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    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)
    Sardhara, Trushal
    ;
    Shkurmanov, Alexander
    ;
    Li, Yong
    ;
    Riedel, Lukas
    ;
    Shi, Shan
    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: Dataset
      82  73
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    Item type:Research Data, Access status: with files ,
    Datasets for structural and mechanical properties of nanoporous networks from FIB reconstruction
    (2025-07-30)
    Li, Yong
    This dataset includes 3D tomographic reconstruction image files, volume mesh files for finite element simulations, and data on the structural and mechanical properties of nanoporous gold (NPG) structures. It serves as a supplement to a dataset paper, with the corresponding DOI provided in the “Related Identifiers” section. Detailed descriptions of the data, as well as the procedures for their preparation and curation, are presented in that paper. The base material, nanoporous gold, was fabricated via a dealloying process and has a solid fraction of approximately 0.30. NPG samples with ligament sizes ranging from 20 nm to 400 nm were prepared through dealloying and subsequent thermal annealing. Tomographic TIFF files were obtained via Focused Ion Beam/Scanning Electron Microscopy (FIB/SEM) 3D reconstruction, with the procedure detailed in Philosophical Magazine (2016, 96(32–34), 3322–3335). Based on the 3D image data, new simulations and analyses were performed. The resulting structural and mechanical property data of nanoporous gold are reported for the first time in the dataset paper and are archived here. This dataset provides a valuable database for the study of nanoporous network materials and can be reused for numerical simulations, additive manufacturing, and machine learning applications within the materials science community.
    Data Type: Compiled Data
      514  1232
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    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)
    Sardhara, Trushal
    ;
    Aydin, Roland C.
    ;
    Li, Yong
    ;
    Piché, Nicolas
    ;
    Gauvin, Raynald
    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: Dataset
      251  324
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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)
    Sardhara, Trushal
    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 data
    Data Type: Dataset
      64  406
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    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)
    Sardhara, Trushal
    ;
    Shkurmanov, Alexander
    ;
    Li, Yong
    ;
    Shi, Shan
    ;
    Cyron, Christian J.
    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: Dataset
      110  59