University of Electronic Science and Technology of China

About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Antenna (radio) & Dielectric. The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.

Recent Progress on Surface Reconstruction of Earth-Abundant Electrocatalysts for Water Oxidation

Abstract: As one important electrode reaction in electrocatalytic and photoelectrochemical cells for renewable energy circulation, oxygen catalysis has attracted considerable research in developing efficient and cost-effective catalysts. Due to the inevitable formation of oxygenic intermediates on surface sites during the complex reaction steps, the surface structure dynamically evolves toward reaction-preferred active species. To date, transition metal compounds, here defined as TM-Xides, where "X" refers to typical nonmetal elements from group IIIA to VIA, including hydroxide as well, are reported as high-performance oxygen evolution reaction (OER) electrocatalysts. However, more studies observe at least exterior oxidation or amorphization of materials. Thus, whether the TM-Xides can be defined as OER catalysts deserves further discussion. This Review pays attention to recent progress on the surface reconstruction of TM-Xide OER electrocatalysts with an emphasis on the identification of the true active species for OER, and aims at disseminating the real contributors of OER performance, especially under long-duration electrocatalysis.

Deep reinforcement learning enabled self-learning control for energy efficient driving

Abstract: To address the air pollution problems and reduce greenhouse gas emissions (GHG), plug-in hybrid electric vehicles (PHEV) have been developed to achieve higher fuel efficiency. The Energy Management System (EMS) is a very important component of a PHEV in achieving better fuel economy and it is a very active research area. So far, most of the existing EMS strategies are either just simply following predefined rules that are not adaptive to changing driving conditions; or heavily relying on accurate prediction of future traffic conditions. Deep learning algorithms have been successfully applied to many complex problems and proved to even outperform human beings in some tasks (e.g., play chess) in recent years, which shows the great potential of such methods in practical engineering problems. In this study, a deep reinforcement learning (Deep Q-network or DQN) based PHEV energy management system is designed to autonomously learn the optimal fuel/electricity splits from interactions between the vehicle and the traffic environment. It is a fully data-driven and self-learning model that does not rely on any prediction, predefined rules or even prior human knowledge. The experiment results show that the proposed model is capable of achieving 16.3% energy savings (with the designed PHEV simulation model) on a typical commute trip, compared to conventional binary control strategies. In addition, a dueling Deep Q-network with dueling structure (DDQN) is also implemented and compared with single DQN in particular with respect to the convergence rate in the training process.

A Fast Optimization Method for General Binary Code Learning

Abstract: Hashing or binary code learning has been recognized to accomplish efficient near neighbor search, and has thus attracted broad interests in recent retrieval, vision, and learning studies. One main challenge of learning to hash arises from the involvement of discrete variables in binary code optimization. While the widely used continuous relaxation may achieve high learning efficiency, the pursued codes are typically less effective due to accumulated quantization error. In this paper, we propose a novel binary code optimization method, dubbed discrete proximal linearized minimization (DPLM) , which directly handles the discrete constraints during the learning process. Specifically, the discrete (thus nonsmooth nonconvex) problem is reformulated as minimizing the sum of a smooth loss term with a nonsmooth indicator function. The obtained problem is then efficiently solved by an iterative procedure with each iteration admitting an analytical discrete solution, which is thus shown to converge very fast. In addition, the proposed method supports a large family of empirical loss functions, which is particularly instantiated in this paper by both a supervised and an unsupervised hashing losses, together with the bits uncorrelation and balance constraints. In particular, the proposed DPLM with a supervised $\ell _{2}$ loss encodes the whole NUS-WIDE database into 64-b binary codes within 10 s on a standard desktop computer. The proposed approach is extensively evaluated on several large-scale data sets and the generated binary codes are shown to achieve very promising results on both retrieval and classification tasks.

Single Ni Atoms Anchored on Porous Few‐Layer g‐C 3 N 4 for Photocatalytic CO 2 Reduction: The Role of Edge Confinement

Abstract: It is greatly intriguing yet remains challenging to construct single-atomic photocatalysts with stable surface free energy, favorable for well-defined atomic coordination and photocatalytic carrier mobility during the photoredox process. Herein, an unsaturated edge confinement strategy is defined by coordinating single-atomic-site Ni on the bottom-up synthesized porous few-layer g-C3 N4 (namely, Ni5 -CN) via a self-limiting method. This Ni5 -CN system with a few isolated Ni clusters distributed on the edge of g-C3 N4 is beneficial to immobilize the nonedged single-atomic-site Ni species, thus achieving a high single-atomic active site density. Remarkably, the Ni5 -CN system exhibits comparably high photocatalytic activity for CO2 reduction, giving the CO generation rate of 8.6 µmol g-1 h-1 under visible-light illumination, which is 7.8 times that of pure porous few-layer g-C3 N4 (namely, CN, 1.1 µmol g-1 h-1 ). X-ray absorption spectrometric analysis unveils that the cationic coordination environment of single-atomic-site Ni center, which is formed by Ni-N doping-intercalation the first coordination shell, motivates the superiority in synergistic N-Ni-N connection and interfacial carrier transfer. The photocatalytic mechanistic prediction confirms that the introduced unsaturated Ni-N coordination favorably binds with CO2 , and enhances the rate-determining step of intermediates for CO generation.

Electron-ion collider in China

Abstract: Lepton scattering is an established ideal tool for studying inner structure of small particles such as nucleons as well as nuclei. As a future high energy nuclear physics project, an Electron-ion collider in China (EicC) has been proposed. It will be constructed based on an upgraded heavy-ion accelerator, High Intensity heavy-ion Accelerator Facility (HIAF) which is currently under construction, together with a new electron ring. The proposed collider will provide highly polarized electrons (with a polarization of ∼80%) and protons (with a polarization of ∼70%) with variable center of mass energies from 15 to 20 GeV and the luminosity of (2–3) × 10$^{33}$ cm$^{−2}$ · s$^{−1}$. Polarized deuterons and Helium-3, as well as unpolarized ion beams from Carbon to Uranium, will be also available at the EicC.The main foci of the EicC will be precision measurements of the structure of the nucleon in the sea quark region, including 3D tomography of nucleon; the partonic structure of nuclei and the parton interaction with the nuclear environment; the exotic states, especially those with heavy flavor quark contents. In addition, issues fundamental to understanding the origin of mass could be addressed by measurements of heavy quarkonia near-threshold production at the EicC. In order to achieve the above-mentioned physics goals, a hermetical detector system will be constructed with cutting-edge technologies.This document is the result of collective contributions and valuable inputs from experts across the globe. The EicC physics program complements the ongoing scientific programs at the Jefferson Laboratory and the future EIC project in the United States. The success of this project will also advance both nuclear and particle physics as well as accelerator and detector technology in China.[graphic not available: see fulltext]