Technische Universität Bergakademie Freiberg

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http://opara.zih.tu-dresden.de/handle/123456789/16
Research data repository of Freiberg University of Mining and Technology.

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    Open Access
    2D Imaging Simulation from 3D Particle Data: Extended Python Notebooks, Simulation Results
    (Technische Universität Bergakademie Freiberg, 2024-07-30) Buchwald, Thomas
    2D projection imaging techniques are simulated with 3D particle data from tomography measurements. This submission contains two distinct parts: Python notebooks to simulate 2D imaging of the particles, and the resulting simulation dataset. This dataset extends a previous one (https://doi.org/10.25532/OPARA-479) by an additional method of particle surface area determination and a larger dataset of randomly oriented projections – ten instead of the previous three. The Python notebooks were created with Jupyter Lab and Anaconda. A environment.yml file is provided that recreates the Anaconda environment. The simulation dataset that results from the provided Python notebooks is made available as CSV files or as pickled Python (pandas) DataFrames. Please refer to the included Readme for a detailed description of the files contained in the archive.
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    Open Access
    2D Imaging Simulation from 3D Particle Data: Python Notebooks, Particle Datasets, and Simulation Results
    (Technische Universität Bergakademie Freiberg, 2024-05-14) Buchwald, Thomas; Ditscherlein, Ralf
    2D projection imaging techniques are simulated with 3D particle data from tomography measurements. This submission contains three distinct parts: original particle data, Python notebooks to simulate 2D imaging of the particles, and the resulting simulation dataset. The particle data comes as STLs that have been converted from VTK particle data as provided by the PARROT database (https://parrot.tu-freiberg.de/). The STL files are *not* identical with the STLs provided by PARROT as of May 2024! The particle data folder is provided as separate archive because of its large size. The Python notebooks were created with Jupyter Lab and Anaconda. A environment.yml file is provided that recreates the Anaconda environment. The simulation dataset that results from the provided Python notebooks is made available as CSV files or as pickled Python (pandas) DataFrames. Please refer to the included Readme for a detailed description of the files contained in the archive.
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    Open Access
    Additional data: Impact of Chitosan's Degree of Deacetylation, Molecular Weight, and Crystallinity on the Photoresponsive Properties of Azobenzene-modified Films and Membranes.
    (Technische Universität Bergakademie Freiberg, 2024-11-15) von Seggern, Nils; M. Thiebes, Yannick; Niewa, Rainer; Cord-Landwehr, Stefan; Moerschbacher, Bruno; Tovar, Günter E.M.; Stegbauer, Linus
    A profound understanding of how the degree of deacetylation (DDA), polymerization degree (DP) and photoswitch concentration impact the photomodulation of properties of chitosan(CS)-based responsive materials can serve as a framework for future applications. Herein, we report responsive thin-films manufactured from chitosans with DDA ranging from 70-94% and DP between 170-3380, incorporating 10 30mol% of the light-responsive azobenzene derivative Sodium-4-[(4-(2-(2-(2-methoxyethoxy)ethoxy)ethoxy)phenyl)diazenyl]-benzenesulfonate (TEGABS). During UV-irradiation of the 10 30% TEGABS|CS thin-films, e.g. a significant increase of the indentation modulus by 10 ± 5% is observed. UV illumination leads to a decrease of the water vapor permeability (WVP), reducing it by up to 81 ± 17% compared to native state. We demonstrate that TEGABS up to 10% remains as a solid-solution in CS films with differing amount of H-aggregates depending on the DDA and DP. TEGABS in concentrations >10% in CS leads to phase separation of TEGABS crystallites with a diameter of 21 ± 8 nm. To conclude the photothermal heating by UV irradiation and the resulting water evaporation are identified as the primary driving force for the variation in mechanical properties and WVP, with photoisomerization playing a subordinate factor. These findings provide a new pathway for the design of polysaccharide-based water vapor permeable photoresponsive membranes.
