Publications

1.
Liehr, A., Dingel, K., Kottke, D., Degener, S., Meier, D., Sick, B., Niendorf, T.: Data selection strategies for minimizing measurement time in materials characterization. Scientific Reports. 15, 1–12 (2025). https://doi.org/10.1038/S41598-025-96221-1.
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Every new material needs to be assessed and qualified for an envisaged application. A steadily increasing number of new alloys, designed to address challenges in terms of reliability and sustainability, poses significant demands on well-known analysis methods in terms of their efficiency, e.g., in X-ray diffraction analysis. Particularly in laboratory measurements, where the intensities in diffraction experiments tend to be low, a possibility to adapt the exposure time to the prevailing boundary conditions, i.e., the investigated microstructure, is seen to be a very effective approach. The counting time is decisive for, e.g., complex texture, phase, and residual stress measurements. Traditionally, more measurement points and, thus, longer data collection times lead to more accurate information. Here, too short counting times result in poor signal-to-background ratios and dominant signal noise, respectively, rendering subsequent evaluation more difficult or even impossible. Then, it is necessary to repeat experiments with adjusted, usually significantly longer counting time. To prevent redundant measurements, it is state-of-the-art to always consider the entire measurement range, regardless of whether the investigated points are relevant and contribute to the subsequent materials characterization, respectively. Obviously, this kind of approach is extremely time-consuming and, eventually, not efficient. The present study highlights that specific selection strategies, taking into account the prevailing microstructure of the alloy in focus, can decrease counting times in X-ray energy dispersive diffraction experiments without any detrimental effect on data quality for the subsequent analysis. All relevant data, including the code, are carefully assessed and will be the basis for a widely adapted strategy enabling efficient measurements not only in lab environments but also in large-scale facilities.
@article{Liehr2025, abstract = {Every new material needs to be assessed and qualified for an envisaged application. A steadily increasing number of new alloys, designed to address challenges in terms of reliability and sustainability, poses significant demands on well-known analysis methods in terms of their efficiency, e.g., in X-ray diffraction analysis. Particularly in laboratory measurements, where the intensities in diffraction experiments tend to be low, a possibility to adapt the exposure time to the prevailing boundary conditions, i.e., the investigated microstructure, is seen to be a very effective approach. The counting time is decisive for, e.g., complex texture, phase, and residual stress measurements. Traditionally, more measurement points and, thus, longer data collection times lead to more accurate information. Here, too short counting times result in poor signal-to-background ratios and dominant signal noise, respectively, rendering subsequent evaluation more difficult or even impossible. Then, it is necessary to repeat experiments with adjusted, usually significantly longer counting time. To prevent redundant measurements, it is state-of-the-art to always consider the entire measurement range, regardless of whether the investigated points are relevant and contribute to the subsequent materials characterization, respectively. Obviously, this kind of approach is extremely time-consuming and, eventually, not efficient. The present study highlights that specific selection strategies, taking into account the prevailing microstructure of the alloy in focus, can decrease counting times in X-ray energy dispersive diffraction experiments without any detrimental effect on data quality for the subsequent analysis. All relevant data, including the code, are carefully assessed and will be the basis for a widely adapted strategy enabling efficient measurements not only in lab environments but also in large-scale facilities.}, author = {Liehr, Alexander and Dingel, Kristina and Kottke, Daniel and Degener, Sebastian and Meier, David and Sick, Bernhard and Niendorf, Thomas}, journal = {Scientific Reports}, keywords = {aimed}, month = 12, number = 1, pages = {1-12}, publisher = {Nature Research}, title = {Data selection strategies for minimizing measurement time in materials characterization}, volume = 15, year = 2025 }
1.
Meier, D., Viefhaus, J., Hartmann, G., Helml, W., Otto, T., Sick, B.: Reconstructing time-of-flight detector values of angular streaking using machine learning. Physical Review Accelerators and Beams. 28, 074601 (2025). https://doi.org/10.1103/csvm-858f.
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Angular streaking experiments enable for experimentation in the attosecond regions. However, the deployed time-of-flight (TOF) detectors are susceptible to noise and failure. These shortcomings make the outputs of the TOF detectors hard to understand for humans and further processing, such as, for example, the extraction of beam properties. In this article, we present an approach to remove high noise levels and reconstruct up to three failed TOF detectors from an arrangement of 16 TOF detectors. Due to its fast evaluation time, the presented method is applicable online during a running experiment. It is trained with simulation data, and we show the results of denoising and reconstruction of our method on real-world experiment data.
@article{Meier2025, abstract = {Angular streaking experiments enable for experimentation in the attosecond regions. However, the deployed time-of-flight (TOF) detectors are susceptible to noise and failure. These shortcomings make the outputs of the TOF detectors hard to understand for humans and further processing, such as, for example, the extraction of beam properties. In this article, we present an approach to remove high noise levels and reconstruct up to three failed TOF detectors from an arrangement of 16 TOF detectors. Due to its fast evaluation time, the presented method is applicable online during a running experiment. It is trained with simulation data, and we show the results of denoising and reconstruction of our method on real-world experiment data.}, author = {Meier, David and Viefhaus, Jens and Hartmann, Gregor and Helml, Wolfram and Otto, Thorsten and Sick, Bernhard}, journal = {Physical Review Accelerators and Beams}, keywords = {aimed}, month = {07}, number = 7, pages = {074601}, publisher = {American Physical Society}, title = {Reconstructing time-of-flight detector values of angular streaking using machine learning}, volume = 28, year = 2025 }
1.
Meier, D., Zeschke, T., Feuer-Forson, P., Sick, B., Viefhaus, J., Hartmann, G.: Offset Finding of Beamline Parameters on the METRIXS Beamline at BESSY II Using Machine Learning. Journal of Physics: Conference Series. 3010, 012130 (2025). https://doi.org/10.1088/1742-6596/3010/1/012130.
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Beamline alignment is challenging as the beamline components must be set up ideally so that the rays follow the desired optical path. Automated methods using a digital twin allow for faster diagnostics and improved beam properties compared to manual tuning. We introduce an automated method of finding the offsets to improve this digital twin model. These offsets represent the unknown but constant differences between the beamline parameter positions as set up at the physical beamline and the corresponding parameter positions of its digital twin. Our method assumes the capability to execute precise relative movements with a known step size for these parameters, although the absolute position information is unknown. By combining the surrogate model with a global optimizer, we successfully determine offsets for 34 beamline parameters on a simulated METRIXS beamline at the BESSY II synchrotron radiation source in Berlin.
@article{Meier2025, abstract = {Beamline alignment is challenging as the beamline components must be set up ideally so that the rays follow the desired optical path. Automated methods using a digital twin allow for faster diagnostics and improved beam properties compared to manual tuning. We introduce an automated method of finding the offsets to improve this digital twin model. These offsets represent the unknown but constant differences between the beamline parameter positions as set up at the physical beamline and the corresponding parameter positions of its digital twin. Our method assumes the capability to execute precise relative movements with a known step size for these parameters, although the absolute position information is unknown. By combining the surrogate model with a global optimizer, we successfully determine offsets for 34 beamline parameters on a simulated METRIXS beamline at the BESSY II synchrotron radiation source in Berlin.}, author = {Meier, David and Zeschke, Thomas and Feuer-Forson, Peter and Sick, Bernhard and Viefhaus, Jens and Hartmann, Gregor}, journal = {Journal of Physics: Conference Series}, keywords = {aimed}, month = {05}, number = 1, pages = {012130}, publisher = {IOP Publishing}, title = {Offset Finding of Beamline Parameters on the METRIXS Beamline at BESSY II Using Machine Learning}, volume = 3010, year = 2025 }
1.
Elsaka, B., Yang, X., Kästner, P., Dingel, K., Sick, B., Lehmann, P., Buhmann, S.Y., Hillmer, H.: Casimir Effect in MEMS: Materials, Geometries, and Metrologies—A Review. Materials 2024, Vol. 17, Page 3393. 17, 3393 (2024). https://doi.org/10.3390/MA17143393.
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Casimir force densities, i.e., force per area, become very large if two solid material surfaces come closer together to each other than 10 nm. In most cases, the forces are attractive. In some cases, they can be repulsive depending on the solid materials and the fluid medium in between. This review provides an overview of experimental and theoretical studies that have been performed and focuses on four main aspects: (i) the combinations of different materials, (ii) the considered geometries, (iii) the applied experimental measurement methodologies and (iv) a novel self-assembly methodology based on Casimir forces. Briefly reviewed is also the influence of additional parameters such as temperature, conductivity, and surface roughness. The Casimir effect opens many application possibilities in microelectromechanical systems (MEMS) and nanoelectromechanical systems (NEMS), where an overview is also provided. The knowledge generation in this fascinating field requires interdisciplinary approaches to generate synergetic effects between technological fabrication metrology, theoretical simulations, the establishment of adequate models, artificial intelligence, and machine learning. Finally, multiple applications are addressed as a research roadmap.
