This README_Supplementary_Data.txt file was generated on 2026-03-03 by Lara Gibowsky (lara.gibowsky@tuhh.de) ------------------- GENERAL INFORMATION ------------------- Name: Lara Gibowsky (ORCID: 0000-0001-6359-9426) Role/Function: Creator, Data Collector, Data Curator (main contact person) Institution: Institute of Thermal Separation Processes, Hamburg University of Technology Address: Eißendorfer Straße 38, 21073 Hamburg, Germany Email: lara.gibowsky@tuhh.de Name: Lukas Maier (ORCID: 0009-0002-0262-8208) Role/Function: Data Collector, Data Curator (alternative contact person) Institution: Institute of Process and Particle Engineering, Graz University of Technology Address: Inffeldgasse 13/III, Graz, 8010, Austria Name: Nanning Jaeschke (ORCID: 0009-0000-0369-3568) Role/Function: Contributing Researcher Institution: Institute of Thermal Separation Processes, Hamburg University of Technology Address: Eißendorfer Straße 38, 21073 Hamburg, Germany Name: Irina Smirnova (ORCID: 0000-0003-4503-4039) Role/Function: Principal Investigator Institution: Institute of Thermal Separation Processes, Hamburg University of Technology Address: Eißendorfer Straße 38, 21073 Hamburg, Germany Name: Stefan Radl (ORCID: 0000-0002-0738-0961) Role/Function: Principal Investigator, Contact person (alternative contact person) Institution: Institute of Process and Particle Engineering, Graz University of Technology Address: Inffeldgasse 13/III, Graz, 8010, Austria Email: radl@tugraz.at Name: Pavel Gurikov (ORCID: 0000-0003-0598-243X) Role/Function: Principal Investigator, Contact Person (alternative contact person) Institution: Institute of Thermal Separation Processes, Hamburg University of Technology Address: Eißendorfer Straße 38, 21073 Hamburg, Germany Email: pavel.gurikov@tuhh.de Date of data collection: September 2024 - May 2026 Location of data collection: Institute of Thermal Separation Processes, Hamburg University of Technology, Hamburg, Germany Funding: This project is partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – GRK 2462 (PintPFS), project number (572006271) and SFB 1615 (503850735). --------------------------- SHARING/ACCESS INFORMATION --------------------------- Title of data set: "Dataset on Hydrogel Particles Under Uniaxial Compression: Experiments and Tetrapod-Based DEM Modeling" DOI of data set: https://doi.org/10.15480/882.16495 Handle: https://hdl.handle.net/11420/60920 Keywords: hydrogels, hydrogel mechanics, DEM simulation, tetrapods, uniaxial compression, multiphysics simulation, structure-property correlation --------------------- DATA & FILE OVERVIEW --------------------- ### 1. basecase.zip (creation date: 02.03.2026, version: 01) - **Description:** This folder contains a dummy simulation setup for a granular hydrogel compaction process, intended as supplementary information for a publication. It demonstrates the simulation workflow using LIGGGHTS. - ## Author Information: **Author:** Lukas Maier **Affiliation:** Institute of Process and Particle Engineering, TU Graz **License:** LGPL 3.0 - ## Directory Structure - `Allrun.sh`: Main execution script. - `system/`: Contains LIGGGHTS input scripts (`.in` files). - `scripts/`: Contains Python post-processing and extraction scripts. - `in/`: Contains parameter files utilized by the LIGGGHTS scripts. - `data/`: Contains data and configuration files. - `mesh/`: Contains mesh geometries (STL files). - ## Usage To run the complete simulation workflow, execute the `Allrun.sh` script: ```bash ./Allrun.sh ``` - ## Dependencies - LIGGGHTS (LAMMPS for granular materials) - MPI (for parallel execution) - Python 3 ### 2. final_simulation_results.zip (creation date: 03.03.2026, version: 01) - **Description:** This folder contains all simulation results generated by the tetrapod-based DEM simulations. - **Folder/files:** - damping - 2025-10-23_LoadingUnloading_full_dataset_3mms.csv: Cyclic loading results (full dataset) at a compression rate of 3 mm/s: stress–strain data for 10 simulated particles. - 2025-10-23_LoadingUnloading_loading_dataset_3mms.csv: Cyclic loading results (only loading part) at a compression rate of 3 mm/s: stress–strain data for 10 simulated particles. - 2025-10-23_LoadingUnloading_unloading_dataset_3mms.csv: Cyclic loading results (only unloading part) at a compression rate of 3 mm/s: stress–strain data for 10 simulated particles. - 2025-11-10_Relaxation.csv: Stress-relaxation results after a compression until 30% strain: time-force data for 1 simulated particle. - 2025-11-21_LoadingUnloading_full_dataset_03mms.csv: Cyclic loading results (full dataset) at a compression rate of 0.3 mm/s: stress–strain data for 10 simulated particles. - 2025-11-21_LoadingUnloading_loading_dataset_03mms.csv: Cyclic loading