Chemie und Prozesstechnik
Filtern
Dokumenttyp
- Zeitschriftenartikel (1171)
- Vortrag (1069)
- Posterpräsentation (442)
- Beitrag zu einem Tagungsband (174)
- Forschungsdatensatz (55)
- Sonstiges (37)
- Buchkapitel (24)
- Forschungsbericht (23)
- Dissertation (15)
- Beitrag zu einem Sammelband (13)
- Preprint (6)
- Handbuch (5)
- Video (5)
- Zeitschriftenheft (Herausgeberschaft für das komplette Heft) (3)
- Sammelband (Herausgeberschaft für den kompletten Band) (2)
- Tagungsband (Herausgeberschaft für den kompletten Band) (1)
- Newsletter (1)
Sprache
- Englisch (3046) (entfernen)
Schlagworte
- Nanoparticles (114)
- Fluorescence (112)
- Concrete (89)
- LIBS (81)
- Mechanochemistry (72)
- Quantum yield (71)
- Ultrasound (71)
- SAXS (69)
- Non-destructive testing (68)
- XPS (61)
Organisationseinheit der BAM
- 1 Analytische Chemie; Referenzmaterialien (1085)
- 6 Materialchemie (973)
- 8 Zerstörungsfreie Prüfung (774)
- 6.1 Oberflächen- und Dünnschichtanalyse (348)
- 6.3 Strukturanalytik (322)
- 1.1 Anorganische Spurenanalytik (277)
- 8.2 Zerstörungsfreie Prüfmethoden für das Bauwesen (232)
- 8.0 Abteilungsleitung und andere (224)
- 1.2 Biophotonik (210)
- 4 Material und Umwelt (186)
Paper des Monats
- ja (26)
Nanoparticles with novel physico-chemical properties have an impact on various scientific disciplines, including medical diagnostics, energy conversion, catalysis, and solid-state lighting. Here, I present examples from my previous work on organic and inorganic nanoscale systems, such as superparamagnetic iron oxide nanoparticles (SPIONs) for blood platelet labeling and magnetic copper-doped bioactive glasses for bone cancer therapy. Additionally, I provide a first insight into my recently started Ph.D. project focusing on bichromophoric organic fluorophores exhibiting Aggregation-Induced Dual-Emission (AIDE) and their integration into nanostructures for water-dispersible nanoscale reporters and nanosensors.
Safety is a key parameter for the design and construction of buildings. The most widely used construction material to date is concrete that consists of about 15-20 wt.-% cement, which is responsible for the main concrete properties, i.e., strength and durability. Despite many standards regulating the quality of cement, at present, there exists no viable method to track the cement life cycle “from cradle to grave”. This led to an increasing interest in simple and robust methods for studying the processes and changes occurring during the life cycle of cement. In this context, we explored the applicability of fluorescence measurements which can be performed with relatively inexpensive and miniaturized instrumentation yet require robust optical probes which survive the harsh cement environment. Therefore, we developed a platform of lanthanide-based upconversion nanoparticles (UCNPs), consisting of a NaYF4 matrix doped with Yb3+ and Er3+ with sizes between 20 nm and 55 nm, which show characteristic multi-color emission patterns, composed of narrow bands of varying intensity in the ultraviolet, visible, near-infrared, and short-wave spectral region and examined their potential for cement probing and the non-invasive monitoring of the hydration processes occurring during cement formation.
UCNPs of different size and chemical composition were synthesized via a thermal decomposition approach under inert conditions. The tailor-made design of different emission patterns was achieved by tuning particle size and morphology, material composition, and particle surface chemistry in upscaleable syntheses. For cement probing, different types of UCNPs were added to cement and the evolution of the UCNP emission pattern was used to probe in-situ changes of physico-chemical parameters in the cementitious environment during hydration, utilizing a simple and portable custom-designed optical setup. The observed changes in the UCNP emission patterns are characteristic for a given particle size, surface chemistry, and cement composition. In addition to fluorescence measurements, conventional isothermal heat flow calorimetry was used to study the influence of UCNP addition on cement hydration kinetics. Subsequently, both sets of measurements were correlated. Our results underline the potential of our optical approach ad UCNPs for the non-invasive probing of cementitious systems and cement hydration. This can be also exploited for cutting-edge applications of construction materials such as 3D concrete printing.
