Ingenieurwissenschaften und zugeordnete Tätigkeiten
Filtern
Dokumenttyp
- Zeitschriftenartikel (3) (entfernen)
Sprache
- Englisch (3)
Referierte Publikation
- ja (3)
Schlagworte
- Amorphous phases (1)
- Bismuth (1)
- Data infrastructures (1)
- Digital representations (1)
- Digital workflows (1)
- In situ EXAFS (1)
- In situ SAXS/WAXS (1)
- Intermediate phases (1)
- Knowledge graphs (1)
- Lacunary Keggin ion (1)
Organisationseinheit der BAM
- 1.3 Instrumentelle Analytik (3) (entfernen)
Here, we elucidate nonclassical multistep crystallization pathways of transition metal phosphates from aqueous solutions. We followed precipitation processes of M-struvites, NH4MPO4·6H2O, and M-phosphate octahydrates, M3(PO4)2·8H2O, where M = Ni, Co, or NixCo1–x, by using in situ scattering and spectroscopy-based techniques, supported by elemental mass spectrometry analyses and advanced electron microscopy. Ni and Co phosphates crystallize via intermediate colloidal amorphous nanophases, which change their complex structures while agglomerating, condensing, and densifying throughout the extended reaction times. We reconstructed the three-dimensional morphology of these precursors by employing cryo-electron tomography (cryo-ET). We found that the complex interplay between metastable amorphous colloids and protocrystalline units determines the reaction pathways. Ultimately, the same crystalline structure, such as struvite, is formed. However, the multistep process stages vary in complexity and can last from a few minutes to several hours depending on the selected transition metal(s), their concentration, and the Ni/Co ratio.
Formation Mechanism of a Nano-Ring of Bismuth Cations and Mono-Lacunary Keggin-Type Phosphomolybdate
(2022)
A new hetero-bimetallic polyoxometalate (POM) nano-ring was synthesized in a one-pot procedure. The structure consists of tetrameric units containing four bismuth-substituted monolacunary Keggin anions including distorted [BiO8] cubes. The nano-ring is formed via self-assembly from metal precursors in aqueous acidic medium. The compound (NH4)16[(BiPMo11O39)4] ⋅ 22 H2O; (P4Bi4Mo44) was characterized by single-crystal X-ray diffraction, extended X-ray absorption fine structure spectroscopy (EXAFS), Raman spectroscopy, matrix-assisted laser desorption/ionisation-time of flight mass spectrometry (MALDI-TOF), and thermogravimetry/differential scanning calorimetry mass spectrometry (TG-DSC-MS). The formation of the nano-ring in solution was studied by time-resolved in situ small- and wide-angle X-ray scattering (SAXS/WAXS) and in situ EXAFS measurements at the Mo−K and the Bi−L3 edge indicating a two-step process consisting of condensation of Mo-anions and formation of Bi−Mo-units followed by a rapid self-assembly to yield the final tetrameric ring structure.
The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples.
In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge.