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Analytical Sciences has developed from Ostwald’s “unentbehrlichen Dienstmagd” to a chemical discipline at the core of many of today’s fundamental and applied scientific problems and innovations. An atomic or molecular understanding of basic processes in chemistry, soft matter physics, materials and life science is enabled only through new analytical methods and instrumentation. Similar observations can be found for pressing sociopolitical conflicts of the future: A rational discussion of global climate change or new energy sources is only possible with reliable analytical results. Progress in Analytical Sciences is only possible if the underlying interdisciplinary character is acknowledged and valued. The talk will illustrate the scope of modern Analytical Science through examples from process analysis relevant to modern process intensification and industry 4.0 to bioanalysis and the use of synchrotron radiation to elucidate fundamental reactions materials.
Carrier gas hot extraction (CGHE) is a commonly applied technique for determination of hydrogen in welded joints using a thermal conductivity device (TCD) for quantitative measurement. The CGHE is based on the accelerated hydrogen effusion due to thermal activation at elevated temperatures. The ISO 3690 standard suggests different specimen geometries as well as necessary minimum extraction time vs. temperature. They have the biggest influence on precise hydrogen determination. The present study summarizes the results and experience of numerous test runs with different specimen temperatures, geometries and factors that additionally influence hydrogen determination. They are namely: specimen surface (polished/as-welded), limited TCD sensitivity vs. specimen volume, temperature measurement vs. effects of PID-furnace controller as well as errors due to insufficient data assessment. Summarized, the temperature is the driving force of the CGHE. Two different methods are suggested to increase the heating rate up the reach the desired extraction temperature without changing the experimental equipment. Suggestions are made to improve the reliability of hydrogen determination depended on the hydrogen signal stability during extraction accompanied by evaluation of the recorded data. Generally, independent temperature measurement with calibration specimens is useful for further data analysis, especially if this data is used for calculation of trapping kinetics by thermal desorption analysis (TDA).
This talk highlights a proof-of-concept that demonstrates the ability to calculate high-resolution Fourier transforms. These can be combined with multi-scale modeling to simulate scattering over a wide range, from small-angle scattering to XRD and PDF.
The preprint documenting this is available on the ArXiv here:
https://doi.org/10.48550/arXiv.2303.13435
The Jupyter notebook, VASP calculation details and MOUSE measured scattering patterns are available from this Zenodo repository: https://dx.doi.org/10.5281/zenodo.7764045
We demonstrate a strategy for simulating wide-range X-ray scattering patterns, which spans the small- and wide scattering angles as well as the scattering angles typically used for Pair Distribution Function (PDF) analysis. Such simulated patterns can be used to test holistic analysis models, and, since the diffraction intensity is on the same scale as the scattering intensity, may offer a novel pathway for determining the degree of crystallinity.
The "Ultima Ratio" strategy is demonstrated on a 64-nm Metal Organic Framework (MOF) particle, calculated from Q < 0.01 1/nm up to Q < 150 1/nm, with a resolution of 0.16 Angstrom. The computations exploit a modified 3D Fast Fourier Transform (3D-FFT), whose modifications enable the transformations of matrices at least up to 8000^3 voxels in size. Multiple of these modified 3D-FFTs are combined to improve the low-Q behaviour. The resulting curve is compared to a wide-range scattering pattern measured on a polydisperse MOF powder. While computationally intensive, the approach is expected to be useful for simulating scattering from a wide range of realistic, complex structures, from (poly-)crystalline particles to hierarchical, multicomponent structures such as viruses and catalysts.
Digitalisierung und Industrie 4.0 verändern komplette Geschäftsmodelle, heben neue Effizienzpotenziale und stärken die Wettbewerbsfähigkeit. Auf dem 57. Tutzing-Symposion vom 15.–18.04.2018 wurde mit Vorträgen und Kreativworkshops erkundet, welche speziellen Anforderungen die Prozessindustrie hat, welche digitalen Innovationen bereits umgesetzt wurden und wo noch Handlungsbedarf besteht. Ein Workshop befasste sich mit den Themenfeldern Datenkonzepte, Datenanalyse, Big Data und künstliche Intelligenz. Es geht nicht um die Digitalisierung von heute. Im Angesicht der wachsenden Digitalisierung unserer Prozesse stellt sich die Frage, ob wir den Prozess wirklich gut kennen. Ob alle Verfahrensschritte detailliert hinterlegt wurden. Nur mit einem heuristischen Ansatz kann das vorhandene Wissen nicht digitalisiert werden.
Sehr schnell werden die Mechanismen eines Massenmarktes mit denen einer Nische verwechselt. Nicht jeder Mechanismus, den wir von großen Suchmaschinen oder Einkaufsportalen kennen, gibt uns einen Hinweis auf Nutzen und Verfügbarkeit für die Prozess- oder pharmazeutische Industrie. Eine gute Analyse der Anforderungen in der Zukunft mit einem Abgleich der derzeitigen technischen Möglichkeiten ist Voraussetzung für eine Verbesserung der derzeitigen digitalen Umsetzung. Dabei ist es sinnvoll unkonventionelle Methoden einzusetzen.
Gold nanocubes with a monodispersed size distribution (edge = 55 nm) deposited on a silicon wafer. These tiny uniform cubes were produced as reference nanoparticles within the EMPIR project nPSize - Improved traceability chain of nanoparticle size measurements. SEM image acquired at 10 kV with an in-lens secondary electron detector. Image colored in ImageJ using LUT “Orange hot.” Published in Microscopy and Microanalysis 25(S2) (2019) 2328.
The combination of microstructural data with other experimental techniques and with modeling is paramount, if we want to extract the maximum amount of information on porous material properties. In particular, quantitative image analysis, statistical approaches, direct discretization of tomographic reconstructions represent concrete possibilities to extend the power of the tomographic 3D representation to insights into the material and component performance. I will show a few examples of possible use of X-ray tomographic data for quantitative assessment of porosity in ceramics.
Moreover, I will show how not-so-novel 2D characterization techniques, based X-ray refraction, can allow a great deal of insights in the damage evolution in microcracked (and porous) ceramics. I will show how X-ray refraction can detect objects (e.g. microcracks) below its own spatial resolution.
Finally, I will discuss the link between the microstructural findings and the mechanical properties of porous microcracked ceramics.
Thermographic super resolution techniques allow the spatial resolution of defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. In this work, we report on the extension of this approach towards a full frame 2D super resolution technique. The approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample
Thermographic super resolution techniques allow the spatial resolution of defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. In this work, we re-port on the extension of this approach towards a full frame 2D super resolution technique. The approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample.