Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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Dahl, Anders Bjorholm

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Technical University of Denmark

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (18/18 displayed)

  • 2023Elucidating the Bulk Morphology of Cellulose-Based Conducting Aerogels with X-Ray Microtomographycitations
  • 2023Elucidating the Bulk Morphology of Cellulose-Based Conducting Aerogels with X-Ray Microtomographycitations
  • 2022SparseMeshCNN with Self-Attention for Segmentation of Large Meshes1citations
  • 2021Quantifying effects of manufacturing methods on fiber orientation in unidirectional composites using structure tensor analysis38citations
  • 2020Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor14citations
  • 2019Process characterization for molding of paper bottles using computed tomography and structure tensor analysiscitations
  • 2017Individual fibre segmentation from 3D X-ray computed tomography for characterising the fibre orientation in unidirectional composite materials148citations
  • 2017Graphite nodules in fatigue-tested cast iron characterized in 2D and 3D12citations
  • 2015Dictionary Based Segmentation in Volumes4citations
  • 2015Characterization of boundary roughness of two cube grains in partly recrystallized copper1citations
  • 2015Boundary Fractal Analysis of Two Cube-oriented Grains in Partly Recrystallized Copper7citations
  • 2014Surface Detection using Round Cut5citations
  • 2014Pattern recognition approach to quantify the atomic structure of graphene4citations
  • 2014Structure Identification in High-Resolution Transmission Electron Microscopic Images6citations
  • 2014Characterization of graphite nodules in thick-walled ductile cast ironcitations
  • 2014Quantification Tools for Analyzing Tomograms of Energy Materialscitations
  • 2013Automated Structure Detection in HRTEM Images: An Example with Graphenecitations
  • 2012Large scale tracking of stem cells using sparse coding and coupled graphscitations

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Chart of shared publication
Dahl, Vedrana Andersen
7 / 10 shared
Mohammadi, Mohsen
2 / 14 shared
Dreier, Till
2 / 3 shared
Oikonomou, Vasileios K.
2 / 2 shared
Sandéhn, Alexandra
2 / 2 shared
Tybrandt, Klas
2 / 11 shared
Bech, Martin
2 / 7 shared
Christensen, Jakob Lønborg
1 / 1 shared
Stavrinidou, Eleni
2 / 9 shared
Hansen, Bjørn
1 / 1 shared
Ørkild, Thomas
1 / 1 shared
Lowes, Mathias
1 / 1 shared
Paulsen, Rasmus Reinhold
1 / 3 shared
Camara, Oscar
1 / 1 shared
Backer, Ole De
1 / 1 shared
Ingwersen, Christian Keilstrup
1 / 1 shared
Sørensen, Kristine Aavild
1 / 1 shared
Mikkelsen, Lars Pilgaard
3 / 71 shared
Christensen, Anders Nymark
2 / 2 shared
Jeppesen, N.
2 / 4 shared
Bissacco, Giuliano
1 / 28 shared
Trinderup, Camilla Himmelstrup
1 / 2 shared
Gundlach, Carsten
2 / 18 shared
Saxena, Prateek
1 / 10 shared
Jespersen, Kristine Munk
2 / 11 shared
Conradsen, Knut
3 / 4 shared
Emerson, Monica Jane
3 / 4 shared
Sturlason, Asger
1 / 2 shared
Hansen, Niels
1 / 18 shared
Mukherjee, Krishnendu
2 / 5 shared
Frandsen, Jens Ole
1 / 1 shared
Fæster, Søren
2 / 34 shared
Larsen, Rasmus
7 / 11 shared
Jørgensen, Peter Stanley
1 / 23 shared
Zhang, Yubin
2 / 46 shared
Juul Jensen, Dorte
2 / 47 shared
Sun, Jun
2 / 8 shared
Stenger, Nicolas
1 / 14 shared
Wagner, Jakob Birkedal
3 / 68 shared
Kling, Jens
3 / 8 shared
Bøggild, Peter
1 / 46 shared
Hansen, Thomas Willum
3 / 55 shared
Vestergaard, Jacob Schack
4 / 4 shared
Booth, Timothy
1 / 9 shared
Sturlason, A.
1 / 2 shared
Holm, Peter
1 / 1 shared
Chart of publication period
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Co-Authors (by relevance)