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    Open Access
    Colloidal probe atomic force microscopy measurements on pristine gas bubbles, agglomerates and particle-laden gas bubbles
    (Technische Universität Bergakademie Freiberg, 2024-07-08) Nicklas, Jan
    This dataset contains experimental Atomic Force Microscopy (AFM) force-displacement measurements of poorly wetted alumina particles (colloidal probes) < 15 µm interacting with pristine gas bubbles, agglomerates and particle-laden gas bubbles (hetero-coagulates) in an aqueous environment. The data was generated in the context of the Collaborative Research Center CRC 920 (Project ID 169148856 – subproject B04) that is funded by the German Research Foundation (DFG). The data set is of interest for researches that study the agglomeration or hetero-coagulation in liquid environment, and it is expected to be of particular interest to those that are concerned with the simulation and modeling of these processes (for example by Discrete Element Method). The AFM raw data was baseline corrected to increase the accessibility of the data sets to those less familiar with the AFM. A detailed interpretation of the AFM data in this repository is found in https://doi.org/10.1021/acs.langmuir.3c01781 and https://doi.org/10.1016/j.colsurfa.2024.134660.
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    Open Access
    Correlative X-ray micro-Computed Tomography (X-µCT) scans of Engineered Artificial Minerals (EnAM)
    (Technische Universität Bergakademie Freiberg, 2024-11-07) Siddique, Asim; Schröer, Laurenz
    Characterizing complex particulate materials like slag using X-ray microcomputed tomography (μCT) is challenging due to minimal grey-scale contrast from similar attenuation properties among phases and intricate microstructures. To address this problem, we developed a standardized multi-scale correlative methodology that combines μCT at different resolutions with scanning electron microscopy and energy-dispersive X-ray spectroscopy (SEM-EDS) and X-ray fluorescence (XRF). By scanning large samples for statistical significance and sub-samples at higher resolutions, we capture detailed microstructures. Aligning SEM-EDS data with μCT scans using inherent markers enables accurate phase segmentation. Mineral mapping from SEM-EDS can help to train segmentation models for μCT data, overcoming μCT limitations and allowing precise 3D mineralogical characterization. This approach provides a robust framework for analyzing complex slag particles. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101005611 https://excite-network.eu.
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    Open Access
    Dynamic Image Analysis: Python Notebooks, Particle Datasets, and Simulation Results
    (Technische Universität Bergakademie Freiberg, 2024-08-20) Buchwald, Thomas
    This submission serves as a validation dataset for the simulation results of 2D imaging methods from 3D particle meshes. The submission contains dynamic image analysis data that can be used for validation of simulation results, Python notebooks for extraction of particles and calculation of particle characteristics, and the final results in as pickled Pandas DataFrames and CSV files. This dataset extends to previous submission: https://doi.org/10.25532/OPARA-479 and https://doi.org/10.25532/OPARA-587, which contain the 3D particle dataset and the simulation algorithm notebooks.
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    Open Access
    Influence of a pyrolysis at different temperatures on the mechanical recycling efficiency of Li-ion batteries
    (Technische Universität Bergakademie Freiberg, 2024-12-12) Kaas, Alexandra; Wilke, Christian; Born, Jannik; Ahuis, Marco; Kwade, Arno; Peuker, Urs
    The integration of a pyrolysis stage into the mechanical recycling process of Li-ion batteries is supposed to improve the decoating of the electrodes, the recovery of valuable components and the overall quality of the products. The effectiveness of the pyrolysis process depends on the applied temperature as the various components of a Li-ion battery melt, evaporate and decompose at different temperatures. The decomposition temperature of the cathode binder is a crucial factor in the thermal mechanical recycling process. Temperatures below this threshold have a negative impact on the recovery rates, as the melting of binder and plastics reduces the efficiency of the recovery process. Conversely, higher temperatures facilitate the recovery of the cathode coating metals (Ni, Co, Li) into the black mass. The majority of the metals (92-98%) are recovered and, following hydrometallurgical treatment, can be reused for cell production, thereby closing the loop and reducing the consumption of raw materials and enhancing the sustainability of batteries. Moreover, the decomposition of the organic components, including binders and plastics, improves the quality of the products, thereby reducing the necessity and extent of further treatment.
