A study of oxygen loss in Bi2Sr2CaCu2O8+y crystal

Wu, Wenbin; Xu, Cenyi; Zu, Jian; Shi, Lei; Zhou, Guien; Zhang, Yuheng

doi:10.1002/pssa.2211440239

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…As hydrogeninduced structural defects belong to the small-size defect type, and defect concentration increases rapidly with current density or charging time, we do not observe that hydrogen atoms trapped in defects induce a peak value of the S-parameter. This is in agreement with previous work by Wu et al [6,7,13] for iron and nickel and by Cao et al [5] for iron using conventional PAS.…”

Section: Analysis Of Hydrogen-induced Defectssupporting

confidence: 94%

“…The use of variable mono-energy positron beams allows one to determine the depth profile of defects in solids, and is particularly suitable for the analysis of surface and near-surface defects [2,3]. Hydrogen damage or hydrogen embrittlement (HE) of iron and steels has been investigated by PAS methods, such as angular correlation, positron annihilation lifetime (PAL), Doppler broadening energy spectroscopy (DBES), and other techniques [4][5][6][7][8]. For instance, a systematic investigation on the effect of interstitial trapping of hydrogen in iron single crystals [4] has been reported.…”

Section: Introductionmentioning

confidence: 99%

“…Studies of lattice defects in hydrogen-charged iron by PAS and electron microscopy [5] showed vacancy clusters of the order of 1 nm in cold-rolled and hydrogen-charged iron. Wu and co-workers have investigated hydrogen damage and hydrogen-induced plastic deformation in high-purity iron, and the critical current density producing hydrogen damage was determined by PAS [6,7]. Studies of tempering, plastic deformation, and hydrogen behavior after cathodic charging and aging in AISI 4340 steel by PAS have been reported [8].…”

Section: Introductionmentioning

confidence: 99%

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Hydrogen-induced defects of AISI 316 stainless steel studied by variable energy Doppler broadening energy spectra

Jean

2004

phys. stat. sol. (a)

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PACS 62.20.Fe, 78.70.Bj Hydrogen-induced defects of AISI 316 stainless steel were systemically investigated by measuring variable energy Doppler broadening energy spectra (DBES) of positron annihilation. Defect profiles of the S-parameter from DBES as a function of positron incident energy up to 30 keV (i.e. ∼1 µm depth) were analyzed. Experimental results showed that hydrogen damage between the surface and the bulk has a significant variation with depth, and strongly depends on the condition of hydrogen charging, i.e. current density and charging time. It has been suggested that the increase in the S-parameter near the surface after hydrogen charging mainly comes from the formation of hydrogen damage (voids); however, the increase in the S-parameter in the bulk after hydrogen charging mainly comes from the production of structural defects (dislocations). Defect densities induced due to hydrogen charging in some cases (e.g. dislocation density in the bulk) were estimated based on a simple trapping model.

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Section: Analysis Of Hydrogen-induced Defectssupporting

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hydrogen-induced defects of AISI 316 stainless steel studied by variable energy Doppler broadening energy spectra

Jean

2004

phys. stat. sol. (a)

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“…Especially, link prediction based on the topological similarity [3][4][5][6] becomes the focus of the research fields. In the aspect of practical applications, link prediction can be widely used to do research on the relationships between proteins in the biological ecosystems, 7,8 the potential relationships in the social networks, [9][10][11][12] the user-commodity relationships in the e-commerce networks, 13 the related information in the collaborative information filtering systems, [14][15][16] the infrastructure planning in the transportation networks, the route planning in the aviation networks, 17,18 the optimal resource diffusion for suppressing disease spreading in the multiplex networks 19 and the applications in the wireless sensor networks, 20 etc.…”

Section: Introductionmentioning

confidence: 99%

Link prediction based on combined influence and effective path

Tian

Zhu

et al. 2019

Int. J. Mod. Phys. B

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Link prediction based on topological similarity in complex networks obtains more and more attention both in academia and industry. Most researchers believe that two unconnected endpoints can possibly make a link when they have large influence, respectively. Through profound investigations, we find that at least one endpoint possessing large influence can easily attract other endpoints. The combined influence of two unconnected endpoints affects their mutual attractions. We consider that the greater the combined influence of endpoints is, the more the possibility of them producing a link. Therefore, we explore the contribution of combined influence for similarity-based link prediction. Furthermore, we find that the transmission capability of path determines the communication possibility between endpoints. Meanwhile, compared to the local and global path, the quasi-local path balances high accuracy and low complexity more effectually in link prediction. Therefore, we focus on the transmission capabilities of quasi-local paths between two unconnected endpoints, which is called effective paths. In this paper, we propose a link prediction index based on combined influence and effective path (CIEP). A large number of experiments on 12 real benchmark datasets show that in most cases CIEP is capable of improving the prediction performance.

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