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- ItemPersistent and reversible solid iodine electrodeposition in nanoporous carbons(Berlin : Springer Nature, 2020) Prehal, Christian; Fitzek, Harald; Kothleitner, Gerad; Presser, Volker; Gollas, Bernhard; Freunberger, Stefan A.; Abbas, QamarAqueous iodine based electrochemical energy storage is considered a potential candidate to improve sustainability and performance of current battery and supercapacitor technology. It harnesses the redox activity of iodide, iodine, and polyiodide species in the confined geometry of nanoporous carbon electrodes. However, current descriptions of the electrochemical reaction mechanism to interconvert these species are elusive. Here we show that electrochemical oxidation of iodide in nanoporous carbons forms persistent solid iodine deposits. Confinement slows down dissolution into triiodide and pentaiodide, responsible for otherwise significant self-discharge via shuttling. The main tools for these insights are in situ Raman spectroscopy and in situ small and wide-angle X-ray scattering (in situ SAXS/WAXS). In situ Raman confirms the reversible formation of triiodide and pentaiodide. In situ SAXS/WAXS indicates remarkable amounts of solid iodine deposited in the carbon nanopores. Combined with stochastic modeling, in situ SAXS allows quantifying the solid iodine volume fraction and visualizing the iodine structure on 3D lattice models at the sub-nanometer scale. Based on the derived mechanism, we demonstrate strategies for improved iodine pore filling capacity and prevention of self-discharge, applicable to hybrid supercapacitors and batteries.
- ItemCharacterization and classification of semantic image-text relations(Berlin : Springer Nature, 2020) Otto, C.; Springstein, M.; Anand, A.; Ewerth, R.The beneficial, complementary nature of visual and textual information to convey information is widely known, for example, in entertainment, news, advertisements, science, or education. While the complex interplay of image and text to form semantic meaning has been thoroughly studied in linguistics and communication sciences for several decades, computer vision and multimedia research remained on the surface of the problem more or less. An exception is previous work that introduced the two metrics Cross-Modal Mutual Information and Semantic Correlation in order to model complex image-text relations. In this paper, we motivate the necessity of an additional metric called Status in order to cover complex image-text relations more completely. This set of metrics enables us to derive a novel categorization of eight semantic image-text classes based on three dimensions. In addition, we demonstrate how to automatically gather and augment a dataset for these classes from the Web. Further, we present a deep learning system to automatically predict either of the three metrics, as well as a system to directly predict the eight image-text classes. Experimental results show the feasibility of the approach, whereby the predict-all approach outperforms the cascaded approach of the metric classifiers.
- ItemLight-regulated growth from dynamic swollen substrates for making rough surfaces(Berlin : Springer Nature, 2020) Xue, Lulu; Xiong, Xinhong; Krishnan, Baiju P.; Puza, Fatih; Wang, Sheng; Zheng, Yijun; Cui, JiaxiNatural organic structures form via a growth mode in which nutrients are absorbed, transported, and integrated. In contrast, synthetic architectures are constructed through fundamentally different methods, such as assembling, molding, cutting, and printing. Here, we report a photoinduced strategy for regulating the localized growth of microstructures from the surface of a swollen dynamic substrate, by coupling photolysis, photopolymerization, and transesterification together. Photolysis is used to generate dissociable ionic groups to enhance the swelling ability that drives nutrient solutions containing polymerizable components into the irradiated region, photopolymerization converts polymerizable components into polymers, and transesterification incorporates newly formed polymers into the original network structure. Such light-regulated growth is spatially controllable and dose-dependent and allows fine modulation of the size, composition, and mechanical properties of the grown structures. We also demonstrate the application of this process in the preparation of microstructures on a surface and the restoration of large-scale surface damage.
