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Eingeladener Vortrag
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Dose enhancement by gold nanoparticles (AuNP) increases the biological effectiveness of radiation damage in biomolecules and tissue.
To apply them effectively during cancer therapy their influence on the locally delivered dose has to be determined. Hereby, the AuNP locations strongly influence the energy deposit in the nucleus, mitochondria, membrane and the cytosol of the targeted cells. In this work, two newly developed continuous and discrete-geometric models for simulations of AuNP in cells are presented. We apply the presented models in Monte-Carlo particle scattering simulations to characterize the energy deposit in cell organelles by radioactive 198AuNP. They emit beta and gamma rays and are therefore considered for applications
with solid tumors. Differences in local dose enhancement between randomly distributed and nucleus targeted nanoparticles are compared.
Hereby nucleus targeted nanoparticels showed a strong local dose enhancement in the radio sensitive nucleus.
Dose enhancement by gold nanoparticles (AuNP) increases the biological effectiveness of radiation damage in biomolecules and tissue. To apply them effectively during cancer therapy their influence on the locally delivered dose has to be determined.[1] Hereby, the AuNP locations strongly influence the energy deposit in the nucleus, mitochondria, membrane and the cytosol of the targeted cells. To estimate these effects, particle scattering simulations are applied. In general, different approaches for modeling the AuNP and their distribution within the cell are possible. In this work, two newly developed continuous and discrete-geometric models for simulations of AuNP in cells are presented. [2] These models are applicable to simulations of internal emitters and external radiation sources. Most of the current studies on AuNP focus on external beam therapy. In contrast, we apply the presented models in Monte-Carlo particle scattering simulations to characterize the energy deposit in cell organelles by radioactive 198AuNP. They emit beta and gamma rays and are therefore considered for applications with solid tumors. Differences in local dose enhancement between randomly distributed and nucleus targeted nanoparticles are compared. Hereby nucleus targeted nanoparticels showed a strong local dose enhancement in the radio sensitive nucleus. These results are the foundation for ongoing experimental work which aims to obtain a mechanistic understanding of cell death induced by radioactive 198Au.
The movement of the macroscopic magnetic moment in ferromagnetic systems can be described by the Landau-Lifshitz (LL) or Landau-Lifshitz-Gilbert (LLG) equation. These equations are strictly valid only at absolute zero temperature. To include temperature effects a stochastic version of the LL or LLG equation for a spin density of one per unit cell can be used instead. To apply the stochastic LL to micromagnetic simulations, where the spin density per unit cell is generally higher, a conversion regarding simulation cell size and temperature has to be established. Based on energetic considerations, a conversion for ferromagnetic bulk and thin film systems is proposed. The conversion is tested in micromagnetic simulations which are performed with the Object Oriented Micromagnetic Framework (OOMMF). The Curie temperatures of bulk Nickel, Cobalt and Iron systems as well as Nickel thin-film systems with thicknesses between 6.3 mono layer (ML) and 31ML are determined from micromagnetic simulations. The results show a good agreement with experimentally determined Curie temperatures of bulk and thin film systems when temperature scaling is performed according to the presented model.
Engineered nanoparticles (NPs) with various chemical compositions and surface functionalities are routinely fabricated for industrial applications such as medical diagnostics, drug delivery, sensing, catalysis, energy conversion and storage, opto-electronics, and information storage which improve the quality of life and European prosperity. NP function, performance, interaction with biological species, and environmental fate are largely determined by their surface functionalities. Standardized repeatable surface characterization methods are therefore vital for quality control of NPs, and to meet increasing concerns regarding their safety. Therefore, industry, regulatory agencies, and policymakers need validated traceable measurement methods and reference materials. This calls for fit-for-purpose, validated, and standardized methods, and reference data and materials on the surface chemistry of engineered NPs. Here, we present a concept for the development of such standardized measurement protocols utilizing method cross-validation and interlaboratory comparisons (ILCs) with emphasis on both advanced measurement methods such as quantitative Nuclear Magnetic Resonance (qNMR), X-ray photoelectron spectroscopy (XPS) and secondary ion mass spectrometry (SIMS) and cost-efficient, non-surface specific methods like optical assays and electrochemical titration methods.