Knowledge representation in the materials science and engineering (MSE) domain is a vast and multi-faceted challenge: Overlap, ambiguity, and inconsistency in terminology are common. Invariant and variant knowledge are difficult to align cross-domain. Generic top-level semantic terminology often is too abstract, while MSE domain terminology often is too specific.
In this presentation, an approach how to maintain a comprehensive and intuitive MSE-centric terminology composing a mid-level ontology–the PMD core ontology (PMDco)–via MSE community-based curation procedures is shown.
The PMDco is designed in direct support of the FAIR principles to address immediate needs of the global experts community and their requirements. The illustrated findings show how the PMDco bridges semantic gaps between high-level, MSE-specific, and other science domain semantics, how the PMDco lowers development and integration thresholds, and how to fuel it from real-world data sources ranging from manually conducted experiments and simulations as well as continuously automated industrial applications.
The PMD Core Ontology (PMDco) is a comprehensive set of building blocks produced via consensus building. The ontological building blocks provide a framework representing knowledge about fundamental concepts used in Materials Science and Engineering (MSE) today. The PMDco is a mid-level ontology that establishes connections between narrower MSE application ontologies and domain neutral concepts used in already established broader (top-level) ontologies. The primary goal of the PMDco design is to enable interoperability between various other MSE-related ontologies and other common ontologies.
PMDco’s class structure is both comprehensive and extensible, rendering it an efficient tool to structure MSE knowledge. The PMDco serves as a semantic middle-layer unifying common MSE concepts via semantic mapping to other semantic representations using well-known key terms used in the MSE domain. The PMDco enables straight-forward documentation and tracking of science data generation and in consequence enables high-quality FAIR data that allows for precise reproducibility of scientific experiments.
The design of PMDco is based on the W3C Provenance Ontology (PROV-O), which provides a standard framework for capturing the production, derivation, and attribution of resources. Via this foundation, the PMDco enables the integration of data from various data origins and the representation of complex workflows.
In summary, the PMDco is a valuable advancement for researchers and practitioners in MSE domains. It provides a common MSE vocabulary to represent and share knowledge, allowing for efficient collaboration and promoting interoperability between diverse domains. Its design allows for the systematic integration of data and metadata, enabling seamless tracing of science data. Overall, the PMDco is a crucial step towards a unified and comprehensive understanding of the MSE domain in general.
The present work is part of the AIFRI project (Artificial Intelligence For Rail Inspection), where we and our project partners train a neural network for defect detection and classification. Our goal at BAM is to generate artificial ultrasound and eddy current training data for the A.I. This paper has an exploratory nature, where we focus on the simulation of eddy current signals for head check cracks, one of the most important rail surface defects. The goal of this paper is twofold. On the one hand, we present our general simulation setup. This includes geometric models for head check cracks with features like branching and direction change, a model for the HC10 rail testing probe, and the configuration of the Faraday simulation software.
On the other hand, we use the Faraday software to simulate eddy current testing signals with a strong focus on the influence of the damage depth on the signal, while differentiating between different crack geometries. Here, we observe an early saturation effect of the test signal at a damage depth of 2 mm (at a crack angle of 25◦ to the surface). That is about 2 mm earlier than we would expect from measurements at a crack angle of 90◦. This behavior will be investigated further in a future paper. Finally, we interpolate the simulated signals in a two-step curve fitting process. With these interpolations we may generate eddy current test signals for any damage depth within the simulated range.
The present work is part of the AIFRI project (Artificial Intelligence For Rail Inspection), where we and our project partners train a neural network for defect detection and classification. Our goal at BAM is to generate artificial ultrasound and eddy current training data for the A.I. This paper has an exploratory nature, where we focus on the simulation of eddy current signals for head check cracks, one of the most important rail surface defects. The goal of this paper is twofold. On the one hand, we present our general simulation setup. This includes geometric models for head check cracks with features like branching and direction change, a model for the HC10 rail testing probe, and the configuration of the Faraday simulation software.
On the other hand, we use the Faraday software to simulate eddy current testing signals with a strong focus on the influence of the damage depth on the signal, while differentiating between different crack geometries. Here, we observe an early saturation effect of the test signal at a damage depth of 2 mm (at a crack angle of 25◦ to the surface). That is about 2 mm earlier than we would expect from measurements at a crack angle of 90◦. This behavior will be investigated further in a future paper. Finally, we interpolate the simulated signals in a two-step curve fitting process. With these interpolations we may generate eddy current test signals for any damage depth within the simulated range.
