Point cloud file (e57) of the main campus of Hamburg University of Technology containing intensity and color information for 125 separate scans.

The presented model is a resin 3D-printable flow cell for in-line measurements in a Spinsolve 80 Carbon Ultra (Magritek GmbH) bench top nuclear magnetic resonance spectrometer. It is designed to be fully printable without any post-printing modifications. The two column parts thread into each other and provide a cavity of 500 μL that is intended to hold immobilized enzyme for flow catalysis or empty resins for dynamic relaxometry experiments. The upper and lower column part each has an integrated mesh of 0.125 μm pore size to hold typical resins. The threaded fittings are designed to use regular 1/16" HPLC tubing. To create a proper seal inside the column, the tubing has to be flanged using, e.g., a thermoelectric flanging tool. The column is filled, assembled and can finally be placed in the opening of the NMR device.

Nanoporous metals with their complex microstructure represent an ideal candidate for method developments that combine physics, data and machine learning. They allow to tune the solid fraction, ligament size and connectivity density within a large range. These microstructural parameters have a large impact on the macroscopic mechanical properties. This makes this class of materials an ideal science case for the development of strategies for dimensionality reduction, supporting the analysis and visualization of the underlying structure-property relationships. Efficient finite element beam modeling techniques are used to generate ~200 data sets for macroscopic compression and nanoindentation of open pore nanofoams. A data base is provided that uses consistent settings of structural and mechanical properties on the microscale for which the elastic-plastic macroscopic compression behavior and the hardness is predicted. Ligament geometries of two different initial solid fractions are chosen, for which the structural randomization, the connectivity density, the yield stress and the work hardening rate are randomly varied in large ranges. This data base allows deriving the microstructure-properties relationships of nanoporous metals by means of dimensionality reduction, data mining and machine learning.

This study investigates biofilm development by 𝘊𝘶𝘱𝘳𝘪𝘢𝘷𝘪𝘥𝘶𝘴 𝘯𝘦𝘤𝘢𝘵𝘰𝘳 H16 on Aerographite, an ultralow-density carbon foam (3-6 mg cm⁻³, porosity >99%) with a hierarchical tetrapodal pore network, as a cathode material in microfluidic bioelectrochemical systems and compares it to plain graphite.

Laser powder bed fusion (PBF-LB/M) of metals belongs to the advanced additive manufacturing processes on the brink of industrialization. Successful manufacturing often requires the utilization of support structures to support overhangs, dissipate heat, and prevent distortion due to residual stresses. Since the support structures result in increased costs, research, as well as industry, aim at optimizing the application of those or the support structures themselves. New approaches are validated with individual use cases, though, preventing an objective comparison of optimization strategies. This paper contributes to the advance of support structure optimization by providing a benchmark strategy including part geometries, which enables to evaluate technical as well as economical aspects of support structures or support strategies. The benchmark process is demonstrated with the help of the currently most used block and pin support structures.

The available data include the raw data and calculations on chemical analyses of various urban and rural green wastes. The wastes were selected in the context of a biorefinery concept and characterised within the FLEXIBI project. The data can be used to evaluate potentials for different recovery pathways. One pathway included in the evaluation is anaerobic digestion. The description of the methods, calculations and results is included in the PDF file.

This dataset encompasses a comprehensive collection of experimental data to the adsorption characteristics and membrane performance of Suwanee River NOM (SRNOM) on Sartobind D and Sartobind Q membranes. The data is segregated into distinct categories, including batch adsorption and desorption, cyclic adsorption processes, dynamic adsorption under varying conditions, and fundamental membrane characterization. Each category is in its respective folder, containing detailed README files.

This dataset contains flow cytometry and cultivation-based microbial data, along with measurements of natural organic matter (NOM) characterized by fluorescence, absorbance, and liquid chromatography-organic carbon detection (LC-OCD) from CS#1H. Data were collected over a one-year monitoring period at two sampling locations: the infiltration ditch and the abstraction well.

