Yuanyuan Hu

Dr. Yuanyuan Hu

orcid

Research Focus & Metrics

Academic Career Length

17Years (2010-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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22 May 2024 in Nature
Verified
Journal Article
Closed access
PDF file
DLDonglin Lu
Donglin Lu
YCYang Chen
Yang Chen
ZLZheyi Lu
Zheyi Lu
LMLikuan Ma
Likuan Ma
QTQuanyang Tao
Quanyang Tao
YLYuan Liu
Yuan Liu

25 January 2024 in Nature Communications
Verified
Journal Article
Open access
PDF file
CGChangsong Gao
Changsong Gao
DLDi Liu
Di Liu
CXChenhui Xu
Chenhui Xu
WXWeidong Xie
Weidong Xie
XZXianghong Zhang
Xianghong Zhang
HCHuipeng Chen
Huipeng Chen
Reservoir computing has attracted considerable attention due to its low training cost. However, existing neuromorphic hardware, focusing mainly on shallow-reservoir computing, faces challenges in providing adequate spatial and temporal scales characteristic for effective computing. Here, we report an ultra-short channel organic neuromorphic vertical transistor with distributed reservoir states. The carrier dynamics used to map signals are enriched by coupled multivariate physics mechanisms, while the vertical architecture employed greatly increases the feedback intensity of the device. Consequently, the device as a reservoir, effectively mapping sequential signals into distributed reservoir state space with 1152 reservoir states, and the range ratio of temporal and spatial characteristics can simultaneously reach 2640 and 650, respectively. The grouped-reservoir computing based on the device can simultaneously adapt to different spatiotemporal task, achieving recognition accuracy over 94% and prediction correlation over 95%. This work proposes a new strategy for developing high-performance reservoir computing networks.

24 September 2020 in Advanced Functional Materials
Verified
Journal Article
Closed access
PDF file
FLFengjing Liu
Fengjing Liu
LWLiang Wang
Liang Wang
JWJiawei Wang
Jiawei Wang
FWFeng Wang
Feng Wang
YCYiyi Chen
Yiyi Chen
CJChao Jiang
Chao Jiang

9 April 2018 in ACS Nano
Verified
Journal Article
Closed access
YHYuanyuan Hu
Yuanyuan Hu
ZRZachary D. Rengert
Zachary D. Rengert
CMCaitlin McDowell
Caitlin McDowell
MFMichael J. Ford
Michael J. Ford
MWMing Wang
Ming Wang
TNThuc-Quyen Nguyen
Thuc-Quyen Nguyen

16 August 2024 in Science Advances
Verified
Journal Article
Open access
XZXitong Zhu
Xitong Zhu
XLXiao Luo
Xiao Luo
YDYunzhou Deng
Yunzhou Deng
HWHuan Wei
Huan Wei
LYLei Ying
Lei Ying
YJYizheng Jin
Yizheng Jin
Quantum-dot light-emitting diodes (QLEDs) are solution-processed electroluminescence devices with great potential as energy-saving, large-area, and low-cost display and lighting technologies. Ideally, the organic hole-transport layers (HTLs) in QLEDs should simultaneously deliver efficient hole injection and transport, effective electron blocking, and robust electrochemical stability. However, it is still challenging for a single HTL to fulfill all these stringent criteria. Here, we demonstrate a general design of doping-bilayer polymer-HTL architecture for stabilizing high-efficiency QLEDs. We show that the bilayer HTLs combining the electrochemical-stable polymer and the electron-blocking polymer unexpectedly increase the hole injection barrier. We mitigated the problem by p-doping of the underlying sublayer of the bilayer HTLs. Consequently, green QLEDs with an unprecedented maximum luminance of 1,340,000 cd m −2 and a record-long operational lifetime ( T 95 lifetime at an initial luminance of 1000 cd m −2 is 17,700 hours) were achieved. The universality of the strategy is examined in various polymer-HTL systems, providing a general route toward high-performance solution-processed QLEDs.

6 August 2021 in ACS Nano
Verified
Journal Article
Closed access
ZLZhiwei Li
Zhiwei Li
LRLiwang Ren
Liwang Ren
SWShiyu Wang
Shiyu Wang
XHXinxin Huang
Xinxin Huang
QLQianyuan Li
Qianyuan Li
YLYuan Liu
Yuan Liu