  • Dahl, Vedrana Andersen
  • Mohammadi, Mohsen
  • Dreier, Till
  • Oikonomou, Vasileios K.
  • Sandéhn, Alexandra
  • Tybrandt, Klas
  • Bech, Martin
  • Christensen, Jakob Lønborg
  • Stavrinidou, Eleni
  • Hansen, Bjørn
  • Ørkild, Thomas
  • Lowes, Mathias
  • Paulsen, Rasmus Reinhold
  • Camara, Oscar
  • Backer, Ole De
  • Ingwersen, Christian Keilstrup
  • Sørensen, Kristine Aavild
  • Mikkelsen, Lars Pilgaard
  • Christensen, Anders Nymark
  • Jeppesen, N.
  • Bissacco, Giuliano
  • Trinderup, Camilla Himmelstrup
  • Gundlach, Carsten
  • Saxena, Prateek
  • Jespersen, Kristine Munk
  • Conradsen, Knut
  • Emerson, Monica Jane
  • Sturlason, Asger
  • Hansen, Niels
  • Mukherjee, Krishnendu
  • Frandsen, Jens Ole
  • Fæster, Søren
  • Larsen, Rasmus
  • Jørgensen, Peter Stanley
  • Zhang, Yubin
  • Juul Jensen, Dorte
  • Sun, Jun
  • Stenger, Nicolas
  • Wagner, Jakob Birkedal
  • Kling, Jens
  • Bøggild, Peter
  • Hansen, Thomas Willum
  • Vestergaard, Jacob Schack
  • Booth, Timothy
  • Sturlason, A.
  • Holm, Peter
OrganizationsLocationPeople

document

Automated Structure Detection in HRTEM Images: An Example with Graphene

  • Wagner, Jakob Birkedal
  • Kling, Jens
  • Larsen, Rasmus
  • Dahl, Anders Bjorholm
  • Hansen, Thomas Willum
  • Vestergaard, Jacob Schack
Abstract

Graphene, as the forefather of 2D-materials, attracts much attention due to its extraordinary properties like transparency, flexibility and outstanding high conductivity, together with a thickness of only one atom. The properties seem to be dependent on the atomic structure of graphene and therefore characterizations on the atomic level are of interest. High-resolution transmission electron microscopy (HRTEM) is a state-of-the-art method to characterize the atomic structure of materials. Due to the inherently low mass-thickness of graphene, the contrast levels in the recorded images are often challenging to interpret. In order to increase the signal-to-noise ratio of the images two routes can be pursued: 1) the exposure time can be increased; or 2) acquiring series of images and summarize them after alignment. Both methods have the disadvantage of summing images acquired over a certain period of time making it difficult to resolve dynamic processes or unstable structures. Tools that assist to get the maximum of information out of recorded images are therefore greatly appreciated.<br/><br/>In order to get the most accurate results out of the structure detection, we have optimized the imaging conditions used for the FEI Titan ETEM with a monochromator and an objective-lens Cs-corrector. To reduce the knock-on damage of the carbon atoms in the graphene structure, the microscope was operated at 80kV. As this strongly increases the influence of the chromatic aberration of the lenses, the energy spread of the electron gun was reduced. Using the monochromator an energy spread of &lt;0.2eV can be achieved. This gives a resolution better then 1.2Å which allow us to resolve the second order reflection of graphene and to visualize the atomic structure in HRTEM (fig. 1).<br/><br/>These images serve as a basis for the image analysis. Single-layer graphene with its regular honeycomb lattice is a perfect model structure to apply automated structure detection. By utilizing Fourier analysis the initial perfect hexagonal structure can easily be recognized. The recorded hexagonal tessellation reflects the unperturbed structure in the image. The centers of the C-hexagons are displayed as nodes. To segment the image into “pure” and “impure” regions, like areas with residual amorphous contamination or defects e.g. holes, a sliding window approach is used. The magnitude of the Fourier transformation within a window is compared to that of a perfect hexagonal tessellation. Areas where this relation exceeds a threshold are recognized as “impure” and a mask is created. As a result, the hexagonal tessellation overlays only the “pure” graphene structure in the image.<br/><br/>As the real graphene structure is never perfect and undisturbed, at least at a length-scale of several nm, the model structure has to be adjusted to the real structure. At this point, the image quality plays a crucial role. The algorithm assumes that irregularities in the graphene can be explained by a deformation in the xy-plane. To model this, a set of tensor B-splines is employed, which is deformed by matching model grid points with the C-hexagon centers. Dependent on the Cs and defocus-settings during microscopy these centers appear either dark or bright. One ends up with a deformed hexagonal tessellation, which can easily be transformed into a honeycomb lattice with the C-atom positions included. As the microstructure is now available in the model, information like the C-C distance can be visualized as shown in fig. 2.<br/><br/>Applying this method, the perfect graphene structure in recorded HRTEM-images can be determined fast and accurate over a wide length scale, and at the same time lattice deformations can be visualized. The method will be refined to facilitate the detection of larger defects like holes and the determination of the edge terminations.

Topics
  • impedance spectroscopy
  • microstructure
  • amorphous
  • Carbon
  • transmission electron microscopy
  • defect