Iron Oxide Nanocubes as a New Certified Reference Material for Nanoparticle Size Measurements
(2023)
The rational design and increasing industrial use of nanomaterials require a reliable characterization of their physicochemical key properties like size, size distribution, shape, and surface chemistry. This calls for nanoscale reference materials (nanoRMs) for the validation and standardization of commonly used characterization methods closely matching real-world nonspherical nano-objects. This encouraged us to develop a nonspherical nanoRM of very small size consisting of 8 nm iron oxide nanocubes (BAM-N012) to complement spherical gold, silica, and polymer nanoRMs. In the following, the development and production of this nanoRM are highlighted including the characterization by transmission electron microscopy (TEM) and small-angle X-ray scattering (SAXS) as complementary methods for size and shape parameters, homogeneity and stability studies, and calculation of a complete uncertainty budget of the size features. The determination of the nanocubes’ edge length by TEM and SAXS allows a method comparison. In addition, SAXS measurements can also provide the mean particle number density and the mass concentration. The certified size parameters, area equivalent circular diameter and square edge length, determined by TEM with a relative expanded uncertainty below 9%, are metrologically traceable to a natural constant for length, the very precisely known (111) lattice spacing of silicon. Cubic BAM-N012 qualifies as a certified nanoRM for estimating the precision and trueness, validation, and quality assurance of particle size and shape measurements with electron microscopy and SAXS as well as other sizing methods suitable for nanomaterials. The production of this new iron oxide nanocube RM presents an important achievement for the nanomaterial community, nanomaterial manufacturers, and regulators.
Non-target (NT) mass spectrometry techniques play a crucial role in metabolomics applications, particularly in areas such as environmental safety. Soft ionization methods, such as electrospray ionization (ESI), are commonly employed due to their ability to generate spectra containing molecular ions, aiding in the identification of unknown peaks. However, ESI may fail to ionize certain compounds effectively, resulting in their absence in NT approaches. In contrast, hard ionization methods like electron impact (EI) can ionize a wide range of compounds but often lack information about the molecular ion. Although extensive databases of EI spectra exist, their nominal mass resolution (NR) limits their utility for modern high-resolution (HR) EI mass spectrometers. Here, the often-applied conversion of HR spectra to nominal mass leads to the loss of unique characteristics, i.e. by grouping distinct masses into more ambiguous nominal masses.
Our study aims to (i) quantify the negative impact of such a binning approach and (ii) develop a machine learning (ML) tool capable of enhancing existing nominal mass spectral libraries. In the initial phase, we employed the RECETOX Exposome HR-[EI+]-MS library to assess the influence of HR spectra on identification. We compared the dot product of each spectrum against all others, utilizing bin sizes of 0.001 and 1 Da. The difference in dot product between the second-best candidate and the query spectrum (the best candidate, with a score of 1) was calculated for both HR and nominal mass spectra. Subsequently, we explored the application of ML techniques to predict HR spectra from nominal mass spectra using the before mentioned dataset.
Preliminary findings demonstrate the potential of high-resolution spectral libraries. As anticipated, HR spectra consistently exhibited lower similarity scores for the second candidate. This observation likely stems from the high redundancy and resulting ambiguity associated with nominal masses. Even after eliminating spectra containing multiple HR masses mapped to the same nominal mass, the aforementioned trend persisted. Furthermore, initial investigations into ML have revealed its ability to predict up to 40% of HR masses within a 10 mDa precision window.
HR-CS-GFMAS turned out to be a sensitive, fast and highly selective tool for fluorine determination. The method is based on the in situ formation of diatomic gallium-mono fluoride (GaF) at a temperature of 1550°C. GaF can be detected at 211.248 nm providing limits of quantificationin the low μg/L range. However, several species specific effects where observed when analyzing PFASs.
