Ingenieurwissenschaften und zugeordnete Tätigkeiten
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Iron can form numerous oxides, hydroxides, and oxide−hydroxides. Despite their relevance, many of the transformation processes between these phases are still poorly understood. In particular the crystallization of quasi-amorphous hydroxides and oxide−hydroxides is difficult to assess, since typical diffraction and scattering methods provide only sampleaveraged information about the crystallized phases. We report a new approach for the investigation of the crystallization of oxide−hydroxides. The approach relies on model-type films that comprise a defined homogeneous nanostructure. The nanostructure allows quantitative linking of Information obtained by bulk-averaging diffraction techniques (XRD, SAXS) with locally resolved information, i.e., Domain sizes (SEM, TEM, LEEM) and phase composition (SAED). Using time-resolved imaging and diffraction we deduce mechanism and kinetics for the crystallization of ferrihydrite into hematite. Hematite forms via nucleation of hematite domains and subsequent Domain growth that terminates only upon complete transformation. A Johnson−Mehl−Avrami−Kolmogorov model describes the kinetics over a wide temperature range. The derived understanding enables the first synthesis of ferrihydrite films with ordered mesoporosity and quantitative control over the films’ hematite and ferrihydrite content.
A Round Robin study has been carried out to estimate the impact of the human element in small-angle scattering data analysis. Four corrected datasets were provided to participants ready for analysis. All datasets were measured on samples containing spherical scatterers, with two datasets in dilute dispersions, and two from powders.
Most of the 46 participants correctly identified the number of populations in the dilute dispersions, with half of the population mean entries within 1.5 % and half of the population width entries within 40 %, respectively. Due to the added complexity of the structure factor, much fewer people submitted answers on the powder datasets.
For those that did, half of the entries for the means and widths were within 44 % and 86 % respectively. This Round Robin experiment highlights several causes for the discrepancies, for which solutions are proposed.
Compared to the clear, real-space images you can get from electron microscopy, X-ray scattering patterns are rather featureless. These patterns, however, contain structural information from all of the material structure illuminated by the X-ray beam. With this technique, you can measure nanoparticle dispersions, catalysts, composites, MOF powders, battery materials, light metal alloys and gels to reveal information on the structural features found within these materials. We have even measured many such materials for several research groups from the University of Birmingham, revealing structure features in the sub-nm to the micrometer range.
Measuring an X-ray scattering pattern is relatively easy, but measuring a high-quality, useful pattern requires significant effort and good laboratory organization. Such laboratory organization can help address the reproducibility crisis in science, and easily multiply the scientific output of a laboratory, while greatly elevating the quality of the measurements. We have demonstrated this for small- and wide-angle X-ray scattering in the MOUSE project (Methodology Optimization for Ultrafine Structure Exploration) [1]. With the MOUSE, we have combined: a) a comprehensive and highly automated laboratory workflow with b) a heavily modified X-ray scattering instrument. This combination allows us to collect fully traceable scattering data, within a well-documented, FAIR-compliant data flow (akin to what is found at the more automated synchrotron beamlines). With two full-time researchers, our lab collects and interprets thousands of datasets, on hundreds of samples, for dozens of projects per year, supporting many users along the entire process from sample selection and preparation, to the analysis of the resulting data.
Everything SAXS
(2019)
In contrast to the crisp, clear images you can get from electron microscopy, small-angle X-ray scattering (SAXS) patterns are rather featureless. These patterns, however, contain averaged structural information of all of the finest material structures that were illuminated by the X-ray beam. With careful and precise investigation, and supplementary information from complementary techniques, this bulk material structure can be quantified to reveal structural information spanning four or even five decades in size. Additionally, while the data correction and analysis is complex, sample preparation is very straightforward, also allowing for in-situ and operando measurements to be performed without breaking a sweat. In the right hands, then, this technique can be the most powerful tool in your analytical arsenal.
Reference nanomaterials
(2016)
This chapter reviews relevant aspects of the still less explored field of RNMs. In the first part, definition of RMs according to different metrological levels was presented as well as the general strategy on their development. In the second part, the importance of RNMs in material science and nanotoxicology is explained and considerations on the use of silica nano-objects as potential future reference material was discussed. Overall, several points should be highlighted from this chapter: Definition of a reference material depends on the level of standardization. Certified reference material represents the highest level of traceability. Different types of reference materials, standard materials and certified reference materials are available, as shown in Table 3.1. The preparation of nano-objects with well-defined mechanical properties is still an unsolved issue and thus needs further R&D activities. Nevertheless, amorphous silica seems to be a promising candidate for reaching this objective. Traceable properties of nanomaterials are still limited to few cases such as primary particle size and thickness measurements of very thin films. In general, developed RMs are monoparametric thus, only one characteristic of the nano-objects is reported. In nanotoxicology, the challenge is in the development of suitable RNM that bears similarities to the measurements made on the sample analysed.
Post-assembly modifications are efficient tools to adjust colloidal features of block copolymer (BCP) particles. However, existing methods often address particle shape, morphology, and chemical functionality individually. For simultaneous control, we transferred the concept of seeded polymerization to phase separated BCP particles. Key to our approach is the regioselective polymerization of (functional) monomers inside specific BCP domains. This was demonstrated in striped PS-b-P2VP ellipsoids. Here, polymerization of styrene preferably occurs in PS domains and increases PS lamellar thickness up to 5-fold. The resulting asymmetric lamellar morphology also changes the particle shape, i.e., increases the aspect ratio. Using 4-vinylbenzyl azide as co-monomer, azides as chemical functionalities can be added selectively to the PS domains. Overall, our simple and versatile method gives access to various multifunctional BCP colloids from a single batch of pre-formed particles.
The hexapeptide hIAPP22–27 (NFGAIL) is known as a crucial amyloid core sequence of the human islet amyloid polypeptide (hIAPP) whose aggregates can be used to better understand the wild‐type hIAPP′s toxicity to β‐cell death. In amyloid research, the role of hydrophobic and aromatic‐aromatic interactions as potential driving forces during the aggregation process is controversially discussed not only in case of NFGAIL, but also for amyloidogenic peptides in general. We have used halogenation of the aromatic residue as a strategy to modulate hydrophobic and aromatic‐aromatic interactions and prepared a library of NFGAIL variants containing fluorinated and iodinated phenylalanine analogues. We used thioflavin T staining, transmission electron microscopy (TEM) and small‐angle X‐ray scattering (SAXS) to study the impact of side‐chain halogenation on NFGAIL amyloid formation kinetics. Our data revealed a synergy between aggregation behavior and hydrophobicity of the phenylalanine residue. This study introduces systematic fluorination as a toolbox to further investigate the nature of the amyloid self‐assembly process.