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Holographic vector field electron tomography of three-dimensional nanomagnets

2019, Wolf, D., Biziere, N., Sturm, S., Reyes, D., Wade, T., Niermann, T., Krehl, J., Warot-Fonrose, B., Büchner, B., Snoeck, E., Gatel, C., Lubk, A.

Complex 3D magnetic textures in nanomagnets exhibit rich physical properties, e.g., in their dynamic interaction with external fields and currents, and play an increasing role for current technological challenges such as energy-efficient memory devices. To study these magnetic nanostructures including their dependency on geometry, composition, and crystallinity, a 3D characterization of the magnetic field with nanometer spatial resolution is indispensable. Here we show how holographic vector field electron tomography can reconstruct all three components of magnetic induction as well as the electrostatic potential of a Co/Cu nanowire with sub 10 nm spatial resolution. We address the workflow from acquisition, via image alignment to holographic and tomographic reconstruction. Combining the obtained tomographic data with micromagnetic considerations, we derive local key magnetic characteristics, such as magnetization current or exchange stiffness, and demonstrate how magnetization configurations, such as vortex states in the Co-disks, depend on small structural variations of the as-grown nanowire.

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Communication: X-ray coherent diffractive imaging by immersion in nanodroplets

2015, Tanyag, Rico Mayro P., Bernando, Charles, Jones, Curtis F., Bacellar, Camila, Ferguson, Ken R., Anielski, Denis, Boll, Rebecca, Carron, Sebastian, Cryan, James P., Englert, Lars, Epp, Sascha W., Erk, Benjamin, Foucar, Lutz, Gomez, Luis F., Hartmann, Robert, Neumark, Daniel M., Rolles, Daniel, Rudek, Benedikt, Rudenko, Artem, Siefermann, Katrin R., Ullrich, Joachim, Weise, Fabian, Bostedt, Christoph, Gessner, Oliver, Vilesov, Andrey F.

Lensless x-ray microscopy requires the recovery of the phase of the radiation scattered from a specimen. Here, we demonstrate a de novo phase retrieval technique by encapsulating an object in a superfluid helium nanodroplet, which provides both a physical support and an approximate scattering phase for the iterative image reconstruction. The technique is robust, fast-converging, and yields the complex density of the immersed object. Images of xenon clusters embedded in superfluid helium droplets reveal transient configurations of quantum vortices in this fragile system.

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Successful optimization of reconstruction parameters in structured illumination microscopy

2019, Karras, Christian, Smedh, Maria, Förster, Ronny, Deschout, Hendrik, Fernandez-Rodriguez, Julia, Heintzmann, Rainer

The impact of the different reconstruction parameters in super-resolution structured illumination microscopy (SIM) on image artifacts is carefully analyzed. These parameters comprise the Wiener filter parameter, an apodization function, zero-frequency suppression and modifications of the optical transfer function. A detailed investigation of the reconstructed image spectrum is concluded to be suitable for identifying artifacts. For this purpose, two samples, an artificial test slide and a more realistic biological system, were used to characterize the artifact classes and their correlation with the image spectra as well as the reconstruction parameters. In addition, a guideline for efficient parameter optimization is suggested and the implementation of the parameters in selected up-to-date processing packages (proprietary and open-source) is depicted. © 2018 The Authors

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Classification and quantification of pore shapes in sandstone reservoir rocks with 3-D X-ray micro-computed tomography

2016, Schmitt, Mayka, Halisch, Matthias, Müller, Cornelia, Fernandes, Celso Peres

Recent years have seen a growing interest in the characterization of the pore morphologies of reservoir rocks and how the spatial organization of pore traits affects the macro behavior of rock–fluid systems. With the availability of 3-D high-resolution imaging, such as x-ray micro-computed tomography (µ-CT), the detailed quantification of particle shapes has been facilitated by progress in computer science. Here, we show how the shapes of irregular rock particles (pores) can be classified and quantified based on binary 3-D images. The methodology requires the measurement of basic 3-D particle descriptors (length, width, and thickness) and a shape classification that involves the similarity of artificial objects, which is based on main pore network detachments and 3-D sample sizes. Two main pore components were identified from the analyzed volumes: pore networks and residual pore ganglia. A watershed algorithm was applied to preserve the pore morphology after separating the main pore networks, which is essential for the pore shape characterization. The results were validated for three sandstones (S1, S2, and S3) from distinct reservoirs, and most of the pore shapes were found to be plate- and cube-like, ranging from 39.49 to 50.94 % and from 58.80 to 45.18 % when the Feret caliper descriptor was investigated in a 10003 voxel volume. Furthermore, this study generalizes a practical way to correlate specific particle shapes, such as rods, blades, cuboids, plates, and cubes to characterize asymmetric particles of any material type with 3-D image analysis.