@article{Elsaka2024, abstract = {Casimir force densities, i.e., force per area, become very large if two solid material surfaces come closer together to each other than 10 nm. In most cases, the forces are attractive. In some cases, they can be repulsive depending on the solid materials and the fluid medium in between. This review provides an overview of experimental and theoretical studies that have been performed and focuses on four main aspects: (i) the combinations of different materials, (ii) the considered geometries, (iii) the applied experimental measurement methodologies and (iv) a novel self-assembly methodology based on Casimir forces. Briefly reviewed is also the influence of additional parameters such as temperature, conductivity, and surface roughness. The Casimir effect opens many application possibilities in microelectromechanical systems (MEMS) and nanoelectromechanical systems (NEMS), where an overview is also provided. The knowledge generation in this fascinating field requires interdisciplinary approaches to generate synergetic effects between technological fabrication metrology, theoretical simulations, the establishment of adequate models, artificial intelligence, and machine learning. Finally, multiple applications are addressed as a research roadmap.}, author = {Elsaka, Basma and Yang, Xiaohui and Kästner, Philipp and Dingel, Kristina and Sick, Bernhard and Lehmann, Peter and Buhmann, Stefan Yoshi and Hillmer, Hartmut}, journal = {Materials 2024, Vol. 17, Page 3393}, keywords = {effect,Van,Waals}, month = {07}, number = 14, pages = 3393, publisher = {Multidisciplinary Digital Publishing Institute}, title = {Casimir Effect in MEMS: Materials, Geometries, and Metrologies—A Review}, volume = 17, year = 2024 }
1.
Engelhardt, A., Decke, J., Meier, D., Dulig, F., Ragunathan, R., Wegener, T., Sick, B., Niendorf, T.: On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision-Based Tool. Advanced Engineering Materials. 25, 2300876 (2023). https://doi.org/10.1002/ADEM.202300876.
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Fracture surface analysis is of utmost importance with respect to structural integrity of metallic materials. This especially holds true for additively manufactured materials. Despite an increasing trend of automatization of testing methods, the analysis and classification of fatigue fracture surface images is commonly done manually by experts. Although this leads to correct results in most cases, it has several disadvantages, e.g., the need of a huge knowledge base to interpret images correctly. In present work, an unsupervised tool for analysis of overview images of fatigue fracture surface images is developed to support nonexperienced users to identify the origin of the fracture. The tool is developed using fracture surface images of additively manufactured Ti6Al4V specimens fatigued in the high-cycle-fatigue regime and is based on the identification of river marks. Several recording parameters seem to have no significant influence on the results as long as preprocessing settings are adapted. Moreover, it is possible to analyze images of other materials with the tool as long as the fracture surfaces contain river marks. However, special features like multiple origins or origins located in direct vicinity to the surface, e.g., caused by increased plastic strains, require a further tool development or alternative approaches.
@article{Engelhardt2023, abstract = {Fracture surface analysis is of utmost importance with respect to structural integrity of metallic materials. This especially holds true for additively manufactured materials. Despite an increasing trend of automatization of testing methods, the analysis and classification of fatigue fracture surface images is commonly done manually by experts. Although this leads to correct results in most cases, it has several disadvantages, e.g., the need of a huge knowledge base to interpret images correctly. In present work, an unsupervised tool for analysis of overview images of fatigue fracture surface images is developed to support nonexperienced users to identify the origin of the fracture. The tool is developed using fracture surface images of additively manufactured Ti6Al4V specimens fatigued in the high-cycle-fatigue regime and is based on the identification of river marks. Several recording parameters seem to have no significant influence on the results as long as preprocessing settings are adapted. Moreover, it is possible to analyze images of other materials with the tool as long as the fracture surfaces contain river marks. However, special features like multiple origins or origins located in direct vicinity to the surface, e.g., caused by increased plastic strains, require a further tool development or alternative approaches.}, author = {Engelhardt, Anna and Decke, Jens and Meier, David and Dulig, Franz and Ragunathan, Rishan and Wegener, Thomas and Sick, Bernhard and Niendorf, Thomas}, journal = {Advanced Engineering Materials}, keywords = {aimed}, month = 11, number = 21, pages = 2300876, publisher = {John Wiley \& Sons, Ltd}, title = {On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision-Based Tool}, volume = 25, year = 2023 }
1.