Additive manufacturing of concrete structures is a novel and emerging technology. Free contouring in civil engineering, which allows for entirely new designs, is a significant advantage. In the future, lower construction costs are expected with increased construction speeds and decreasing required materials and workers. However, architects and civil engineers rely on a certain quality of execution to fulfil construction standards. Although several techniques and approaches demonstrate the advantages, quality control during printing is highly challenging and rarely applied. Due to the continuous mixing process commonly used in 3D concrete printing, it is impossible to exclude variations in the dry mixture or water content, and a test sample cannot be taken as a representative sample for the whole structure. Although mortar properties vary only locally, a defect in one layer during printing could affect the entire integrity of the whole structure . Therefore, real-time process monitoring is required to record and document the printing process. At the Bundesanstalt für Materialforschung und -prüfung (BAM) a new test rig for the additive manufacturing of concrete is built. The primary purpose is measuring and monitoring the properties of a mortar during the printing process. The following study investigates an approach for calculating yield stress and plastic viscosity based on experimentally recorded pressure data. The calculations assume that fresh mortar behaves as a Bingham fluid and that the Buckingham-Reiner-equation is applicable. A test setup consisting of rigid pipes with integrated pressure sensors at different positions is utilized. Monitoring the printing process with different sensors is crucial for the quality control of an ongoing process.
With femtosecond-laser direct writing (fs-LDW) maturing in all aspects as a manufacturing technology, a toolset for quality assurance must be developed. In this work we introduce a first of its kind test artifact. Test artifacts are standardized 3D models with specific geometric features to evaluate the performance of writing parameters. Test artifacts are already common in other 3D additive manufacturing technologies e.g. selective laser melting. The test artifact introduced in this work was developed in particular to accommodate the high geometrical resolution of fs-LDW structures and the limited possibilities to examine the resulting structure. Geometric accuracy, surface adhesion as well as confocal Raman spectroscopy results were considered when evaluating the design of the test artifact. We will explain the individual features and design considerations of our fs-LDW test artifact. The difference between two slicers, Cura and 3DPoli, and the implications on measured feature sizes and the general shape is quantified. The measured geometries are used to derive a general design guide for a specific combination of photoresists, laser power and scanning speed and to analyze the geometric accuracy of a structure produced using these guidelines. The shown test artifact is publicly available as STL file on GitHub (https://github.com/BAMresearch/2PP-TestArtifact) and in the supplement.
An automated and straightforward detection and data treatment strategy for the determination of the protein relative concentration in individual human cells by single cell–inductively coupled plasma–time-of-flight mass spectrometry (sc-ICP-ToF-MS) is proposed. Metal nanocluster (NC)-labeled specific antibodies for the target proteins were employed, and ruthenium red (RR) staining, which binds to the cells surface, was used to determine the number of cell events as well as to evaluate the relative volume of the cells. As a proof of concept, the expression of hepcidin, metallothionein-2, and ferroportin employing specific antibodies labeled with IrNCs, PtNCs, and AuNCs, respectively, was investigated by sc-ICP-ToF-MS in human ARPE-19 cells. Taking into account that ARPE-19 cells are spherical in suspension and RR binds to the surface of the cells, the Ru intensity was related to the cell volume (i.e., the cell volume is directly proportional to (Ru intensity)3/2), making it possible to determine not only the mass of the target proteins in each individual cell but also the relative concentration. The proposed approach is of particular interest in comparing cell cultures subjected to different supplementations. ARPE-19 cell cultures under two stress conditions were compared: a hyperglycemic model and an oxidative stress model. The comparison of the control with treated cells shows not only the mass of analyzed species but also the relative changes in the cell volume and concentration of target proteins, clearly allowing the identification of subpopulations under the respective treatment.
Surface-functionalized polymer beads encoded with molecular luminophores and nanocrystalline emitters such as semiconductor nanocrystals, often referred to as quantum dots (QDs), or magnetic nanoparticles are broadly used in the life sciences as reporters and carrier beads. Many of these applications require a profound knowledge of the chemical nature and total number of their surface functional groups (FGs), that control bead charge, colloidal stability, hydrophobicity, and the interaction with the environment and biological systems. For bioanalytical applications, also the
number of groups accessible for the subsequent functionalization with, e.g., biomolecules or targeting ligands is relevant. In this study, we explore the influence of QD encoding on the amount of carboxylic acid (COOH) surface FGs of 2 μm polystyrene microparticles (PSMPs). This is done for frequently employed oleic acid and oleylamine stabilized, luminescent core/shell CdSe QDs and two commonly used encoding procedures. This included QD addition during bead formation by a thermally induced polymerization reaction and a post synthetic swelling procedure. The accessible number of COOH groups on the surface of QD-encoded and pristine beads was quantified by two colorimetric assays, utilizing differently sized reporters and electrostatic and covalent interactions. The results were compared to the total number of FGs obtained by a conductometric titration and Fourier transform infrared spectroscopy (FTIR). In addition, a comparison of the impact of QD and dye encoding on the bead surface chemistry was performed. Our results demonstrate the influence of QD encoding and the QD-encoding strategy on the number of surface FG that is ascribed to an interaction of the QDs with the carboxylic acid groups on the bead surface. These findings are of considerable relevance for applications of nanoparticle-encoded beads and safe-by-design concepts for nanomaterials.
