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Institution

National University of Defense Technology

EducationChangsha, China
About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Computer science & Radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the influence of gold nanoparticles (GNPs) on graphene was studied by Raman spectroscopy and it was found that the contact of GNPs could induce the whole Raman spectrum of graphene to redshift.
Abstract: The influence of gold nanoparticles (GNPs) on graphene was studied by Raman spectroscopy. It was found that the contact of GNPs could induce the whole Raman spectrum of graphene to redshift. And the shift of the 2D peak is more obvious than that of the G peak. A model of local strain was brought forward to explain the shift of Raman spectrum, which comes from the charges transfer between the GNPs and graphene. The observation of the Raman shifts helps us to gain more physical insights into the graphene-related systems.

102 citations

Journal ArticleDOI
TL;DR: In this article, a tailored multifunctional interlayer between the solid electrolyte and Li anode can successfully address the interfacial issues in NASCION-type Li conductors, which is designed by creating a quasi-solid-state paste in which the functionalities of LAGP (Li1.5Al0.5Ge1.3) nanoparticles and an ionic liquid (IL) electrolyte are combined.
Abstract: NASCION-type Li conductors have great potential to bring high capacity solid-state batteries to realization, related to its properties such as high ionic conductivity, stability under ambient conditions, wide electrochemical stability window, and inexpensive production. However, their chemical and thermal instability toward metallic lithium (Li) has severely hindered attempts to utilize Li as anode material in NASCION-based battery systems. In this work, it is shown how a tailored multifunctional interlayer between the solid electrolyte and Li anode can successfully address the interfacial issues. This interlayer is designed by creating a quasi-solid-state paste in which the functionalities of LAGP (Li1.5Al0.5Ge1.5(PO4)3) nanoparticles and an ionic liquid (IL) electrolyte are combined. In a solid-sate cell, the LAGP-IL interlayer separates the Li metal from bulk LAGP and creates a chemically stable interface with low resistance (≈5 Ω cm2) and efficiently prevents thermal runaway at elevated temperatures (300 °C). Solid-state cells designed with the interlayer can be operated at high current densities, 1 mA cm−2, and enable high rate capability with high safety. Here developed strategy provides a generic path to design interlayers for solid-state Li metal batteries.

102 citations

Journal ArticleDOI
TL;DR: The proposed dhDNA ratiometric sensor has higher reproducibility, accuracy, stability, sensitivity, and simplicity, which are significant for the development of the sensor in various fields for practical applications.
Abstract: In this work, the dual signal-tagged hairpin structured DNA (dhDNA)-based ratiometric probe was developed by the combination of ferrocene-labeled signal probe (Fc-sP) and methylene blue-modified inner reference probe (MB-rP) in one hairpin-structured DNA. On the basis of this, a high-performance ratiometric electrochemical method was proposed for biomarker detection. In contrast to the conventional ratiometric electrochemical probe, this dhDNA ratiometric probe integrated sP and rP into one structure, which ensured the completely same modification condition and the interdependence of sP and rP on one sensing interface. As a result, the dhDNA ratiometric probe possesses a stronger ability to eliminate the disturbance of environmental change, which was proven by the fact that the changes of the surface roughness and pH value had no significant effects on the reproducibility and stability of the sensor. Moreover, in the proposed strategy, the initial ratio responses of Fc-sP to MB-rP ((IFc-sP/IMB-rP)0) are c...

102 citations

Journal ArticleDOI
TL;DR: This letter finds that matrix inversion based on Newton iteration (NI) is suitable for data detection in massive MIMO system, and proposes a diagonal band Newton iterations (DBNI) method, which is an approximate method for NI.
Abstract: Currently, massive multiple-input multiple-output (MIMO) is one of the most promising wireless transmission technologies for 5G. Massive MIMO requires handling with large-scale matrix computation, especially for matrix inversion. In this letter, we find that matrix inversion based on Newton iteration (NI) is suitable for data detection in massive MIMO system. In contrast with recently proposed polynomial expansion (PE) method for matrix inversion, we analyze both the algorithm complexity and precision in detail, and propose a diagonal band Newton iteration (DBNI) method, which is an approximate method for NI. Compared with PE method, DBNI can obtain higher precision and approximately equal complexity, and we give an explanation of how to select the bandwidth of DBNI.

102 citations

Journal ArticleDOI
01 Mar 2017-Small
TL;DR: A stretchable fiber supercapacitor based on buckled MnO2 /oxidized carbon nanotube (CNT) fiber electrode is fabricated by a simple prestraining-then-buckling method and shows the outstanding stability and repeatability in performance as a stretchable SC.
Abstract: A stretchable fiber supercapacitor (SC) based on buckled MnO2 /oxidized carbon nanotube (CNT) fiber electrode is fabricated by a simple prestraining-then-buckling method. The prepared stretchable fiber SC has a specific volumetric capacitance up to 409.4 F cm-3 , which is 33 times that of the pristine CNT fiber based SC, and shows the outstanding stability and repeatability in performance as a stretchable SC.

102 citations


Authors

Showing all 39659 results

NameH-indexPapersCitations
Rui Zhang1512625107917
Jian Li133286387131
Chi Lin1251313102710
Wei Xu103149249624
Lei Liu98204151163
Xiang Li97147242301
Chang Liu97109939573
Jian Huang97118940362
Tao Wang97272055280
Wei Liu96153842459
Jian Chen96171852917
Wei Wang95354459660
Peng Li95154845198
Jianhong Wu9372636427
Jianhua Zhang9241528085
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
202397
2022469
20212,986
20203,468
20193,695