Ilchen, M., Bari, S., Baumann, T., Behrens, C., Bican, Y., Bidhendi, M., Boll, R., Braune, M., Brenner, G., Callegari, F., Fanis, A.D., Degenhardt, M., Dingel, K., Düsterer, S., Egun, F., Ehresmann, A., Erk, B., Funke, L., Galler, A., Geloni, G., Götzke, G., Gorkhover, T., Grünert, J., Grychtol, P., Guetg, M., Hans, A., Held, A., Hindrikson, R., Hoesch, M., Jahnke, T., Jastrow, F., Kienberger, R., Laksman, J., Larsson, M., Liu, J., Marangos, J., Marder, L., Meier, D., Meyer, M., Mirian, N., Montano, J., Mullins, T., Music, V., Ott, C., Otto, T., Ovcharenko, Y., Palutke, S., Passow, C., Pfeifer, T., Rennhack, N., Rivas, D., Rolles, D., Rudenko, A., Rupprecht, P., Savio, S., Schletter, A., Scholz, F., Seltmann, J., Serkez, S., Schmidt, P., Schneidmiller, E., Sick, B., Thomas, R., Usenko, S., Viefhaus, J., Walter, P., Wanie, V., Wieland, N., Wülfing, L., Yurkov, M., Zhaunerchyk, V., Tiedtke, K., Helml, W.: Short-pulse diagnostics at the attosecond frontier for EuXFEL and FLASH (Conference Presentation). https://doi.org/10.1117/12.2669164. PC12581, PC1258108 (2023). https://doi.org/10.1117/12.2669164.
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Fundamental electron dynamics at the attosecond frontier and their direct coupling to structural dynamics of matter yield novel insights into the energy-distribution and protection mechanisms of Nature. The angular-streaking technique has exclusively demonstrated its capability of obtaining the full time-energy structure of XFEL pulses with attosecond resolution directly in the time-domain, thus enabling XFELs to study electron dynamics from element-specific vistas and their importance as onset of subsequent structural dynamics. We will present latest advances of this technique together with first results from the 2022 EuXFEL atto-campaign and the complementary prospects of the FLASH 2020+ innovation project at DESY.
@article{Ilchen2023, abstract = {Fundamental electron dynamics at the attosecond frontier and their direct coupling to structural dynamics of matter yield novel insights into the energy-distribution and protection mechanisms of Nature. The angular-streaking technique has exclusively demonstrated its capability of obtaining the full time-energy structure of XFEL pulses with attosecond resolution directly in the time-domain, thus enabling XFELs to study electron dynamics from element-specific vistas and their importance as onset of subsequent structural dynamics. We will present latest advances of this technique together with first results from the 2022 EuXFEL atto-campaign and the complementary prospects of the FLASH 2020+ innovation project at DESY.}, author = {Ilchen, Markus and Bari, Sadia and Baumann, Thomas and Behrens, Christopher and Bican, Yilmaz and Bidhendi, Mahdi and Boll, Rebecca and Braune, Markus and Brenner, Günter and Callegari, Francesca and Fanis, Alberto De and Degenhardt, Markus and Dingel, Kristina and Düsterer, Stefan and Egun, Felix and Ehresmann, Arno and Erk, Benjamin and Funke, Lars and Galler, Andreas and Geloni, Gianluca and Götzke, Gesa and Gorkhover, Tais and Grünert, Jan and Grychtol, Patrik and Guetg, Marc and Hans, Andreas and Held, Arne and Hindrikson, Ruda and Hoesch, Moritz and Jahnke, Till and Jastrow, Fini and Kienberger, Reinhard and Laksman, Joakim and Larsson, Mats and Liu, Jia and Marangos, Jon and Marder, Lutz and Meier, David and Meyer, Michael and Mirian, Najmeh and Montano, Jacobo and Mullins, Terence and Music, Valerija and Ott, Christian and Otto, Thorsten and Ovcharenko, Yevheniy and Palutke, Steffen and Passow, Christopher and Pfeifer, Thomas and Rennhack, Nils and Rivas, Daniel and Rolles, Daniel and Rudenko, Artem and Rupprecht, Patrick and Savio, Sara and Schletter, Albert and Scholz, Frank and Seltmann, Jörn and Serkez, Svitozar and Schmidt, Philipp and Schneidmiller, Evgeny and Sick, Bernhard and Thomas, Richard and Usenko, Sergey and Viefhaus, Jens and Walter, Peter and Wanie, Vincent and Wieland, Niclas and Wülfing, Lasse and Yurkov, Mikhail and Zhaunerchyk, Vitali and Tiedtke, Kai and Helml, Wolfram}, journal = {https://doi.org/10.1117/12.2669164}, keywords = {aimed}, month = {06}, pages = {PC1258108}, publisher = {SPIE}, title = {Short-pulse diagnostics at the attosecond frontier for EuXFEL and FLASH (Conference Presentation)}, volume = {PC12581}, year = 2023 }
1.
