physical background and material development strategies for the application of two-photon polymerization (2PP) and selective volumetric sintering for the additive manufacturing of structures in the volume of ceramic slips and green compacts.
The Binder Jetting BJ process is one of the most versatile additive manufacturing technologies in use. In this process a binder is locally jetted into a powder bed for the consolidation of a 3D structure, layer by layer. Basically, all materials which can be provided as a flowable powder and, thus, spreadable to a thin layer, can be processed. Metals, ceramics and polymers are processable, but also materials from nature, such as sand, wood sawdust and insect frass. Moreover, the BJ technology is adapted to large building volumes of some cubic meters easily. Besides these striking advantages, the manufacture of ceramic parts by BJ is still challenging, as the packing density of the powder bed is generally too low and the particle size of a flowable powder too large for a successful densification of printed parts in a subsequent sintering step to an advanced ceramic product. After an introduction of binder jetting in general and highlighting some examples, strategies for obtaining dense ceramic parts by BJ will be introduced.
Advances in ultrafast laser manufacturing: nanostructures, thin films, and scaling perspectives
(2024)
Advanced ultrafast laser technology is a rapidly growing field that currently enables many new industrial and scientific applications. During the last decades, this has been significantly driven by the availability of high-repetition-rate laser sources and novel beam delivery concepts. At the laser side, Moore’s law equally manifests for ultrafast laser technologies, since the average output power of such lasers doubles approximately every two years. This development is mainly driven by the increase of the pulse repetition rates of energetic laser pulses, currently enforcing the development of smart beam control and novel scanning strategies for preventing heat-accumulation and plasma-shielding effects during laser-based materials processing. This keynote presentation addresses the advantages, recent developments, and perspectives of laser processing with ultrashort laser pulses. A special focus is laid on the tailored structuring of thin films as well as the manufacturing and probing of sub-diffraction surface nanostructures – an ongoing race to extreme scales. Current limitations are identified and an outlook to future scaling perspectives will be provided.
Protection against terroristic or accidental scenarios in industrial settings requires suitable designs of structures to resist blast loads. Field testing as well as finite element simulations are among the techniques available to engineers in the understanding of the structural behavior against blast loading.
As blast testing of complex scenarios can be very resource intensive, tests are generally performed for simplified scenarios. Numerical tools can be used to model these scenarios to get better insight into blast loading, structural response, and the resulting damage to the structure. During the next steps, the simplified scenario is successively modified in numerical simulations to incorporate complexities that cannot be covered in blast testing experiments. One of the conditions for this approach to work is that the original simplified numerical simulation is valid. The scopes and challenges encountered in such a validation are the focus of this presentation/article.
A relatively ‘simple’ field test of a horizontal reinforced concrete (RC) slab subjected to blast loading is taken as an example for validation of the performance of numerical tools. The blast test incorporated various measurement techniques to quantify the blast load as well as the behavior of the RC slab. Blast load was measured using flush mounted piezoelectric pressure gauges, whereas acceleration sensors and fiber-optic sensor cables were used to characterize the dynamic behavior of the slab under blast loading. Additionally, damage characteristics were ascertained also using fiber optic sensing. The application of such measurement techniques, along with different numerical software available for the analysis of the scenario in question, demonstrate the scope of our contribution.
When it comes to the challenges, it begins with proper instrumentation of the test specimen followed by the data processing. For numerical modelling, geometric conditions with appropriate boundary constraints, physical conditions such as the configuration of the rebars, as well as material parameters add to this challenge. The issues of choosing appropriate material models and comparison of results with multiple software tools will be discussed. This discussion forms the basis for a coherent approach to technical-safety assessment of blast effects on structures in its broader sense.
The presentation demonstrates an application of multi-scale optical imaging methods such as spectroscopic imaging ellipsometry and white light interference microscopy for the investigation of wide-bandgap semiconductors for power electronics. The capabilities of these methods for the development of new reference calibration samples for scanning microwave microscopes (SMM) and conductive atomic force microscopes (C AFM) are discussed.