- ItemQuantum-critical scale invariance in a transition metal alloy(Berlin : Springer Nature, 2020) Nakajima, Yasuyuki; Metz, Tristin; Eckberg, Christopher; Kirshenbaum, Kevin; Hughes, Alex; Wang, Renxiong; Wang, Limin; Saha, Shanta R.; Liu, I-Lin; Butch, Nicholas P.; Campbell, Daniel; Eo, Yun Suk; Graf, David; Liu, Zhonghao; Borisenko, Sergey V.; Zavalij, Peter Y.; Paglione, JohnpierreQuantum-mechanical fluctuations between competing phases induce exotic collective excitations that exhibit anomalous behavior in transport and thermodynamic properties, and are often intimately linked to the appearance of unconventional Cooper pairing. High-temperature superconductivity, however, makes it difficult to assess the role of quantum-critical fluctuations in shaping anomalous finite-temperature physical properties. Here we report temperature-field scale invariance of non-Fermi liquid thermodynamic, transport, and Hall quantities in a non-superconducting iron-pnictide, Ba(Fe1/3Co1/3Ni1/3)2As2, indicative of quantum criticality at zero temperature and applied magnetic field. Beyond a linear-in-temperature resistivity, the hallmark signature of strong quasiparticle scattering, we find a scattering rate that obeys a universal scaling relation between temperature and applied magnetic fields down to the lowest energy scales. Together with the dominance of hole-like carriers close to the zero-temperature and zero-field limits, the scale invariance, isotropic field response, and lack of applied pressure sensitivity suggests a unique quantum critical system unhindered by a pairing instability.
- ItemStrongly correlated superconductor with polytypic 3D Dirac points(Berlin : Springer Nature, 2020) Borisenko, Sergey; Bezguba, Volodymyr; Fedorov, Alexander; Kushnirenko, Yevhen; Voroshin, Vladimir; Sturza, Mihai; Aswartham, SaicharanTopological superconductors should be able to provide essential ingredients for quantum computing, but are very challenging to realize. Spin–orbit interaction in iron-based superconductors opens the energy gap between the p-states of pnictogen and d-states of iron very close to the Fermi level, and such p-states have been recently experimentally detected. Density-functional theory predicts existence of topological surface states within this gap in FeTe1−xSex making it an attractive candidate material. Here we use synchrotron-based angle-resolved photoemission spectroscopy and band structure calculations to demonstrate that FeTe1−xSex (x = 0.45) is a superconducting 3D Dirac semimetal hosting type-I and type-II Dirac points and that its electronic structure remains topologically trivial. We show that the inverted band gap in FeTe1−xSex can possibly be realized by further increase of Te content, but strong correlations reduce it to a sub-meV size, making the experimental detection of this gap and corresponding topological surface states very challenging, not to mention exact matching with the Fermi level. On the other hand, the p–d and d–d interactions are responsible for the formation of extremely flat band at the Fermi level pointing to its intimate relation with the mechanism of high-Tc superconductivity in IBS.
- ItemExtracting local nucleation fields in permanent magnets using machine learning(Berlin : Springer Nature, 2020) Gusenbauer, Markus; Oezelt, Harald; Fischbacher, Johann; Kovacs, Alexander; Zhao, Panpan; Woodcock, Thomas George; Schrefl, ThomasMicrostructural features play an important role in the quality of permanent magnets. The coercivity is greatly influenced by crystallographic defects, like twin boundaries, as is well known for MnAl-C. It would be very useful to be able to predict the macroscopic coercivity from microstructure imaging. Although this is not possible now, in the present work we examine a related question, namely the prediction of simulated nucleation fields of a quasi-three-dimensional (rescaled and extruded) system constructed from a two-dimensional image. We extract features of the image and analyze them via machine learning. A large number of extruded systems are constructed from 10 × 10 pixel sub-images of an Electron Backscatter Diffraction (EBSD) image using an automated meshing procedure. A local nucleation field is calculated by micromagnetic simulation of each quasi-three-dimensional system. Decision trees, trained with the simulation results, can predict nucleation fields of these quasi-three-dimensional systems from new images within seconds. As for now we cannot quantitatively predict the macroscopic coercivity, nevertheless we can identify weak spots in the magnet and see trends in the nucleation field distribution.
- ItemNickelate superconductors - a renaissance of the one-band Hubbard model(Berlin : Springer Nature, 2020) Kitatani, Motoharu; Si, Liang; Janson, Oleg; Arita, Ryotaro; Zhong, Zhicheng; Held, KarstenThe recently discovered nickelate superconductors appear, at first glance, to be even more complicated multi-orbital systems than cuprates. To identify the simplest model describing the nickelates, we analyse the multi-orbital system and find that it is instead the nickelates which can be described by a one-band Hubbard model, albeit with an additional electron reservoir and only around the superconducting regime. Our calculations of the critical temperature TC are in good agreement with experiment, and show that optimal doping is slightly below 20% Sr-doping. Even more promising than 3d nickelates are 4d palladates.