Meier, D., Ragunathan, R., Degener, S., Liehr, A., Vollmer, M., Niendorf, T., Sick, B.: Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning. Scientific Reports 2023 13:1. 13, 1–12 (2023). https://doi.org/10.1038/s41598-023-31580-1.
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X-ray diffraction crystallography allows non-destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X-ray diffraction has been highly time-consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep-learning-based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU-based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.
@article{Meier2023, abstract = {X-ray diffraction crystallography allows non-destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X-ray diffraction has been highly time-consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep-learning-based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU-based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.}, author = {Meier, David and Ragunathan, Rishan and Degener, Sebastian and Liehr, Alexander and Vollmer, Malte and Niendorf, Thomas and Sick, Bernhard}, journal = {Scientific Reports 2023 13:1}, keywords = {methods,Imaging}, month = {04}, number = 1, pages = {1-12}, publisher = {Nature Publishing Group}, title = {Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning}, volume = 13, year = 2023 }
1.
Huhnstock, R., Paetzold, L., Merkel, M., Kuświk, P., Ehresmann, A.: Combined Funnel, Concentrator, and Particle Valve Functional Element for Magnetophoretic Bead Transport Based on Engineered Magnetic Domain Patterns. Small. n/a, 2305675 (2023). https://doi.org/https://doi.org/10.1002/smll.202305675.
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Abstract Controlled actuation of superparamagnetic beads (SPBs) within a microfluidic environment using tailored dynamic magnetic field landscapes (MFLs) is a potent approach for the realization of point-of-care diagnostics within Lab-on-a-chip (LOC) systems. Making use of an engineered magnetic domain pattern as the MFL source, a functional LOC-element with combined magnetophoretic “funnel”, concentrator, and “valve” functions for micron-sized SPBs is presented. A parallel-stripe domain pattern design with periodically decreasing/increasing stripe lengths is fabricated in a topographically flat continuous exchange biased (EB) thin film system by ion bombardment induced magnetic patterning (IBMP). It is demonstrated that, upon application of external magnetic field pulses, a fully reversible concentration of SPBs at the domain pattern's focal point occurs. In addition, it is shown that this functionality may be used as an SPB “funnel”, allowing only a maximum number of particles to pass through the focal point. Adjusting the pulse time length, the focal point can be clogged up for incoming SPBs, resembling an on/off switchable particle “valve”. The observations are supported by quantitative theoretical force considerations.
@article{https://doi.org/10.1002/smll.202305675, abstract = {Abstract Controlled actuation of superparamagnetic beads (SPBs) within a microfluidic environment using tailored dynamic magnetic field landscapes (MFLs) is a potent approach for the realization of point-of-care diagnostics within Lab-on-a-chip (LOC) systems. Making use of an engineered magnetic domain pattern as the MFL source, a functional LOC-element with combined magnetophoretic “funnel”, concentrator, and “valve” functions for micron-sized SPBs is presented. A parallel-stripe domain pattern design with periodically decreasing/increasing stripe lengths is fabricated in a topographically flat continuous exchange biased (EB) thin film system by ion bombardment induced magnetic patterning (IBMP). It is demonstrated that, upon application of external magnetic field pulses, a fully reversible concentration of SPBs at the domain pattern's focal point occurs. In addition, it is shown that this functionality may be used as an SPB “funnel”, allowing only a maximum number of particles to pass through the focal point. Adjusting the pulse time length, the focal point can be clogged up for incoming SPBs, resembling an on/off switchable particle “valve”. The observations are supported by quantitative theoretical force considerations.}, author = {Huhnstock, Rico and Paetzold, Lukas and Merkel, Maximilian and Kuświk, Piotr and Ehresmann, Arno}, journal = {Small}, keywords = {imported}, number = {n/a}, pages = 2305675, title = {Combined Funnel, Concentrator, and Particle Valve Functional Element for Magnetophoretic Bead Transport Based on Engineered Magnetic Domain Patterns}, volume = {n/a}, year = 2023 }
1.
