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您的检索式：作者名="Wanru Song"
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| 1 | High-Entropy Perovskite Oxide: A New Opportunity for Developing Highly Active and Durable Air Electrode for Reversible Protonic Ceramic Electrochemical Cells显示文摘Reversible proton ceramic electrochemical cell(R-PCEC)is regarded as the most promising energy conversion device,which can realize efficient mutual conversion of electrical and chemical energy and to solve the problem of large-scale energy storage.However,the development of robust electrodes with high catalytic activity is the main bottleneck for the commercialization of R-PCECs.Here,a novel type of high-entropy perovskite oxide consisting of six equimolar metals in the A-site,Pr_(1/6)La_(1/6)Nd_(1/6)Ba_(1/6)Sr_(1/6)Ca_(1/6)CoO_(3−δ)(PLN-BSCC),is reported as a high-performance bifunctional air electrode for R-PCEC.By harnessing the unique functionalities of multiple ele-ments,high-entropy perovskite oxide can be anticipated to accelerate reaction rates in both fuel cell and electrolysis modes.Especially,an R-PCEC utilizing the PLNBSCC air electrode achieves exceptional electrochemical performances,demonstrating a peak power density of 1.21 W cm^(−2)for the fuel cell,while simultaneously obtaining an astonishing current density of−1.95 A cm^(−2)at an electrolysis voltage of 1.3 V and a temperature of 600℃.The significantly enhanced electrochemical performance and durability of the PLNBSCC air electrode is attributed mainly to the high electrons/ions conductivity,fast hydration reactivity and high configurational entropy.This research explores to a new avenue to develop optimally active and stable air electrodes for R-PCECs. | Zuoqing Liu Zhengjie Tang Yufei Song Guangming Yang Wanru Qian Meiting Yang Yinlong Zhu Ran Ran Wei Wang Wei Zhou Zongping Shao | 2022 | Nano-Micro Letters2022，14，12： | 1 |
| 2 | Augmented Deep Multi-Granularity Pose-Aware Feature Fusion Network for Visible-Infrared Person Re-Identification显示文摘Visible-infrared Cross-modality Person Re-identification(VI-ReID)is a critical technology in smart public facilities such as cities,campuses and libraries.It aims to match pedestrians in visible light and infrared images for video surveillance,which poses a challenge in exploring cross-modal shared information accurately and efficiently.Therefore,multi-granularity feature learning methods have been applied in VI-ReID to extract potential multi-granularity semantic information related to pedestrian body structure attributes.However,existing research mainly uses traditional dual-stream fusion networks and overlooks the core of cross-modal learning networks,the fusion module.This paper introduces a novel network called the Augmented Deep Multi-Granularity Pose-Aware Feature Fusion Network(ADMPFF-Net),incorporating the Multi-Granularity Pose-Aware Feature Fusion(MPFF)module to generate discriminative representations.MPFF efficiently explores and learns global and local features with multi-level semantic information by inserting disentangling and duplicating blocks into the fusion module of the backbone network.ADMPFF-Net also provides a new perspective for designing multi-granularity learning networks.By incorporating the multi-granularity feature disentanglement(mGFD)and posture information segmentation(pIS)strategies,it extracts more representative features concerning body structure information.The Local Information Enhancement(LIE)module augments high-performance features in VI-ReID,and the multi-granularity joint loss supervises model training for objective feature learning.Experimental results on two public datasets show that ADMPFF-Net efficiently constructs pedestrian feature representations and enhances the accuracy of VI-ReID. | Zheng Shi Wanru Song Junhao Shan Feng Liu | 2023 | Computers, Materials & Continua2023，77，12： | 0 |
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