Included
among the many challenges regarding renewable energy technology
are improved electrocatalysts for the oxygen evolution reaction (OER).
In this study, we report a novel bifunctional electrocatalyst based
on a highly dense CoO
x
catalyst by introducing
CeO
x
. The CoO
x
catalyst is fabricated by two-step electrodeposition, including
Co seed formation, to obtain a very dense, layered structure, and
CeO
x
is also successfully deposited on
the CoO
x
catalyst. CoO
x
is an active catalyst showing good activity (η = 0.331
V at 10 mA cm–2) and also stability for the OER.
Higher activity is observed with the CeO
x
/CoO
x
electrocatalyst (η = 0.313
V at 10 mA cm–2). From mechanistic studies conducted
with synchrotron-based photoemission electron spectroscopy and DFT
calculations, Ce promotes a synergistic effect by perturbing the electronic
structure of surface Co species (facile formation to CoOOH) on the
CoO
x
catalyst and optimizes the binding
energy of intermediate oxygenated adsorbates.
Dynamics of a polymer gel network is described by the theory proposed by Tanaka, Hocker, and Benedek (THB) that gives the diffusion coefficient of a polymer network D K G f 4 3
A B S T R A C TA realistic inflow boundary condition is essential for successful simulation of the developing turbulent boundary layer or channel flows. Recent advances in artificial intelligence (AI) have enabled the development of an inflow generator that performs better than the synthetic methods based on intuitions. In the present work, we applied generative adversarial networks (GANs), a representative of unsupervised learning, to generate an inlet boundary condition of turbulent channel flow. Upon learning the two-dimensional spatial structure of turbulence using data obtained from direct numerical simulation (DNS) of turbulent channel flow, the GAN could generate instantaneous flow fields that are statistically similar to those of DNS. Surprisingly, the GAN could produce fields at various Reynolds numbers without any additional simulation based on the trained data of only three Reynolds numbers. This indicates that the GAN could learn the universal nature of Reynolds number effect and might reflect other simulation conditions. Eventually, through a combination of the GAN and a recurrent neural network (RNN), we developed a novel model (RNN-GAN) that could generate time-varying fully developed flow for a long time. The spatiotemporal correlations of the generated flow are in good agreement with those of the DNS. This proves the usefulness of unsupervised learning in the generation of synthetic turbulence fields.
Single-image super-resolution aims to generate a highresolution version of a low-resolution image, which serves as an essential component in many computer vision applications. This paper investigates the robustness of deep learning-based super-resolution methods against adversarial attacks, which can significantly deteriorate the superresolved images without noticeable distortion in the attacked low-resolution images. It is demonstrated that stateof-the-art deep super-resolution methods are highly vulnerable to adversarial attacks. Different levels of robustness of different methods are analyzed theoretically and experimentally. We also present analysis on transferability of attacks, and feasibility of targeted attacks and universal attacks.
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