Akhundzada, S., Dingel, K., Bischof, D., Janzen, C., Sick, B., Ehresmann, A.: Domain Imaging in Periodic Submicron Wide Nanostructures by Digital Drift Correction in Kerr Microscopy. Advanced Photonics Research. 4, 2300170 (2023). https://doi.org/https://doi.org/10.1002/adpr.202300170.
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Magneto-optical Kerr microscopy is a powerful method for imaging magnetic domains. Even though domain imaging below the diffractive resolution limit is possible, such investigations are getting increasingly complex with decreasing structure size due to the decreasing Kerr contrast. As magnetic domain images free of topographical artifacts are obtained by subtracting a reference image from the actual image, the corresponding challenges are additionally increased by unavoidable sample motion in the time interval between acquiring the two images. Software-based drift corrections typically rely on a unique structure in the image's region of interest (ROI), recognized automatically or selected manually by the user. By digital image shifting, the ROI positions in the actual and reference images are aligned, and the sample motion is compensated. For magnetic domain imaging in periodically arranged micro- or nano-objects, unique topographical features are not given, making the drift correction by ROIs difficult, often even impossible. Herein, a novel software-based approach is presented for drift corrections to image domains with features close/below the optical resolution limit and for investigating periodically arranged micro- or nano-objects without utilizing ROIs. High-contrast images are obtained, enabling the characterization of periodically arranged 1D, 2D, and 3D magnetic objects with lateral dimensions below 100 nm.
@article{https://doi.org/10.1002/adpr.202300170, abstract = {Magneto-optical Kerr microscopy is a powerful method for imaging magnetic domains. Even though domain imaging below the diffractive resolution limit is possible, such investigations are getting increasingly complex with decreasing structure size due to the decreasing Kerr contrast. As magnetic domain images free of topographical artifacts are obtained by subtracting a reference image from the actual image, the corresponding challenges are additionally increased by unavoidable sample motion in the time interval between acquiring the two images. Software-based drift corrections typically rely on a unique structure in the image's region of interest (ROI), recognized automatically or selected manually by the user. By digital image shifting, the ROI positions in the actual and reference images are aligned, and the sample motion is compensated. For magnetic domain imaging in periodically arranged micro- or nano-objects, unique topographical features are not given, making the drift correction by ROIs difficult, often even impossible. Herein, a novel software-based approach is presented for drift corrections to image domains with features close/below the optical resolution limit and for investigating periodically arranged micro- or nano-objects without utilizing ROIs. High-contrast images are obtained, enabling the characterization of periodically arranged 1D, 2D, and 3D magnetic objects with lateral dimensions below 100 nm.}, author = {Akhundzada, Sapida and Dingel, Kristina and Bischof, David and Janzen, Christian and Sick, Bernhard and Ehresmann, Arno}, journal = {Advanced Photonics Research}, keywords = {imported}, number = 10, pages = 2300170, title = {Domain Imaging in Periodic Submicron Wide Nanostructures by Digital Drift Correction in Kerr Microscopy}, volume = 4, year = 2023 }
1.
Meier, D., Ramirez, L.V., Völker, J., Viefhaus, J., Sick, B., Hartmann, G.: Optimizing a superconducting radio-frequency gun using deep reinforcement learning. Physical Review Accelerators and Beams. 25, 104604 (2022). https://doi.org/10.1103/PHYSREVACCELBEAMS.25.104604/FIGURES/11/MEDIUM.
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Superconducting photoelectron injectors are promising for generating highly brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultrafast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimizing the beam properties is challenging due to the high number of possible machine parameter combinations. This article shows the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the required computation time, we replace the costly simulation with a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms standard optimization methods for the required function evaluations given a defined minimum accuracy.