Volumetric changes follow Portland cement hydration reactions: aluminates' hydration is generally expansive, whereas silicates' reaction leads to shrinkage. Determining the volumetric variation at very early ages (i.e., first minutes/hours) presents operational challenges; most standards prescribe the measurement on specimens that are already hardened, while measurements from the fresh state are not standardized. This article reports the first attempt to use in-situ X-ray diffraction (XRD) to measure the early-age volumetric variation of a cementitious paste. For this purpose, a C3A + gypsum paste was assessed for 36 h, measuring its vertical displacement over time through XRD. The results showed that the expansion increased in the first ∼13 h, in line with the ettringite formed up to ∼11 h. In addition, the volumetric expansion agreed well with the heat release in calorimetry. It is concluded that the volumetric variation measurement of cementitious pastes through in-situ XRD is a promising technique, but further studies are needed to make this technique consolidated.
Defects are still common in metal components built with Additive Manufacturing (AM). Process monitoring methods for laser powder bed fusion (PBF-LB/M) are used in industry, but relationships between monitoring data and defect formation are not fully understood yet. Additionally, defects and deformations may develop with a time delay to the laser energy input. Thus, currently, the component quality is only determinable after the finished process.
Here, active laser thermography, a non-destructive testing method, is adapted to PBF-LB/M, using the defocused process laser as heat source. The testing can be performed layer by layer throughout the manufacturing process. The results of the defect detection using infrared cameras are presented for a custom research PBF-LB/M machine. Our work enables a shift from post-process testing of components towards in-situ testing during the AM process. The actual component quality is evaluated in the process chamber and defects can be detected between layers.
The presentation provides an overview and introduction to the need and drivers for the digital transformation of the quality infrastructure (QI). It illustrates the tools and processes that are the fundation of a digital QI and how the initiatve QI-Digital aims at developing accordant solutions. Special emphasis is given to the pilot projects at BAM.
Current decontamination strategies of PFAS-burdened soils mainly consist of adsorption methods using adsorbents for fixation of PFAS in the ground. A second option is the utilization of a “pump and treat” process, cycling polluted soils through a washing plant leading to the concentration of the pollutants in the fine fraction. Only a subsequent, high-energy consuming pyrolysis process guarantees the total destruction of all fluorinated organic contaminants. These approaches are cost-intensive and not intended for the direct decomposition of all PFAS contaminants. Hence, there is a great demand for innovative developments and chemical treatment technologies, dealing with new strategies of tackling the PFAS problem. Thus, we investigated mechanochemical treatment of PFAS contaminated soils with various additives in a ball mill and analyzed the PFAS defluorination with gas chromatography mass spectrometry (GC-MS) and liquid chromatography tandem mass spectrometry (LC-MS/MS), respectively, as well as the fluoride mineralization by ion chromatography (IC) and fluorine K-edge X-ray absorption near-edge structure (XANES) spectroscopy.
The focus of the presentation focus will be on 3D imaging by means of X-ray Computed Tomography (XCT) at the lab and at synchrotron, and the non-destructive residual stress (RS) characterization by diffraction of additively manufactured (AM) materials in BAM (Berlin, Germany). The manufacturing defects and high RS are inherent of AM techniques and affect structural integrity of the components. Using XCT the defects size and shape distribution as well as geometrical deviations can be characterized, allowing the further optimization of the manufacturing process. Diffraction-based RS analysis methods using neutron and synchrotron X-rays at large scale facilities offer the possibility to non-destructively spatially resolve both surface and bulk RS in complex components and track their changes following applied thermal or mechanical loads.