Data supplement for publication: "An Experimental Study on the Time Dependence of Diffusive Mass Transfer of Single Oxygen Bubbles" ------------------------------------------------------------ Publication Information ------------------------------------------------------------ Title: An Experimental Study on the Time Dependence of Diffusive Mass Transfer of Single Oxygen Bubbles Keywords: gas-liquid mass Transfer, Sherwood number, PLIF, LSFM Authors: Lotta Kursula - 0009-0006-6587-2709 Institute of Multiphase Flows, Hamburg University of Technology, Hamburg, Germany Sayaka Takagi Institute of Multiphase Flows, Hamburg University of Technology, Hamburg, Germany Felix Kexel - 0000-0003-4268-2348 Institute of Multiphase Flows, Hamburg University of Technology, Hamburg, Germany Marko Hoffmann Institute of Multiphase Flows, Hamburg University of Technology, Hamburg, Germany Michael Schlüter - 0000-0001-5969-2150 Institute of Multiphase Flows, Hamburg University of Technology, Hamburg, Germany DOI of publication: DOI of data supplement: https://doi.org/10.15480/882.17032 License: Public Domain Mark 1.0 Universal Abstract of the paper: In the vast majority of gas-liquid engineering applications, the liquid phase contains a range of dissolved gaseous species. These dissolved process gases transfer from the liquid phase to the gaseous phase countercurrent to the typically desired mass transfer of gas to liquid. In process design, the resulting change in the composition of the gaseous phase is usually neglected, although a temporal change in the composition of the gaseous phase can directly influence the mass transfer performance over time. The current fundamental study quantifies the mass transfer performance of oxygen bubbles to liquid phases saturated with another gas. For this purpose, the oxygen mass transfer from a bubble to degassed, helium-, nitrogen-, argon- and carbon dioxide-saturated water is studied. Light Sheet Fluorescence Microscopy is used as imaging system for Planar Laser-induced Fluorescence measurements of dissolved oxygen concentration fields, delivering local instantaneous Sherwood numbers, diffusion coefficients and mass Transfer coefficients. For the first time, the study showcases that the mass transfer performance from a gaseous dispersed to a liquid continuous phase is independent of time if mass transfer occurs in one direction only. If mass transfer occurs in both directions, the mass transfer performance of the dispersed phase is significantly lower and its time dependence higher to liquids containing gaseous species with high solubilities, such as carbon dioxide in water. Furthermore, the results suggest that applying intrinsic values, such as diffusion or mass transfer coefficients, obtained for binary systems in multicomponent systems can lead to high uncertainty.

Simulated and approximated values for the article "Analysis of semi-open queueing networks using lost customers approximation with an application to robotic mobile fulfilment systems".

This dataset was used to characterize devolatilized wood pellets for fluidized bed applications. The results and the details about the methodology are presented in "Jarolin, K.; Wang, S.; Dymala, T.; Song, T.; Heinrich, S.; Shen, L.; Dosta, M. (2021): Characterizing devolatilized wood pellets for fluidized bed applications. In: Biomass Conversion and Biorefinery." In this work, three types of wood pellets, called Type A (white sawdust pellet from spruce wood), Type B (brown sawdust pellet from foliage and coniferous wood), and Type C (brown sawdust pellet from mixed, unspecified wood types) were characterized after devolatilization. Three different methods for devolatilization were applied: 1. Muffle Furnace (MF) at 900°C 2. Fluidized Bed Reactor (FBR) at 900°C 3. empty fluidized bed reactor with high gas flow rate (HFR) at 900°C 4. empty fluidized bed reactor with low gas flow rate (LFR) at 900°C The raw data from the three main methods for characterization are provided in this repository: 1. compressionTest: Formatted force-displacement measurements of quasi-steady, uniaxial compression tests in the radial direction. The data was used to study breakage behavior. 2. impactTest: Mass loss of the pellets due to impact on a steel plate at different velocities. The mass loss is given after 10 consecutive impacts. In case of breakage, the masses of the fragments are given. The data was used to study resistance to impact and the continuous wear of the pellets. 3. uCT: Micro-computed tomography images from the same pellets before and after devolatilization as well as images of devolatilized pellet for reference. One of the pellets' end-faces was ground into an angle to allow the identification of the orientation. The data was used to study the change in the porosity of the pellets. Please refer to the linked publication for further details.

Data containing the results and figures presented in the paper "A rigorous optimization method for long-term multi-stage investment planning: Integration of hydrogen into a decentralized multi-energy system" by Luka Bornemann and Jelto Lange and Martin Kaltschmitt, submitted to the Journal Energy Reports.

Data and code to reproduce results and figures presented in the paper "Optimizing Temperature, Pressure, and Waste Heat Utilization in PEM Electrolyzers: A Model-based Approach to Enhance Integrated Energy System Efficiency" by Luka Bornemann and Jelto Lange and Martin Kaltschmitt, submitted for The 38th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems – ECOS 2025. The code consists of: - Plotting the results figures (plotting.py)