results (only loading part) at a compression rate of 0.3 mm/s: stress–strain data for 10 simulated particles. - 2025-11-21_LoadingUnloading_unloading_dataset_03mms.csv: Cyclic loading results (only unloading part) at a compression rate of 0.3 mm/s: stress–strain data for 10 simulated particles. - flexible_pods: Comparison of simulation with flexible tetrapod structure that can change its distance from the center of mass: strain-force data of one simulation. - 2025-10-28_flex_1e0.csv - 2025-10-28_flex_1e-1.csv - 2025-10-28_flex_1e-2.csv - 2025-10-28_flex_1e-3.csv - 2025-10-28_flex_1e-4.csv - 2025-10-28_flex_1e-5.csv - 2025-10-28_flex_1e-7.csv - 2025-10-28_flex_1e-9.csv - softer_pods: Comparison with soft tetrapods: simulation results using tetrapod parcels in which the four constituent particles in one parcel are assigned a lower Young’s modulus than in the standard simulations. - 2025-11-03_Y_1e7.csv - 2025-11-03_Y_1e6.csv - validation - 2025-10-23_1mms.csv: Validation of the tetrapod simulation for uniaxial compression of a hydrogel particle at 1 mm/s: force–strain data for one simulated particle. - 2025-10-27_05mms.csv: Validation of the tetrapod simulation for uniaxial compression of a hydrogel particle at 0.5 mm/s: force–strain data for one simulated particle. - 2025-11-06_03mms.csv: Validation of the tetrapod simulation for uniaxial compression of a hydrogel particle at 0.3 mm/s: force–strain data for one simulated particle. - 2025-09-19_optimized.csv: Optimized parameter set for tetrapod-based DEM modeling of uniaxial compression of a hydrogel particle at 3 mm/s: force–strain data for one simulated particle. - 2025-10-23_Uncertainty_Quantification.csv: Stochastic variation of 10 packings captures variability observed in the experiments: Strain-force data for 10 simulated particle grids. - 2025-10-23_With_spheres.csv: Comparison of tetrapod approach with common DEM particle approach: strain-force data for one simulated particle. ### 3. Videos.zip (creation date: 12.05.2026, version: 01) - **Description:** Example videos of the uniaxial compression of single particles (experiments or tetrapod-based DEM simulations), used for optical determination of Poisson’s ratio. - **Folder:** - experiments: Videos of the uniaxial compression experiments on single particles at varying compression rates: - 0.1mms.MP4 - 0.15mms.MP4 - 0.3mms.MP4 - 0.5mms.MP4 - 1mms.MP4 - 3mms.MP4 - Hydrogel_Small_Piston.MP4 - simulations: Videos of the uniaxial compression simulations on single particles at varying compression rates (generated with Paraview): - Simu_0.3mms.mp4 - Simu_0.5mms.mp4 - Simu_1mms.mp4 - Simu_3mms.mp4 ### 4. optimized.tar.gz (creation date: 03.03.2026, version: 01) - **Description:** Example simulation results (without post processing) for the optimized parameter set at a compression rate of 3 mm/s. - **Folder:** - equivalent_spheres: LIGGGHTS files generated during the simulation of a uniaxial compression of a hydrogel particle: Information about the tetrapod positions during the simulation. - post_forces: LIGGGHTS files generated during the simulation of a uniaxial compression of a hydrogel particle: Information about the forces occurring during the simulation - post-sphere: LIGGGHTS files generated during the simulation of a uniaxial compression of a hydrogel particle: Information about the mesoparticle positions during the simulation. - processed_data: strain_force_data.csv generated during the simulation. - parameter.in: Contains parameter files utilized by the LIGGGHTS scripts. ### 5. final_experiment_results.zip (creation date: 12.05.2026, version: 01) - **Description:** All experimental results generated for this study. - **Folder:** - ageing: - ageing_3_mm_s.csv: Averaged force–strain data (mean ± standard deviation) from uniaxial compression tests performed 1, 282, 297, and 351 days after gel synthesis (N = 10 hydrogel particles). - cyclic_loading: - cyclic_loading_0.3_mm_s.csv: Averaged force–strain data (mean ± standard deviation) from cyclic compression tests at 0.3 mm/s for loading to 20%, 30%, 40%, and 60% strain (N = 10 hydrogel particles). - cyclic_loading_3_mm_s.csv: Averaged force–strain data (mean ± standard deviation) from cyclic compression tests at 3 mm/s for loading to 20%, 30%, 40%, and 60% strain (N = 10 hydrogel particles). - apparent_rate_dependence_compression: Averaged force–strain data (mean ± standard deviation) from uniaxial compression tests conducted across multiple compression rates (N = 10 hydrogel particles), including hydrogel-specific properties such as diameter, roundness, alginate concentration, skeletal density of the alginate network, hydrogel density, compression rate, apparent Young’s modulus, and apparent Poisson’s ratio. - compression_rate_0.1_mm_s.csv - compression_rate_0.15_mm_s.csv - compression_rate_0.3_mm_s.csv - compression_rate_0.5_mm_s.csv - compression_rate_1_mm_s.csv - compression_rate_3_mm_s.csv ### 6. post_processing.zip (creation date: 12.05.2026, version: 01) - **Description:** Python scripts used for post processing. - **Files:** - E-Modul_Particles.txt: Python script for evaluating trimmed force–displacement data, including Hertz-theory fitting to determine the Young’s modulus from single-particle compression tests. - Running_average_cutting.txt: Python script to edit and trim the raw force–displacement data from the Texture Analyser, including detection of the contact point (start of compression) and determination of the initial particle height. - Hydrogel_Shape_Analysis_Code: Python script for determination of the lower particle contour, fitting left and right parabolas, and exporting frame-by-frame geometry measurements. An additional detailed read-me with the description of all files and subfolders is provided in the folder. --------------------------- METHODOLOGICAL INFORMATION --------------------------- The methodology of the distinct analysis. 1. Gel synthesis: A 2 wt.% sodium alginate solution (G/M =0.57) was prepared in deionized water by overhead stirring for 2 h followed by 1 h ultrasonication to ensure complete homogenization. The solution was stored overnight at 4 ∘C to degas and standardize conditions, minimizing voids and other structural defects in the hydrogel matrix. Gelation was achieved via ionic crosslinking by dripping the alginate solution (spider dripping setup; 0.7 mm inner-diameter needles, 12.0 cm drop height) into a 2 wt.% aqueous CaCl2 bath and stirring the beads overnight to ensure complete gelation, yielding homogeneous spherical particles of ≈2 mm diameter. The hydrogel beads were then stored in sealed containers with fresh deionized water at 4 ∘C until use to minimize contamination and aging. 2. Aerogel production: The hydrogels were converted to aerogels via a standard supercritical CO2 drying protocol. Water was first replaced by an organic solvent (typically ethanol), after which samples were sealed in filter paper bags and dried in a 3.9 L autoclave at 12 MPa and 60 ∘C with a continuous CO2 flow of ≈300 g/min until ethanol was fully extracted (≈2 h). 3. SEM pictures: Prior to SEM analysis, aerogels were sputter-coated with a conductive gold layer (≈6 nm). SEM imaging was performed under high vacuum with an InLens detector at 3 kV (100,000×, working distance 5.6 mm). 4. BET analysis: Low-temperature N2 physisorption was performed to characterize the microstructure of supercritically dried samples. The samples (20–30 mg) were degassed under vacuum at 105 ∘C for ≈6 h, and the mass-specific surface area was calculated by the BET method from three replicate measurements. 5. Density measurement: The hydrogel particle density was calculated from the solid content, avoiding geometric errors from non-ideal sphericity of millimeter-sized beads. The solid content was obtained by drying ≈6.5 g hydrogel at 105 ∘C overnight (triplicate), the alginate skeleton density was measured by helium pycnometry (n=8), and water density was set to 997 kg/m3 at 20 ∘C. 6. Mechanical properties: Uniaxial compression tests were performed on hydrogel particles using a texture analyzer (Stable Micro Systems; force/displacement resolution ≈1 mN/≈1 μm; 500 Hz) up to 100 N, with probe speeds 0.10–3.00 mm/s (pre-speed 0.20 mm/s); contact was identified in post-processing via a Python running-average force criterion and each condition was repeated n=10 (total 60 particles). Particle diameter, apparent Poisson’s ratio (3 videos per condition), and circularity (ImageJ; 50 particles) were determined from the initial position and image analysis. 7. Tetrapod simulations: All data and analysis code (including explanations and example workflows) are available in the GitHub repository: https://gitlab.tugraz.at/13097018C9D61E3C/LIGGGHTS-PUBLIC/tree/dev-Lukas_contactModel ----------------- RESEARCH CONTEXT ----------------- This study provides experimental datasets for biopolymer-based hydrogel particles under uniaxial compression, including monotonic and cyclic loading as well as stress-relaxation tests across multiple compression rates. It also includes the corresponding tetrapod-based discrete element method (DEM) simulations, together with simulation outputs and calibrated parameters, enabling direct experiment–simulation comparison. Accordingly, the work is divided into two main parts: (1) hydrogel particle characterization to obtain the required input parameters, and (2) tetrapod-based DEM simulations, comprising (a) parameter optimization and (b) validation across multiple compression rates.