| 1. |
- Coupette, Fabian, et al.
(författare)
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Percolation of rigid fractal carbon black aggregates
- 2021
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Ingår i: Journal of Chemical Physics. - : AIP Publishing. - 0021-9606 .- 1089-7690. ; 155:12
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Tidskriftsartikel (refereegranskat)abstract
- We examine network formation and percolation of carbon black by means of Monte Carlo simulations and experiments. In the simulation, we model carbon black by rigid aggregates of impenetrable spheres, which we obtain by diffusion-limited aggregation. To determine the input parameters for the simulation, we experimentally characterize the micro-structure and size distribution of carbon black aggregates. We then simulate suspensions of aggregates and determine the percolation threshold as a function of the aggregate size distribution. We observe a quasi-universal relation between the percolation threshold and a weighted average radius of gyration of the aggregate ensemble. Higher order moments of the size distribution do not have an effect on the percolation threshold. We conclude further that the concentration of large carbon black aggregates has a stronger influence on the percolation threshold than the concentration of small aggregates. In the experiment, we disperse the carbon black in a polymer matrix and measure the conductivity of the composite. We successfully test the hypotheses drawn from simulation by comparing composites prepared with the same type of carbon black before and after ball milling, i.e., on changing only the distribution of aggregate sizes in the composites.& nbsp;
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| 2. |
- De Luca, Alberto, et al.
(författare)
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On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types : Chronicles of the MEMENTO challenge
- 2021
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 240
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Tidskriftsartikel (refereegranskat)abstract
- Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.
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| 3. |
- Klamt, Tobias, et al.
(författare)
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Flexible Disaster Response of Tomorrow: Final Presentation and Evaluation of the CENTAURO System
- 2019
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Ingår i: IEEE robotics & automation magazine. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1070-9932 .- 1558-223X. ; 26:4, s. 59-72
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Tidskriftsartikel (refereegranskat)abstract
- Mobile manipulation robots have great potential for roles in support of rescuers on disaster-response missions. Robots can operate in places too dangerous for humans and therefore can assist in accomplishing hazardous tasks while their human operators work at a safe distance. We developed a disaster-response system that consists of the highly flexible Centauro robot and suitable control interfaces, including an immersive telepresence suit and support-operator controls offering different levels of autonomy.
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| 4. |
- Schilling, Fabian, et al.
(författare)
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Geometric and visual terrain classification for autonomous mobile navigation
- 2017
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Ingår i: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : IEEE. - 9781538626825
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Konferensbidrag (refereegranskat)abstract
- In this paper, we present a multi-sensory terrain classification algorithm with a generalized terrain representation using semantic and geometric features. We compute geometric features from lidar point clouds and extract pixel-wise semantic labels from a fully convolutional network that is trained using a dataset with a strong focus on urban navigation. We use data augmentation to overcome the biases of the original dataset and apply transfer learning to adapt the model to new semantic labels in off-road environments. Finally, we fuse the visual and geometric features using a random forest to classify the terrain traversability into three classes: safe, risky and obstacle. We implement the algorithm on our four-wheeled robot and test it in novel environments including both urban and off-road scenes which are distinct from the training environments and under summer and winter conditions. We provide experimental result to show that our algorithm can perform accurate and fast prediction of terrain traversability in a mixture of environments with a small set of training data.
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| 5. |
- Zhang, Xiaofu, et al.
(författare)
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Suppression of the transition to superconductivity in crystal/glass high-entropy alloy nanocomposites
- 2022
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Ingår i: Communications Physics. - : NATURE PORTFOLIO. - 2399-3650. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- High entropy alloys are multielement materials exhibiting enhanced properties compared to their binary or ternary equivalents. Here, the authors investigate the influence of microstructure and elemental distribution on the transport and superconducting properties of (TaNb)(1-x)(ZrHfTi)(x) thin films. Superconducting high entropy alloys (HEAs) may combine extraordinary mechanical properties with robust superconductivity. They are suitable model systems for the investigation of the interplay of disorder and superconductivity. Here, we report on the superconductivity in (TaNb)(1-x)(ZrHfTi)(x) thin films. Beyond the near-equimolar region, the films comprise hundreds-of-nanometer-sized crystalline grains and show robust bulk superconductivity. However, the superconducting transitions in these nanocomposites are dramatically suppressed in the near-equimolar configurations, i.e., 0.45 < x < 0.64, where elemental distributions are equivalently homogeneous. Crystal/glass high entropy alloy nanocomposite phase separation was observed for the films in the near-equimolar region, which yields a broadened two-step normal to superconducting transition. Furthermore, the diamagnetic shielding in these films is only observed far below the onset temperature of superconductivity. As these unusual superconducting transitions are observed only in the samples with the high mixing entropy, this compositional range influences the collective electronic properties in these materials.
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