Additive manufacturing (AM) technologies are becoming increasingly important, not only for the manufacture of parts, but also as repair technology that complement existing production technologies. Powder bed fusion of metals by laser beam (PBF-LB/M) combines the freedom in design with high achievable accuracy, making it ideal as a repair approach. However, there are still challenges in adapting process for repair applications. When mounting parts inside PBF-LB/M machines, their real position within the build volume is unknown. One goal of a repair process is to minimize the offset between the base component and the additively manufactured structure to reduce additional rework. For a minimum offset between component and additively manufactured structure, the actual position of the component has to be identified with high precision within the machine coordinate system (MCS). In this work a process setup is presented that allows the actual position of a gas turbine blade to be detected inside a PBF-LB/M machine. A high resolution camera with 65 megapixel is used for this purpose. The presented setup is implemented on a SLM 280 HL PBF-LB/M machine. In addition to the setup, a novel repair workflow using PBF-LB/M is presented. The developed setup and workflow consider inaccuracies in the component and camera mounting, as well as process inaccuracies. This includes keystone distortion correction by homography. The machine setup and workflow are used to repair a real gas turbine blade. Subsequently the offset between the turbine blade and the additivley manufactured structure is validated by 3D scanning the repaired part. The maximum offset is 160 µm. The presented approach can be extended to other geometries and PBF-LB/M machine manufacturers. The high-resolution camera approach is platform independent, which facilates the market penetration of PBF-LB/M repair processes.
Components in aircrafts are usually replaced when critical defects are present.
An alternative approach is repairing using gas dynamic cold spraying: metal particles are shot at a surface at supersonic speeds to selectively rebuild damaged material. Compared to other material-deposition techniques, its advantage is the small thermal impact on the component, preserving its mechanical properties.
Component-repair can save considerable amounts of energy and resources. However, its industrial application at large scale needs reproducible, good repair-material properties to guarantee a safe component life. The aim of this project is the development of safe, automatized repair-procedures considering the mechanical fatigue properties of the repair.
Investigating the phononic properties is beneficial for predicting low thermal conductivity thermoelectric materials.1–3 Employing density functional theory4 takes many calculation steps and consumes a lot of computational resources.5,6 Using machine learning driven interatomic potentials (MLIP, e.g., Gaussian approximation potential,8 GAP) opens up a faster route to phonons7 but in most cases, the potentials are specifically tailored for a certain compound. In this work, we automate the generation of such MLIPs in a Python code-based workflow, based on the automation tools atomate29 and pymatgen10 which combines the automatic DFT computations with the automated fitting of GAPs. Automation enables easier testing, benchmarking and validation.11 We aim to provide the workflow-generated potentials for storage in databases.
GlasDigital
(2023)
Der aktuelle Stand des MateriaDigital Projektes GlasDigital wird vorgestellt. Hierbei wird allgemein die Problem- und Zielstellung präsentiert, als auch auf 2 separaten Postern die Ergebnisse. Diese beinhalten zum Einen die smarte Gestaltung der robotergestützten Glasschmelzanlage der BAM inkl. Analytik und zum Anderen die Digitalisierungsbestrebungen im Bereich Glas, d.h. ML-gestützte C-S-P-Simulation, Ontologie für den Werkstoff Glas, Digitaler Zwilling des Gießprozesses.
A deep insight into the chemistry and nature of individual chemical bonds is essential for understanding materials. Bonding analysis is expected to provide important features for large-scale data analysis and machine learning of material properties. Such information on chemical bonds can be calculated using the LOBSTER (www.cohp.de) software package, which post-processes data from modern density functional theory computations by projecting plane wave-based wave functions onto a local atomic orbital basis. We have performed bonding analysis on 1520 compounds (insulators and semiconductors) using a fully automated workflow combining the VASP and LOBSTER software packages. We then automatically evaluated the data with LobsterPy (https://github.com/jageo/lobsterpy) and provide results as a database. The projected densities of states and bonding indicators are benchmarked on VASP projections and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine-learning model for phononic properties, which shows an increase in prediction accuracies by 27 % (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features.