Unlike conventional alloys, which typically consist of one main element, high-entropy alloys (HEAs) contain five or more principal elements, which broaden chemical complexity and with it a realm of synergistic mechanisms. The AlMo0.5NbTa0.5TiZr HEA initiated a subclass of Al-containing refractory (r)HEAs that has recently drawn attention [2]. The alloy has a superalloy-resembling B2/bcc nanostructure, which inspired its name refractory high entropy superalloy (RSA). With high-temperature (HT) compressive strengths beyond conventional Ni-based superalloys, this nanostructure could be used for improved HT structural applications. However, in the application-relevant HT regime the Al-Zr-rich B2 phase decomposes to form a hexagonal Al-Zr-based intermetallic (Al4-xZr5; x: 0..1) [3,4]. This work explores the fascinating yet fatal micromechanisms associated to this phase transformation, in the context of creep, annealing and oxidation experiments performed between 800 and 1200 °C.
The material was produced by arc-melting and heat treatment in argon, which lead to grain boundaries decorated with up to 7%. Interrupted constant-load creep tests were performed under vacuum (at 10-4 Pa), at 900–1100 °C with external tensile stresses of 30–120 MPa. Oxidation experiments were separately conducted for 24 hours at 800 and 1000 °C in both dry (21% O2 + 79% N2) and humid (8% O2 + 74% N2 + 18% H2O) air. After the experiments, the samples were characterized by X-ray diffraction, scanning electron microscopy and transmission electron microscopy to reveal degradation mechanisms. Crystallographic texture, orientation relationships and stabilization of an oxygen-containing iso structure (Al4-xZr5(Ox-y); y: 0..x) of the Al-Zr-rich intermetallic are found and discussed.
Sunlight is an almost unavoidable environmental cue and plays a fundamental role in the biology of pro- and eukaryotic organisms. To cope with sunlight-associated stresses e.g., high temperatures, UV radiation with associated DNA damage, accumulation of reactive oxygen species (ROS), desiccation and osmotic stresses, it is important for organisms to accurately sense and respond to changes in light. The benefits of light are obvious for green organisms such as cyanobacteria, algae and plants which use light as an energy source (photosynthesis). Less apparent are other light-dependent processes such as light-driven DNA repair by photolyases (photoreactivation) or ion pumping by microbial opsins. Fungi that can share light-flooded habitats with phototrophs may profit from their excess photosynthetic products. Rock-inhabiting Dothideomycetes and Eurotiomycetes including Knufia petricola possess many proteins for absorbing UV/blue, green, red and far-red light, produce the black 1,8 dihydroxynaphthalene (DHN) melanin and orange-red carotenoids, and may live in multispecies biofilms. Here, we are addressing the question to which extent constitutive pigment formation (melanin and carotenoids) and responses mediated by the stress-activated mitogen-activated protein (MAP) kinase contribute to the observed light (UV-B) tolerance of K. petricola.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
Driven by recent technological advances and the need for improved viral diagnostic applications, mass spectrometry-based proteomics comes into play for detecting viral pathogens accurately and efficiently. However, the lack of specific algorithms and software tools presents a major bottleneck for analyzing data from host-virus samples. For example, accurate species- and strain-level classification of a priori unidentified organisms remains a very challenging task in the setting of large search databases. Another prominent issue is that many existing solutions suffer from the protein inference issue, aggravated because many homologous proteins are present across multiple species. One of the contributing factors is that existing bioinformatic algorithms have been developed mainly for single-species proteomics applications for model organisms or human samples. In addition, a statistically sound framework was lacking to accurately assign peptide identifications to viral taxa. In this presentation, an overview is given on current bioinformatics developments that aim to overcome the above-mentioned issues using algorithmic and statistical methods. The presented methods and software tools aim to provide tailored solutions for both discovery-driven and targeted proteomics for viral diagnostics and taxonomic sample profiling. Furthermore, an outlook is provided on how the bioinformatic developments might serve as a generic toolbox, which can be transferred to other research questions, such as metaproteomics for profiling microbiomes and identifying bacterial pathogens.
An alternative method for lithium isotope analysis by using high-resolution atomic absorption spectrometry (HR-CS-AAS) is proposed herein. This method is based on monitoring the isotope shift of approximately 15 pm for the electronic transition 22P←22S at around the wavelength of 670.8 nm, which can be measured by state-of-the-art HR-CS-AAS. Isotope analysis can be used for (i) the traceable determination of Li concentration and (ii) isotope amount ratio analysis based on a combination of HR-CS-AAS and spectral data analysis by machine learning (ML).
In the first case, the Li spectra are described as the linear superposition of the contributions of the respective isotopes, each consisting of a spin-orbit doublet, which can be expressed as Gaussian components with constant spectral position and width and different relative intensity, reflecting the isotope ratio in the sample. Precision was further improved by using lanthanum as internal spectral standard. The procedure has been validated using human serum-certified reference materials. The results are metrologically comparable and compatible with the certified values.