- ItemScholarly event characteristics in four fields of science: a metrics-based analysis(Berlin : Springer Nature, 2020) Fathalla, S.; Vahdati, S.; Lange, C.; Auer, SörenOne of the key channels of scholarly knowledge exchange are scholarly events such as conferences, workshops, symposiums, etc.; such events are especially important and popular in Computer Science, Engineering, and Natural Sciences.However, scholars encounter problems in finding relevant information about upcoming events and statistics on their historic evolution.In order to obtain a better understanding of scholarly event characteristics in four fields of science, we analyzed the metadata of scholarly events of four major fields of science, namely Computer Science, Physics, Engineering, and Mathematics using Scholarly Events Quality Assessment suite, a suite of ten metrics.In particular, we analyzed renowned scholarly events belonging to five sub-fields within Computer Science, namely World Wide Web, Computer Vision, Software Engineering, Data Management, as well as Security and Privacy.This analysis is based on a systematic approach using descriptive statistics as well as exploratory data analysis. The findings are on the one hand interesting to observe the general evolution and success factors of scholarly events; on the other hand, they allow (prospective) event organizers, publishers, and committee members to assess the progress of their event over time and compare it to other events in the same field; and finally, they help researchers to make more informed decisions when selecting suitable venues for presenting their work.Based on these findings, a set of recommendations has been concluded to different stakeholders, involving event organizers, potential authors, proceedings publishers, and sponsors. Our comprehensive dataset of scholarly events of the aforementioned fields is openly available in a semantic format and maintained collaboratively at OpenResearch.org.
- ItemEngineering microrobots for targeted cancer therapies from a medical perspective(Berlin : Springer Nature, 2020) Schmidt, Christine K.; Medina-Sánchez, Mariana; Edmondson, Richard J.; Schmidt, Oliver G.Systemic chemotherapy remains the backbone of many cancer treatments. Due to its untargeted nature and the severe side effects it can cause, numerous nanomedicine approaches have been developed to overcome these issues. However, targeted delivery of therapeutics remains challenging. Engineering microrobots is increasingly receiving attention in this regard. Their functionalities, particularly their motility, allow microrobots to penetrate tissues and reach cancers more efficiently. Here, we highlight how different microrobots, ranging from tailor-made motile bacteria and tiny bubble-propelled microengines to hybrid spermbots, can be engineered to integrate sophisticated features optimised for precision-targeting of a wide range of cancers. Towards this, we highlight the importance of integrating clinicians, the public and cancer patients early on in the development of these novel technologies.
- ItemReplication and Refinement of an Algorithm for Automated Drusen Segmentation on Optical Coherence Tomography(Berlin : Springer Nature, 2020) Wintergerst, M.W.M.; Gorgi Zadeh, S.; Wiens, V.; Thiele, S.; Schmitz-Valckenberg, S.; Holz, F.G.; Finger, R.P.; Schultz, T.Here, we investigate the extent to which re-implementing a previously published algorithm for OCT-based drusen quantification permits replicating the reported accuracy on an independent dataset. We refined that algorithm so that its accuracy is increased. Following a systematic literature search, an algorithm was selected based on its reported excellent results. Several steps were added to improve its accuracy. The replicated and refined algorithms were evaluated on an independent dataset with the same metrics as in the original publication. Accuracy of the refined algorithm (overlap ratio 36–52%) was significantly greater than the replicated one (overlap ratio 25–39%). In particular, separation of the retinal pigment epithelium and the ellipsoid zone could be improved by the refinement. However, accuracy was still lower than reported previously on different data (overlap ratio 67–76%). This is the first replication study of an algorithm for OCT image analysis. Its results indicate that current standards for algorithm validation do not provide a reliable estimate of algorithm performance on images that differ with respect to patient selection and image quality. In order to contribute to an improved reproducibility in this field, we publish both our replication and the refinement, as well as an exemplary dataset.