Dataset Information

3D Magnetic Induction Maps of Nanoscale Materials Revealed by Electron Holographic Tomography.

ABSTRACT: The investigation of three-dimensional (3D) ferromagnetic nanoscale materials constitutes one of the key research areas of the current magnetism roadmap and carries great potential to impact areas such as data storage, sensing, and biomagnetism. The properties of such nanostructures are closely connected with their 3D magnetic nanostructure, making their determination highly valuable. Up to now, quantitative 3D maps providing both the internal magnetic and electric configuration of the same specimen with high spatial resolution are missing. Here, we demonstrate the quantitative 3D reconstruction of the dominant axial component of the magnetic induction and electrostatic potential within a cobalt nanowire (NW) of 100 nm in diameter with spatial resolution below 10 nm by applying electron holographic tomography. The tomogram was obtained using a dedicated TEM sample holder for acquisition, in combination with advanced alignment and tomographic reconstruction routines. The powerful approach presented here is widely applicable to a broad range of 3D magnetic nanostructures and may trigger the progress of novel spintronic nonplanar nanodevices.

SUBMITTER: Wolf D

PROVIDER: S-EPMC4862384 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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3D Magnetic Induction Maps of Nanoscale Materials Revealed by Electron Holographic Tomography.

Wolf Daniel D Rodriguez Luis A LA Béché Armand A Javon Elsa E Serrano Luis L Magen Cesar C Gatel Christophe C Lubk Axel A Lichte Hannes H Bals Sara S Van Tendeloo Gustaaf G Fernández-Pacheco Amalio A De Teresa José M JM Snoeck Etienne E

Chemistry of materials : a publication of the American Chemical Society 20150908 19

The investigation of three-dimensional (3D) ferromagnetic nanoscale materials constitutes one of the key research areas of the current magnetism roadmap and carries great potential to impact areas such as data storage, sensing, and biomagnetism. The properties of such nanostructures are closely connected with their 3D magnetic nanostructure, making their determination highly valuable. Up to now, quantitative 3D maps providing both the internal magnetic and electric configuration of the same spec ...[more]

PMID: 27182110

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Project description:BackgroundAs an extension of electron tomography (ET), serial section electron tomography (serial section ET) aims to align the tomographic images of multiple thick tissue sections together, to break through the volume limitation of the single section and preserve the sub-nanoscale voxel size. It could be applied to reconstruct the intact synapse, which expands about one micrometer and contains nanoscale vesicles. However, there are several drawbacks of the existing serial section ET methods. First, locating and imaging regions of interest (ROIs) in serial sections during the shooting process is time-consuming. Second, the alignment of ET volumes is difficult due to the missing information caused by section cutting and imaging. Here we report a workflow to simplify the acquisition of ROIs in serial sections, automatically align the volume of serial section ET, and semi-automatically reconstruct the target synaptic structure.ResultsWe propose an intelligent workflow to reconstruct the intact synapse with sub-nanometer voxel size. Our workflow includes rapid localization of ROIs in serial sections, automatic alignment, restoration, assembly of serial ET volumes, and semi-automatic target structure segmentation. For the localization and acquisition of ROIs in serial sections, we use affine transformations to calculate their approximate position based on their relative location in orderly placed sections. For the alignment of consecutive ET volumes with significantly distinct appearances, we use multi-scale image feature matching and the elastic with belief propagation (BP-Elastic) algorithm to align them from coarse to fine. For the restoration of the missing information in ET, we first estimate the number of lost images based on the pixel changes of adjacent volumes after alignment. Then, we present a missing information generation network that is appropriate for small-sample of ET volume using pre-training interpolation network and distillation learning. And we use it to generate the missing information to achieve the whole volume reconstruction. For the reconstruction of synaptic ultrastructures, we use a 3D neural network to obtain them quickly. In summary, our workflow can quickly locate and acquire ROIs in serial sections, automatically align, restore, assemble serial sections, and obtain the complete segmentation result of the target structure with minimal manual manipulation. Multiple intact synapses in wild-type rat were reconstructed at a voxel size of 0.664 nm/voxel to demonstrate the effectiveness of our workflow.ConclusionsOur workflow contributes to obtaining intact synaptic structures at the sub-nanometer scale through serial section ET, which contains rapid ROI locating, automatic alignment, volume reconstruction, and semi-automatic synapse reconstruction. We have open-sourced the relevant code in our workflow, so it is easy to apply it to other labs and obtain complete 3D ultrastructures which size is similar to intact synapses with sub-nanometer voxel size.

Dataset Information

3D Magnetic Induction Maps of Nanoscale Materials Revealed by Electron Holographic Tomography.

Publications

3D Magnetic Induction Maps of Nanoscale Materials Revealed by Electron Holographic Tomography.

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