Ingenieurwissenschaften und zugeordnete Tätigkeiten
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Organisationseinheit der BAM
- 6.0 Abteilungsleitung und andere (5) (entfernen)
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.
Molecular crystalline materials are usually brittle and are prone to breaking when mechanically stressed. This fragility greatly limits they application in next the generation of adaptable, functional materials. The recent discovery of mechanical compliancy in molecular crystals has solved this problem.1 Based on the nature of the deformation, molecular crystals can be divided into being plastically (irreversibly) or elastically (reversibly) bendable. The plastic deformation is generally associated with anisotropic molecular arrangements and the existence of low energy slip planes which allow a permanent motion within the lattice. Here we report 4-bromo-6-[(6-chloropyridin-2-ylimino)methyl]phenol (CPMBP) as a promising candidate for future waveguide technologies.2 CPMBP has been found to have two different polymorphs with distinct optical and mechanical properties. The brittle crystals of Form I exhibits very weak emission at 605 nm (λex = 425 nm; photoluminescence quantum yield Φ = 0.4 %). In contrast, Form II has a large plastic regime together with a bright emission at 585 nm (λex = 425 nm; Φ = 8.7 %). Taking advantage of the favorable mechanical flexibility and optical properties, Form II was used as a shapeable optical waveguide. By changing the wavelength of the light source, active or passive waveguiding can be realized. CPMBP could thus be used as a flexible wavelength filter.
We created a workflow that fully automates bonding analysis using Crystal Orbital Hamilton Populations, which are bond-weighted densities of states. This enables understanding of crystalline material properties based on chemical bonding information. To facilitate data analysis and machine-learning research, our tools include automatic plots, automated text output, and output in machine-readable format.
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.