This is the readme file for all data used within the publication "Exploring key ionic interactions for magnesium degradation in simulated body fluid - a data-driven approach" Created: 25th January 2020 by Dr. Berit Zeller-Plumhoff Contact: berit.zeller-plumhoff@hzg.de The publication is based on a number of experimental and computational data sets. These are: - microCT imaging data (raw and processed) before and after degradation - Fiji/ImageJ scripts for automated processing and read-out of the imaging data - SEM+EDX imaging data (raw and processed) - Matlab scripts for processing of the EDX data - Hydra/Medusa output files for precipitation prediction - Jupyter Notebooks for the final analysis and plotting of the processed data Due to the large amount of imaging data (~70GB per microCT dataset, raw+processed), we are publishing only the processed data of two exemplary data sets - one prior to degradation and one after degradation of the sample. All other data is stored and will be provided upon request. Where a differentiation of datasets into 'C' and 'D' is denoted, this corresponds to the ph-adjusted and non-adjusted cases, respectively, as described in the publication. Please find below a short description of all datasets that are made available. For better handling the data was divided into nine .zip files. The folder "microCT_sol1_sample1_degraded" contains the exemplary microCT data for the first sample degraded in the pH-adjusted solution 1, following data processing after reconstruction. All image files are in .tif format, with a 1.6 micrometer isotropic voxel size. The folder "cropped" contains the manually cropped filtered images of the 0.7 mm ROI (from the sample centre) after "script filtering.ijm". Using the trainable WEKA segmentation with the "classifier.model" file images were segmented and saved in "seg_weka" by utilising the script "Segmenting_with_weka.ijm". Some files had to be manually corrected - these were saved in "seg_weka_edited". Finally, the images were aligned along their longitudinal axis using the script "BW_Conversion_Alignment.ijm" and saved in the folder "aligned". Folders "microCT_sol1_sample1_degraded_raw" and "microCT_sol1_sample1_degraded_reco" are the raw projection data and tomogaphic reconstruction of this dataset. The folder structure for the initial scan of sample 1 (sample_C1_initial) is simimilar; however, it contains only a general segmented folder "segmented_all" with the filtered, segmented and aligned images and their respective outline (subfolder "outline") for the calculation of the initial sample surface area. The image processing was performed using the "filtering.ijm" script. Again, folders "sample_C1_initial_raw" and "sample_C1_initial_reco" are the raw projection data and tomogaphic reconstruction of this dataset. The Fiji scripts are saved in the folder "Fiji_Scripts", divided into those used for before and after degradation scans. Note that all file paths require changing for the scripts to run.The scripts "Data_Gathering_Initialscan_BZP_C_D.ijm" and was used to gather outline data from the entire sample, outline data from the ROI, and volume data from the ROI. The file "edx_analysis.m" is the Matlab file that was used to analyse all EDX data for further processing. The variable f was saved as "EDX_results.mat". The paths are given relative to the .m file and need to be adjusted if this is moved. The file accesses the "EDX_data" folder. This folder contains the "SEMEDX" folder in which the elemental wt% maps from EDX measurements for each sample are stored as .txt files. The folder "Segmented" then contains the respective segmented residual magnesium images and the segmented degradation layer images that the Matlab file requires for its computation. The folder "Data_Mg_Ion contains" all processed data that is then read and further processed by the respective Jupyter notebooks. The subfolders "Initial_data" and "Final_data" contain the histograms as calculated by Fiji/ImageJ for the volume (V) and area (A) assessment of the initial and degraded wires. The data structure is: # Final V data: /Final_data/histo_1.1C # Initial V data: /Initial_data/initial_histo_1.1C # Initial A data (whole sample, not 0.7mm ROI): /Initial_data/initial_outline_1.1C # Initial A data (0.7mm ROI): /Initial_data/initial_outline_ROI_1.1C The subfolder "Hydra_Medusa" contains the Mg and Ca precipitates as computed by Hydra Medusa for each solution in .csv format. The files "EperimentalData.csv" and "ph_per_sample.csv" contain the pH and temperature measurements for each sample/solution in averaged or full format. The file "EDX_results.mat" contains the mean elemental wt% for each element and its distribution along the degradation layer as computed in Matlab. The file "ion_concentrations.txt" contains the theoretical initial concentration for all ions. The precipitation-dependent corrections are given in "corr_conc.pkl" (which is calculated in the file "precipitation_graphs_publication.ipynb"). "statistics_lincorr_publication.ipynb" - this Jupyter notebook contains the results for the Student's t-test and the linear correlation of parameters "precipitation_graphs_publication.ipynb" - this Jupyter notebook creates the precipitation graphs used in the publication "graphs_publication.ipynb" - this Jupyter notebook creates all other graphs "tree_regression_publication.ipynb" - this Jupyter notebook contains the tree regression analysis using different input parameter sets and different regression models All figures generated by the Jupyter notebooks are saved in the "figures" subfolder. Please note that for all Jupyter notebooks to work you may need to install missing modules.

