Ingenieurwissenschaften und zugeordnete Tätigkeiten
Filtern
Erscheinungsjahr
Dokumenttyp
- Posterpräsentation (797) (entfernen)
Sprache
- Englisch (663)
- Deutsch (133)
- Französisch (1)
Schlagworte
- Corrosion (36)
- Additive manufacturing (30)
- Mechanochemistry (24)
- Additive Manufacturing (20)
- Glass (20)
- Nanoparticles (19)
- Crystallization (17)
- Degradation (14)
- Fluorescence (14)
- In situ (14)
Organisationseinheit der BAM
- 6 Materialchemie (203)
- 5 Werkstofftechnik (97)
- 8 Zerstörungsfreie Prüfung (95)
- 6.3 Strukturanalytik (65)
- 8.5 Röntgenbildgebung (60)
- 6.6 Physik und chemische Analytik der Polymere (55)
- 9 Komponentensicherheit (53)
- 1 Analytische Chemie; Referenzmaterialien (45)
- 6.1 Oberflächen- und Dünnschichtanalyse (44)
- 4 Material und Umwelt (36)
Eingeladener Vortrag
- nein (1)
Nanoparticles (NPs) have become important materials for a variety of chemical technologies, including catalysis. One of the main challenges is the reduction of green house gases, such as CO2. One opportunity besides the capturing is the conversion to synthesis gas via the reverse water-gas shift reaction.
A facile and efficient method is described for the solvothermal synthesis of size-tunable, stable, and uniform NiCu core-shell NPs. The diameter of the NPs can be tuned in a range from 6 nm to 30 nm and the Ni:Cu ratio from 30:1 to 1:1. The NPs are structurally characterized with combination of transmission electron microscopy, anomalous small-angle X-ray scattering, X-ray photoelectron spectroscopy, and X-ray absorption fine structure. Using these analytical methods, a core-shell-shell structure their chemical composition is elucidated. A depletion from the core to the shell is observed, with the core consisting of NiCu alloy, surrounded by an inner Ni-rich shell and an outer NiO shell. The SiO2-supported NiCu core-shell NPs show pronounced selectivity of >99% for CO in the catalytic reduction of CO2 to CO using hydrogen as reactant (reverse water–gas shift reaction).
In the pursuit of discovering materials with desirable properties, extending the available material libraries is crucial. High-throughput simulations have become an integral part in designing new materials in the past decades. However, there is no straightforward way of distinguishing synthesizable materials from all the proposed candidates. This project focuses on employing AI-driven methods to estimate synthesizability of materials.
Up to now, material scientists and engineers have relied on domain knowledge as well as empirical heuristics to guess the stability and synthesizability of molecules and crystals. The famous Pauling rules of crystal stability are an example of such heuristics. However, after the accelerating material discovery in all the years since Pauling, these rules now fail to account for the stability of most known crystals. A new predictive set of heuristics for crystal stability/synthesizability is unlikely to be uncovered by human perception, given the magnitude and dimensionality of crystallographic data. Hence, a data-driven approach should be proposed to find a predictive model or set of heuristics which differentiate synthesizable crystal structures from the rest. The main challenge of this research problem is the lack of a negative set for classification. Here, there are two classes of data: the positive class which contains synthesizable materials and the negative class which contains materials which are not synthesizable. While the data from the positive class is simply the data of crystals which have been experimentally synthesized, we do not have access to data points which are certainly unsynthesizable. Strictly speaking, if an attempt of synthesizing a crystal fails, it does not necessarily follow that the crystal is not synthesizable. Also, there is no database available which contains the intended crystal structures of unsuccessful synthesis attempts.
This project proposes a semi-supervised learning scheme to predict crystal synthesizability. The ML model is trained on experimental and theoretical crystal data. The initial featurization focuses on local environments which is inspired by the Pauling Rules. The experimental data points are downloaded through the Pymatgen API from the Materials Project database which contains relaxed structures recorded in Inorganic Crystal Structure Database – ICSD. The theoretical data is queried from select databases accessible through the Optimade project’s API.
Vorstellung der Themen der Nachwuchsgruppe "Materialcharakterisierung und -informatik für die Nachhaltigkeit im Bauwesen" von Prof. Sabine Kruschwitz (TU Berlin und BAM)
Within NFDI-MatWerk (“National Research Data Infrastructure for Material Sciences”/ “Nationale Forschungsdateninfrastruktur für Materialwissenschaften und Werkstofftechnik“), the Task Area Materials Data Infrastructure (TA-MDI) will provide tools and services to easily store, share, search, and analyze data and metadata. Such a digital materials environment will ensure data integrity, provenance, and authorship. The MatWerk consortium aims to develop specific solutions jointly with Participant Projects (PPs), which are scientific groups or institutes covering different domains, from theory and simulations to experiments. The Data Exploitation Methods group of the Karlsruhe Institute of Technology-Steinbuch Centre of Computing, as part of TA-MDI, is developing specific solutions in close collaboration with three PPs.
PP07, together with the University of Stuttgart, aims at the image-based prediction of the material properties of stochastic microstructures using large-scale supercomputers. PP13, in cooperation with the University of Saarland, focuses on tomographic methods at various scales in materials research. PP18, together with the Federal Institute for Materials Research and Testing (“Bundesanstalt für Materialforschung und -prüfung”), aspires to define the criteria for materials reference datasets and usage analytics.
The requirements and goals are comparable for each PP: their research outputs, which are scientific datasets, should conform to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. We aim to shape them from a data management perspective making use of the FAIR Digital Object concept, including structured metadata and storage solutions. The results will be a blueprint which will act as a reference for future datasets. Even though the collaboration is in an early stage, the initial steps already show the added value of this approach.