In the second case, for isotope amount ratio analysis, a scalable tree boosting ML algorithm (XGBoost) was employed and calibrated using a set of samples with 6Li isotope amount fractions ranging from 0.06 to 0.99 mol mol−1. The training ML model was validated with certified reference materials. The procedure was applied to the isotope amount ratio determination of a set of stock chemicals and a BAM candidate reference material NMC111 (LiNi1/3Mn1/3Co1/3O2), a Li-battery cathode material. These determinations were compared with those obtained by MC-ICP-MS and found to be metrologically comparable and compatible. The residual bias was −1.8‰, and the precision obtained ranged from 1.9‰ to 6.2‰. This precision was sufficient to resolve naturally occurring variations. The NMC111 cathode candidate reference material was analyzed using high-resolution continuum source atomic absorption spectrometry with and without matrix purification to assess its suitability for technical applications. The results obtained were metrologically compatible with each other.
Glow discharge optical emission spectroscopy (GD-OES) is a technique for the analysis of solids such as metals, semiconductors, and ceramics. A low-pressure glow discharge plasma is applied in this system, which ‘sputters’ and promotes the sample atoms to a higher energy state. When the atoms return to their ground state, they emit light with characteristic wavelengths, which a spectrometer can detect. Thus, GD-OES combines the advantages of ICP-OES with solid sampling techniques, which enables it to determine the bulk elemental composition and depth profiles. However, direct solid sampling methods such as glow-discharge spectroscopy require reference materials for calibration due to the strong matrix effect.
Reference materials are essential when the accuracy and reliability of measurement results need to be guaranteed to generate confidence in the analysis. These materials are frequently used to determine measurement uncertainty, validate methods, suitability testing, and quality assurance. In addition, they guarantee that measurement results can be compared to recognized reference values. Unfortunately, the availability of certified reference materials suited to calibrate all elements in different matrix materials is limited. Therefore various calibration strategies and the preparation of traceable matrix-matched calibration standards will be discussed.
Machine learning is an essential component of the growing field of data science. Through statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. Therefore, it was tried in our work to combine GD-OES with machine learning strategies to establish a new and robust calibration model, which can be used to identify the elemental composition and concentration of metals from a single spectrum. For this purpose, copper reference materials from different manufacturers, which contain various impurity elements, were investigated using GD-OES. The obtained spectra information are evaluated with different algorithms (e.g., gradient boosting and artificial neural networks), and the results are compared and discussed in detail.
In view of the increasing digitization of research and the use of data-intensive measurement and analysis methods, research institutions and their staff are faced with the challenge of documenting a constantly growing volume of data in a comprehensible manner, archiving them for the long term, and making them available for discovery and re-use by others in accordance with the FAIR principles. At BAM, we aim to facilitate the integration of research data management (RDM) strategies during the whole research cycle from the creation and standardized description of materials datasets to their publication in open repositories. To this end, we present the BAM Data Store, a central system for internal RDM that fulfills the heterogenous demands of materials science and engineering labs. The BAM Data Store is based on openBIS, an open-source software developed by the ETH Zurich that has originally been created for life science laboratories but that has since been deployed in a variety of research domains. The software offers a browser-based user interface for the digital representation of lab inventory entities (e.g., samples, chemicals, instruments, and protocols) and an electronic lab notebook for the standardized documentation of experiments and analyses.
To investigate whether openBIS is a suitable framework for the BAM Data Store, we carried out a pilot phase during which five research groups with employees from 16 different BAM divisions were introduced to the software. The pilot groups were chosen to represent a diverse array of domain use cases and RDM requirements (e.g., small vs big data volume, heterogenous vs structured data types) as well as varying levels of prior IT knowledge on the users’ side.
Overall, the results of the pilot phase are promising: While the creation of custom data structures and metadata schemas can be time-intensive and requires the involvement of domain experts, the system offers specific benefits in the form of a simplified documentation and automation of research processes, as well as constituting a basis for data-driven analysis. In this way, heterogeneous research workflows in various materials science research domains could be implemented, from the synthesis and characterization of nanomaterials to the monitoring of engineering structures. In addition to the technical deployment and the development of domain-specific metadata standards, the pilot phase also highlighted the need for suitable institutional infrastructures, processes, and role models. An institute-wide rollout of the BAM Data Store is currently being planned.