1,409 research outputs found
Jamming transition in a highly dense granular system under vertical vibration
The dynamics of the jamming transition in a three-dimensional granular system
under vertical vibration is studied using diffusing-wave spectroscopy. When the
maximum acceleration of the external vibration is large, the granular system
behaves like a fluid, with the dynamic correlation function G(t) relaxing
rapidly. As the acceleration of vibration approaches the gravitational
acceleration g, the relaxation of G(t) slows down dramatically, and eventually
stops. Thus the system undergoes a phase transition and behaves like a solid.
Near the transition point, we find that the structural relaxation shows a
stretched exponential behavior. This behavior is analogous to the behavior of
supercooled liquids close to the glass transition.Comment: 5 pages, 5 figures, accepted by Phys. Rev.
SRZoo: An integrated repository for super-resolution using deep learning
Deep learning-based image processing algorithms, including image
super-resolution methods, have been proposed with significant improvement in
performance in recent years. However, their implementations and evaluations are
dispersed in terms of various deep learning frameworks and various evaluation
criteria. In this paper, we propose an integrated repository for the
super-resolution tasks, named SRZoo, to provide state-of-the-art
super-resolution models in a single place. Our repository offers not only
converted versions of existing pre-trained models, but also documentation and
toolkits for converting other models. In addition, SRZoo provides
platform-agnostic image reconstruction tools to obtain super-resolved images
and evaluate the performance in place. It also brings the opportunity of
extension to advanced image-based researches and other image processing models.
The software, documentation, and pre-trained models are publicly available on
GitHub.Comment: Accepted in ICASSP 2020, code available at
https://github.com/idearibosome/srzo
Ruthenium anchored on carbon nanotube electrocatalyst for hydrogen production with enhanced Faradaic efficiency
Developing efficient and stable electrocatalysts is crucial for the electrochemical production of pure and clean hydrogen. For practical applications, an economical and facile method of producing catalysts for the hydrogen evolution reaction (HER) is essential. Here, we report ruthenium (Ru) nanoparticles uniformly deposited on multi-walled carbon nanotubes (MWCNTs) as an efficient HER catalyst. The catalyst exhibits the small overpotentials of 13 and 17 mV at a current density of 10 mA cm(-2) in 0.5M aq. H2SO4 and 1.0M aq. KOH, respectively, surpassing the commercial Pt/C (16 mV and 33 mV). Moreover, the catalyst has excellent stability in both media, showing almost "zeroloss" during cycling. In a real device, the catalyst produces 15.4% more hydrogen per power consumed, and shows a higher Faradaic efficiency (92.28%) than the benchmark Pt/C (85.97%). Density functional theory calculations suggest that Ru-C bonding is the most plausible active site for the HER
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