This research has been supported by the Federal Ministry of Education and Research
(BMBF) – funding code M532701 / the Deutsche Forschungsgemeinschaft (DFG, German
Research Foundation) - project number NFDI 38/1, project no. 460247524.
As part of a joint project involving the Fraunhofer Institute for Silicate Research (ISC), the Friedrich Schiller University of Jena, the Clausthal University of Technology and the Federal Institute for Materials Research and Testing (BAM), digital tools are to be created for the development of new types of glass materials. Current processes for the production of glasses with improved properties are usually very cost- and energy-intensive due to the low degree of automation and are subject to long development cycles. The use of robotic synthesis processes in combination with self-learning machines is intended to overcome these problems in the long term. The development of new types of glass can then not only be accelerated considerably, but also be achieved with much less effort.
In this talk, data generation via a robotic high-throughput glass melting system is presented, which should be the experimental basis for the ontology developed within the project GlasDigital.
Mechanochemistry offers a unique opportunity to modify or synthesize new crystal forms. Although the method is very promising, little is known about the mechanochemical means to control the synthesis of a solid form. Using an polymorphic organic cocrystal system, we show here that mechanochemistry can be used to obtain a polymorph transformation under the apparently conventional (thermal) transition point.
Industrial and agricultural waste streams (waste waters, sludges, tailings etc.), which contain high concentrations of NH4+, PO43- and transition metals, are environmentally harmful due to their toxic pollutants. At the same time, phosphorus and selective transition metals such as Cobalt could be potentially depleted as a critical raw material due to the high demand and rapidly declining natural ore deposits. Therefore, due to simultaneous scarcity and abundance, the phosphorus and 3d metal recovery from agricultural, industrial, mining, or urban wastewaters have been an important factor in sustaining our global consumption and preservation of the natural environment. Typically, separate pathways have been considered to extract hazardous substances such as transition metals or phosphate, independently from each other. Here, we report the synthesis routes for transition metal phosphate (TMP) compounds (M3(PO4)2∙8H2O, NH4MPO4∙6H2O, M = Ni2+, Co2+, NixCo1-x2+ M-struvite and M-phosphate octahydrate), which allow for P, ammonia and metal co-precipitation. The precipitation of these compounds from industrial and agricultural waste waters could be a promising P-recovery route. Through adjusting the reaction conditions, the stability, crystallite size and morphology of the as-obtained TMP could be controlled. Detailed investigations of the precipitation process using ex- and in-situ techniques provided new insights into their non-classical crystallization mechanism/crystal engineering of these materials. These TMPs involve transitional colloidal nanophases which subsequently aggregate and condense to final crystals after extended reaction times. However, the reaction kinetics of the formation of a final crystalline product vary significantly depending on the metal cation involved in the precipitation process. Ni-struvite is stable in a wide reactant concentration range and at different metal/phosphorus (M/P)-ratios, whereas Co tends to form Co-struvite and/or Co-phosphate octahydrate depending on the (M/P)-ratio. The mixed NixCo1-x system shows a significantly different crystallization behavior and reaction kinetics of the precipitation compared to the pure endmembers. The observed various degree of stability could be linked to the octahedral metal coordination environment in these compounds. The achieved level of control over the precipitates, is highly desirable for 3d- and P-recovery methods. Under this paradigm, the crystals can be potentially upcycled as precursor materials for (electro)catalytical applications.
Industrial and agricultural waste streams (waste waters, sludges, tailings etc.), which contain high concentrations of NH4+, PO43- and transition metals, are environmentally harmful due to their toxic pollutants. At the same time, phosphorus and selective transition metals such as Cobalt could be potentially depleted as a critical raw material due to the high demand and rapidly declining natural ore deposits. Therefore, due to simultaneous scarcity and abundance, the phosphorus and 3d metal recovery from agricultural, industrial, mining, or urban wastewaters have been an important factor in sustaining our global consumption and preservation of the natural environment. Typically, separate pathways have been considered to extract hazardous substances such as transition metals or phosphate, independently from each other. Here, we investigate the crystallization of transition metal phosphate (TMP) compounds (NH4MPO4∙6H2O, M3(PO4)2∙8H2O with M = Ni2+, Co2+, NixCo1-x2+ M-struvite and M-phosphate octahydrate) out of aqueous solutions, which allow for P, ammonia and metal co-precipitation. The precipitation of these compounds from industrial and agricultural waste waters has high potential as a P- and 3d metal recovery route. For this purpose, a detailed understanding of the crystallization process beginning from combination of solved ions and ending in a final crystalline product is required. Through adjusting the reaction conditions, the stability, crystallite size and morphology of the as-obtained TMPs could be controlled. Detailed investigations of the precipitation process in time using ex- and in-situ techniques provided new insights into their non-classical crystallization mechanism/crystal engineering of these materials. These TMPs involve transitional colloidal nanophases during the crystallization process. Over time, their complex amorphous framework changes significantly resulting simultaneously in an agglomeration and densification of the compound. After extended reaction times these colloidal nanophases condensed to a final crystal. However, the reaction kinetics of the formation of a final crystalline product and the lifetime of these intermediate phases vary significantly depending on the metal cation involved in the precipitation process. Ni-struvite is stable in a wide reactant concentration range and at different metal/phosphorus (M/P)-ratios, whereas Co tends to form Co-struvite and/or Co-phosphate octahydrate depending on the (M/P)-ratio. The mixed NixCo1-x system shows a significantly different crystallization behavior and reaction kinetics of the precipitation compared to the pure endmembers. The observed various degree of stability could be linked to the octahedral metal coordination environment in these compounds. The achieved level of control over the precipitates, is highly desirable for 3d- and P-recovery methods. Under this paradigm, the crystals can be potentially upcycled as precursor materials for (electro)catalytical applications.