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Item type:Research Data, Access status: with files , Research data - Generative adversarial networks for creating realistic training data for machine learning-based segmentation of FIB tomography data(2025-01-22)This dataset contains simulated and domain-adapted multi-voltage FIB tomography data on hierarchical nanoporous gold, trained machine learning model weights, and segmentation results of synthetic and real hierarchical nanoporous gold data. For more information, please refer to the published research article: Generative adversarial networks for creating realistic training data for machine learning-based segmentation of FIB tomography dataData Type: Dataset64 402 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Enhancing 3D reconstruction accuracy of multi-voltage FIB tomography images using multimodal machine learning(2023-12-13); ; ; ; This dataset contains simulated multi-voltage FIB tomography data of hierarchical nanoporous gold, trained machine learning model weights and segmentation results of synthetic and real hierarchical nanoporous gold data. For more information, please refer to the published research article: Enhancing 3D reconstruction accuracy of multi-voltage FIB tomography images using multimodal machine learning.Data Type: Dataset82 72 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Role of slice thickness quantification in the 3D reconstruction of FIB tomography data of nanoporous materials(2023-11-02); ; ; ; This dataset contains synthetic structures of hierarchical nanoporous gold, which mimics real hierarchical nanoporous gold structures, segmentation results and repositioned interpolated structures of the real hierarchical nanoporous gold dataset and trained weights of machine learning model used for interpolation. For more information, please refer to the published research article: Role of slice thickness quantification in the 3D reconstruction of FIB tomography data of nanoporous materials.Data Type: Dataset110 56 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , How Nanoporous Silicon-Polypyrrole Hybrids Flex Their Muscles in Aqueous Electrolytes: In Operando High-Resolution X-Ray Diffraction and Electron Tomography-Based Micromechanical Computer Simulations(2022-10-28); ; ; ; Dataset that contains different data on an in situ, combined electrochemical and x-ray diffraction investigation of the electrochemical actuation of a polypyrrole-filled porous silicon hybrid material. The pores of nanoporous silicon are filled by the electroactive polymer polypyrrole. An applied potential to the hybrid material immersed in an electrolyte solution leads to an incorporation of ions into the polymer and an expansion of the whole hybrid material. This electrochemical actuation is investigated by x-ray diffraction experiments, in which the lattice constant of the highly mono-crystalline porous silicon host material is studied with respect to the actuation. The dataset contains the electrochemical and the x-ray datasets. Moreover, a transmission electron microscopy tomography is performed on the polypyrrole-porous silicon hybrid material to obtain a three-dimensional model. This tomography reconstruction is included in the dataset. It can be transformed into an FEM model so that the actuation-induced strains and stresses in the complex porous silicon host structure can be studied and compared to the experimentally gained insights.Data Type: Dataset169 182 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Wafer-Scale Fabrication of Hierarchically Porous Silicon and Silica by Active Nanoparticle-Assisted Chemical Etching and Pseudomorphic Thermal OxidationDataset containing analysis on a hierarchical porous silicon structure. The structure consists of 1µm macropores hexagonally arranged and interconnected by mesopores in the range of 60nm. Included are a scanning electron micrograph of an overview of the macroporous structure which can be analysed to obtain structural properties such as the pore diameter, surface roughness, surface area and pore volume. Also included are reconstructed transmission electron microscopy and transmission x-ray microscopy tomography data to analyse the same structural parameters on smaller and larger length scale, respectively.Data Type: Dataset483 266 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Training deep neural networks to reconstruct nanoporous structures from FIB tomography images using synthetic training data(2022-02-02); ; ; ; This dataset contains simulated FIB tomography data of nanoporous/hierarchical nanoporous gold, synthetic FIB-SEM images of hierarchical nanoporous gold and segmentation results of real hierarchical nanoporous gold dataset. Abstract of the paper: Focused ion beam (FIB) tomography is a destructive technique used to collect three-dimensional (3D) structural information at a resolution of a few nanometers. For FIB tomography, a material is eroded in a layer-wise manner. After the erosion of each layer (whose thickness ranges on the nanometer scale), the current material surface is imaged by a scanning electron microscope. Especially for nanoporous materials, the reconstruction of the 3D microstructure of the material, from the information collected during FIB tomography, is impaired by the shine-through effect. This effect prevents a unique mapping between voxel intensity values and material phase (e.g., solid or void). It often substantially reduces the accuracy of conventional methods for image segmentation. Here we demonstrate how machine learning can be used to tackle this problem. A bottleneck in doing so is the availability of sufficient training data. To overcome this problem, we present a novel approach to generate synthetic FIB-SEM images using Monte Carlo simulations, which can be used as training data for machine learning. Based on this approach, we compare the performance of different machine learning architectures for segmenting FIB tomography data of nanoporous materials. We demonstrate that two-dimensional (2D) convolutional neural network (CNN) architectures with a group of adjacent slices as input data as well as 3D CNN perform best and can improve the segmentation performance by more than 100%.Data Type: Dataset251 321 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Giant Piezoelectrolytic Actuation in a Nanoporous Silicon-Polypyrrole Hybrid Material(2020-05-22); ; ; ; Data for the publication "Giant Piezoelectrolytic Actuation in a Nanoporous Silicon-Polypyrrole Hybrid Material" as well as a detailed description of the datasets.Data Type: Dataset508 198