@article{Meier2022, abstract = {Superconducting photoelectron injectors are promising for generating highly brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultrafast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimizing the beam properties is challenging due to the high number of possible machine parameter combinations. This article shows the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the required computation time, we replace the costly simulation with a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms standard optimization methods for the required function evaluations given a defined minimum accuracy.}, author = {Meier, David and Ramirez, Luis Vera and Völker, Jens and Viefhaus, Jens and Sick, Bernhard and Hartmann, Gregor}, journal = {Physical Review Accelerators and Beams}, keywords = {aimed}, month = 10, number = 10, pages = 104604, publisher = {American Physical Society}, title = {Optimizing a superconducting radio-frequency gun using deep reinforcement learning}, volume = 25, year = 2022 }
1.
Dingel, K., Otto, T., Marder, L., Funke, L., Held, A., Savio, S., Hans, A., Hartmann, G., Meier, D., Viefhaus, J., Sick, B., Ehresmann, A., Ilchen, M., Helml, W.: Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulses. Scientific Reports. 12, 17809- (2022). https://doi.org/10.1038/s41598-022-21646-x.
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X-ray free-electron lasers (XFELs) as the world’s brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time–energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.
@article{dingel2022artificial, abstract = {X-ray free-electron lasers (XFELs) as the world’s brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time–energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.}, author = {Dingel, Kristina and Otto, Thorsten and Marder, Lutz and Funke, Lars and Held, Arne and Savio, Sara and Hans, Andreas and Hartmann, Gregor and Meier, David and Viefhaus, Jens and Sick, Bernhard and Ehresmann, Arno and Ilchen, Markus and Helml, Wolfram}, journal = {Scientific Reports}, keywords = {imported}, number = 1, pages = {17809–}, title = {Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulses}, volume = 12, year = 2022 }
1.
Huhnstock, R., Reginka, M., Sonntag, C., Merkel, M., Dingel, K., Sick, B., Vogel, M., Ehresmann, A.: Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes. Scientific Reports. 12, 20890- (2022). https://doi.org/10.1038/s41598-022-25391-z.
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The transport of magnetic particles (MPs) by dynamic magnetic field landscapes (MFLs) using magnetically patterned substrates is promising for the development of Lab-on-a-chip (LOC) systems. The inherent close-to-substrate MP motion is sensitive to changing particle–substrate interactions. Thus, the detection of a modified particle–substrate separation distance caused by surface binding of an analyte is expected to be a promising probe in analytics and diagnostics. Here, we present an essential prerequisite for such an application, namely the label-free quantitative experimental determination of the three-dimensional trajectories of superparamagnetic particles (SPPs) transported by a dynamically changing MFL. The evaluation of defocused SPP images from optical bright-field microscopy revealed a “hopping”-like motion of the magnetic particles, previously predicted by theory, additionally allowing a quantification of maximum jump heights. As our findings pave the way towards precise determination of particle–substrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy.
@article{huhnstock2022threedimensional, abstract = {The transport of magnetic particles (MPs) by dynamic magnetic field landscapes (MFLs) using magnetically patterned substrates is promising for the development of Lab-on-a-chip (LOC) systems. The inherent close-to-substrate MP motion is sensitive to changing particle–substrate interactions. Thus, the detection of a modified particle–substrate separation distance caused by surface binding of an analyte is expected to be a promising probe in analytics and diagnostics. Here, we present an essential prerequisite for such an application, namely the label-free quantitative experimental determination of the three-dimensional trajectories of superparamagnetic particles (SPPs) transported by a dynamically changing MFL. The evaluation of defocused SPP images from optical bright-field microscopy revealed a “hopping”-like motion of the magnetic particles, previously predicted by theory, additionally allowing a quantification of maximum jump heights. As our findings pave the way towards precise determination of particle–substrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy.}, author = {Huhnstock, Rico and Reginka, Meike and Sonntag, Claudius and Merkel, Maximilian and Dingel, Kristina and Sick, Bernhard and Vogel, Michael and Ehresmann, Arno}, journal = {Scientific Reports}, keywords = {3dpt}, number = 1, pages = {20890–}, title = {Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes}, volume = 12, year = 2022 }
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Dingel, K., Huhnstock, R., Knie, A., Ehresmann, A., Sick, B.: AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems. Computer Physics Communications. 262, 107859 (2021). https://doi.org/10.1016/j.cpc.2021.107859.
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Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x-y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated.