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    Open Access
    Research data for: “Forces during film drainage and detachment of NMC and spherical graphite in particle-bubble interactions quantified by CP-AFM and modeling to understand the salt flotation of battery black mass”
    (Technische Universität Bergakademie Freiberg, 2024-12-09) Nicklas, Jan
    This dataset characterizes the particle-bubble interaction for single battery black mass particles (NMC 622 and spherical graphite) in sodium chloride solutions (0.001 mol/L to 0.750 mol/L) for pH 3 to pH 10. The interaction of black mass particles with gas bubbles in the AFM-geometry gives information about the likeliness of particle-bubble-attachment and detachment in salt flotation. The research data consists of two parts: A) the Experimental Atomic Force Microscopy data for the interaction of black mass particles (NMC 622 (NMC) and spherical graphite (SG)) with sessile gas bubbles in salt solutions and B) the Data for the key figures of “Forces during film drainage and detachment of NMC and spherical graphite in particle-bubble interactions quantified by CP-AFM and modeling to understand the salt flotation of battery black mass”.
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    Open Access
    Robust Laser Cross Detection
    (Technische Universität Bergakademie Freiberg, 2024-12-12) Kluwe, Moritz Niklas; Hardege, Robert
    The source code repository contains the complete Python implementation of the laser cross detection algorithm, including example datasets. The repository includes synthetic image generation tools, evaluation scripts, and reference implementations of RANSAC and Probabilistic-Hough-Transform methods for comparison. Example datasets feature both synthetic and real-world calibration images, with documentation detailing their usage. All code is thoroughly commented and includes usage examples. The implementation requires common Python libraries (numpy, scipy, lmfit) and provides a straightforward API for integration into existing calibration workflows.
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    Open Access
    Rohdaten für Dissertation "Kinetische Studie der Umbenetzungsagglomeration als Basis für die zweidimensionale formselektive Trennung von Partikelgemischen"
    (Technische Universität Bergakademie Freiberg, 2024-10-02) Schreier, Julia
    Dieser Datensatz enthält die Rohdaten zur Dissertation "Kinetische Studie der Umbenetzungsagglomeration als Basis für die zweidimensionale formselektive Trennung von Partikelgemischen". Um Partikelgemische hinsichtlicher ihrer Primärpartikelform mit Hilfe der Umbenetzungsagglomeration trennen zu können, muss der Prozess in der Benetzungsphase abgebrochen werden. Um diesen Zeitpunkt bestimmen zu können, kann das Rührerdrehmoment genutzt werden, da damit die Prozesskinetik in-line bestimmt werden kann. Um diese Annahme zu beweisen, wurden zu unterschiedlichen Zeitpunkten Proben aus der Umbenetzungsagglomeration entnommen und mittels Mikroröntgentomographie (µ-CT) und dreidimensionaler Bildverarbeitung analysiert. Die Agglomeratgröße am Ende des Prozesses wurde mit zweidimensionaler Bildverarbeitung bestimmt.
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    Open Access
    Supplementary data of publication: "In-line Image Analysis of Particulate Processes with Deep Learning: Optimizing Training Data Generation via Copy-Paste Augmentation"
    (Technische Universität Bergakademie Freiberg, 2024-04-26) Daus, Sarah
    This dataset contains the supplementary data of the open-access publication "In-line Image Analysis of Particulate Processes with Deep Learning: Optimizing Training Data Generation via Copy-Paste Augmentation". It provides all the necessary data and scripts to replicate the results of the study and to further use the synthetic data generation to train one's own deep learning image segmentation model. The original scripts have been slightly modified to make them more suitable for general use, e.g. parameters have been passed to an argument parser instead of hard-coding them whenever possible. Abstract of Paper: Monitoring particle properties directly in the process using in-situ microscopy can provide valuable input to control loops, improve process understanding and facilitate process optimization. However, obtaining reliable information from these images remains a challenge, especially for higher solids concentrations or agglomerating systems. Recent studies have successfully applied deep learning models to extract particle characteristics from in-situ image data. Despite these advances, the problem of generating training data has not been properly addressed. Manual annotation is time-consuming and prone to bias due to high particle counts, particle overlap, and out-of-focus objects. This paper presents a new approach to generate training data for segmentation models by combining conventional segmentation methods with copy-paste augmentation. A case study was conducted in which depth filtration experiments were performed on irregularly shaped alumina particles. An instance segmentation model trained on data generated using the proposed approach successfully detected and characterized particles, even at high solids concentrations.