The data is organized as a ZIP Archive split in 5GB parts. To use the Data, all files have to be downloaded and extracted together. The Archive contains 4 folders. 1. Dynamic-Test-Data 2. Modulation_measurements 3. Pictures 4. Tensile test 1. Dynamic-Test-Data This folder contains the vibrational data from the hydraulic testing machine. It is not shown in the publication but was used to validate the correctness of the measurements. ### 2. Modulation_measurements This folder contains the vibroacoustic measurements for all samples. There are subfolders in which the data files of the different combinations between the piezoceramics have been saved as '.mat'. Each File contains four arrays. + freq ... Frequencies used for the high-frequency excitation 201000 Hz to 220000 Hz in steps of 500 Hz (Low frequency is 5Hz due to the limitations of the pulsing machine.) + VAM ... Measurement signal, from which the modulation is calculated. It contains 2s with a sampling rate of 2e6 MSa for each high frequency. + Chirp_mod ... Contains the measurement of a linear chirp ranging from 1Hz to 300kHz in 5 seconds. Here the Frequency-Response-Spectrum can be evaluated. The sampling rate was set to 1e6 to save data. + pulser .... 2 seconds of measurement with a sampling rate of 1e6 MSa where just the hydraulic testing machine is running. To evaluate if there are any differences in the machine. This data was not. used for the publication. ### 3. Pictures This folder contains pictures of all samples in different stages. + C-Scans of the bonds + Fracture Surfaces after the tensile test + Trough - light photos of the bonded plates + Through-light images of each cut-out specimen ### 4. Tensile Test This folder contains the tensile-test data for every sample measured at a Zwick Z100. Exemplary pictures of the testing are in the pictures folder. Questions can be directed to benjamin.boll@tuhh.de or robert.meissner@tuhh.de

The data set contains stress strain measurements and 3D computed tomography (CT)-data measured in in situ uniaxial compression experiments with parallel CT-imaging on unsaturated granular (soil) specimens. The CT-data has been measured with the laboratory X-ray tomograph at Laboratoire 3SR, Université Grenoble Alpes during uniaxial compression of the specimens applied by a miniaturized uniaxial compression apparatus developed at TUHH.

This data set contains research data related to the article "In situ X-ray CT imaging of transient water retention experiments with cyclic drainage and imbibition" to be published in the Journal Open Geomechanics. The research data include 3D CT images acquired during cyclic drainage and imbibition of a sand specimen in a transient in situ water retention experiment that was run in the X-ray tomograph at Laboratoire 3SR at Univ. Grenoble Alpes. Besides the CT images, also data from the multiphase image analysis as well as macroscopic water retention data, measured in parallel to the CT scans, are published.

Results and complementary data for the paper: Study on the Cohesive Edge Crack in a Square Plate with the Cohesive Element Method. A numerical CEM-based model was built to compute the fracture process zone size for an edge crack in a finite square plate of different lengths. The fracture process zone size is given for different plate sizes, physical crack lengths and softening (cohesive) laws.

This study investigates carbon nanotube growth on triply periodic minimal surfaces and investigates the impact of the substrates permeability on the morphology and composition by altering their unit cell size.

This repository contains a **complete research data package** for the first experimental validation of transport-based recurrence CFD (rCFD) methodology applied to liquid mixing in stirred tank reactors. The dataset includes M-Star lattice Boltzmann simulations providing velocity field databases, rCFD implementation with calibrated diffusion parameters, experimental conductivity probe measurements, systematic parametric injection location optimization (90 cases), post-processing workflows, and comprehensive validation results demonstrating 3,400× computational speedup versus ANSYS Fluent while achieving closer agreement with experimental data than conventional high-fidelity CFD methods. **Research Context**: This dataset addresses a critical computational bottleneck in stirred tank reactor (STR) design and optimization. Conventional CFD methods (ANSYS Fluent, M-Star) require days to weeks to simulate mere seconds of mixing time, rendering parametric studies and real-time process optimization impractical for industrial applications. Recurrence CFD (rCFD) exploits pseudo-periodic flow patterns in turbulent stirred tank flows to achieve orders-of-magnitude computational acceleration while preserving predictive accuracy. **Key Innovation**: First rigorous experimental validation of rCFD for liquid mixing, demonstrating that calibrated rCFD (operating on coarse 4.7M lattice point M-Star flow fields) predicts mixing time closer to experimental measurements (τ₉₅=24.5±1.0 s vs 24.9±2.7 s experimental) than high-resolution M-Star (29.1±1.4 s, 90M nodes) or ANSYS Fluent (29.2±2.9 s, 3.4M cells). The resulting computational efficiency enables a 90-case parametric design space exploration in 2.25 hours—a task requiring 320 days with Fluent.