@article{Dingel2021, abstract = {Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x-y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated.}, author = {Dingel, Kristina and Huhnstock, Rico and Knie, André and Ehresmann, Arno and Sick, Bernhard}, journal = {Computer Physics Communications}, keywords = {3dpt}, month = {01}, pages = 107859, publisher = {Elsevier BV}, title = {AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems}, volume = 262, year = 2021 }
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Reginka, M., Hoang, H., Efendi, Ö., Merkel, M., Huhnstock, R., Holzinger, D., Dingel, K., Sick, B., Bertinetti, D., Herberg, F.W., Ehresmann, A.: Transport Efficiency of Biofunctionalized Magnetic Particles Tailored by Surfactant Concentration. Langmuir. 37, 8498–8507 (2021). https://doi.org/10.1021/acs.langmuir.1c00900.
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@article{doi:10.1021/acs.langmuir.1c00900, author = {Reginka, Meike and Hoang, Hai and Efendi, Özge and Merkel, Maximilian and Huhnstock, Rico and Holzinger, Dennis and Dingel, Kristina and Sick, Bernhard and Bertinetti, Daniela and Herberg, Friedrich W. and Ehresmann, Arno}, journal = {Langmuir}, keywords = {3dpt}, note = {PMID: 34231364}, number = 28, pages = {8498-8507}, title = {Transport Efficiency of Biofunctionalized Magnetic Particles Tailored by Surfactant Concentration}, volume = 37, year = 2021 }
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Dingel, K., Liehr, A., Vogel, M., Degener, S., Meier, D., Niendorf, T., Ehresmann, A., Sick, B.: AI-Based On The Fly Design of Experiments in Physics and Engineering. In: Workshop on Self-Improving System Integration (2021).
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@inproceedings{Dingel2021, author = {Dingel, Kristina and Liehr, Alexander and Vogel, Michael and Degener, Sebastian and Meier, David and Niendorf, Thomas and Ehresmann, Arno and Sick, Bernhard}, booktitle = {Workshop on Self-Improving System Integration}, keywords = {3dpt}, title = {{AI-Based On The Fly Design of Experiments in Physics and Engineering}}, year = 2021 }
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Huhnstock, R., Reginka, M., Tomita, A., Merkel, M., Dingel, K., Holzinger, D., Sick, B., Vogel, M., Ehresmann, A.: Translatory and rotatory motion of Exchange-Bias capped Janus particles controlled by dynamic magnetic field landscapes. Scientific Reports. (2021).
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@article{huhnstock2021, author = {Huhnstock, Rico and Reginka, Meike and Tomita, Andreea and Merkel, Maximilian and Dingel, Kristina and Holzinger, Dennis and Sick, Bernhard and Vogel, Michael and Ehresmann, Arno}, journal = {Scientific Reports}, keywords = {3dpt}, publisher = {Nature Publishing Group}, title = {Translatory and rotatory motion of Exchange-Bias capped Janus particles controlled by dynamic magnetic field landscapes}, year = 2021 }
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Meier, D., Hartmann, G., Völker, J., Viefhaus, J., Sick, B.: Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm. In: Chubar, O. en Sawhney, K. (reds) Advances in Computational Methods for X-Ray Optics V. bll 71–77. SPIE (2020). https://doi.org/10.1117/12.2568001.
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@inproceedings{MHV20, author = {Meier, David and Hartmann, Gregor and Völker, Jens and Viefhaus, Jens and Sick, Bernhard}, booktitle = {Advances in Computational Methods for X-Ray Optics V}, editor = {Chubar, Oleg and Sawhney, Kawal}, keywords = {itegpub}, organization = {International Society for Optics and Photonics}, pages = {71 – 77}, publisher = {SPIE}, title = {{Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm}}, volume = 11493, year = 2020 }
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Holzinger, D., Koch, I., Burgard, S., Ehresmann, A.: Directed Magnetic Particle Transport above Artificial Magnetic Domains Due to Dynamic Magnetic Potential Energy Landscape Transformation. ACS Nano. 9, 7323–7331 (2015). https://doi.org/10.1021/acsnano.5b02283.
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@article{doi:10.1021/acsnano.5b02283, author = {Holzinger, Dennis and Koch, Iris and Burgard, Stefan and Ehresmann, Arno}, journal = {ACS Nano}, keywords = {3dpt}, note = {PMID: 26134922}, number = 7, pages = {7323-7331}, title = {Directed Magnetic Particle Transport above Artificial Magnetic Domains Due to Dynamic Magnetic Potential Energy Landscape Transformation}, volume = 9, year = 2015 }