Instytut Podstawowych Problemów Techniki
Polskiej Akademii Nauk


Yafei Xue

South China Normal University (CN)

Ostatnie publikacje
1.  Xue YP., Jang H., Byra M., Cai ZY., Wu M., Chang EY., Ma YJ., Su J., Automated cartilage segmentation and quantification using 3D ultrashort echo time (UTE) cones MR imaging with deep convolutional neural networks, European Radiology, ISSN: 1432-1084, DOI: 10.1007/s00330-021-07853-6, Vol.31, pp.7653-7663, 2021

Objective: To develop a fully automated full-thickness cartilage segmentation and mapping of T1, T1ρ, and T2*, as well as macromolecular fraction (MMF) by combining a series of quantitative 3D ultrashort echo time (UTE) cones MR imaging with a transfer learning–based U-Net convolutional neural networks (CNN) model. Methods: Sixty-five participants (20 normal, 29 doubtful-minimal osteoarthritis (OA), and 16 moderate-severe OA) were scanned using 3D UTE cones T1 (Cones-T1), adiabatic T1ρ (Cones-AdiabT1ρ), T2* (Cones-T2*), and magnetization transfer (Cones-MT) sequences at 3 T. Manual segmentation was performed by two experienced radiologists, and automatic segmentation was completed using the proposed U-Net CNN model. The accuracy of cartilage segmentation was evaluated using the Dice score and volumetric overlap error (VOE). Pearson correlation coefficient and intraclass correlation coefficient (ICC) were calculated to evaluate the consistency of quantitative MR parameters extracted from automatic and manual segmentations. UTE biomarkers were compared among different subject groups using one-way ANOVA. Results: The U-Net CNN model provided reliable cartilage segmentation with a mean Dice score of 0.82 and a mean VOE of 29.86%. The consistency of Cones-T1, Cones-AdiabT1ρ, Cones-T2*, and MMF calculated using automatic and manual segmentations ranged from 0.91 to 0.99 for Pearson correlation coefficients, and from 0.91 to 0.96 for ICCs, respectively. Significant increases in Cones-T1, Cones-AdiabT1ρ, and Cones-T2* (p < 0.05) and a decrease in MMF (p < 0.001) were observed in doubtful-minimal OA and/or moderate-severe OA over normal controls. Conclusion: Quantitative 3D UTE cones MR imaging combined with the proposed U-Net CNN model allows a fully automated comprehensive assessment of articular cartilage.

Słowa kluczowe:
deep learning, cartilage, biomarkers, osteoarthritis

Afiliacje autorów:
Xue YP. - South China Normal University (CN)
Jang H. - University of California (US)
Byra M. - IPPT PAN
Cai ZY. - inna afiliacja
Wu M. - University of California (US)
Chang EY. - University of California (US)
Ma YJ. - University of California (US)
Su J. - inna afiliacja
2.  Chen R., Xue Yafei ., Xu X., Yang H., Qiu T., Shui Lingling ., Wang Xin ., Zhou G., Giersig M., Pidot S., Hutchison J.A ., Akinoglu E.M., Lithography-free synthesis of periodic, vertically-aligned, multi-walled carbon nanotube arrays, NANOTECHNOLOGY, ISSN: 0957-4484, DOI: 10.1088/1361-6528/ac345a, Vol.33, No.065304 , pp.1-9, 2021

Until now, the growth of periodic vertically aligned multi-walled carbon nanotube (VA-
MWCNT) arrays was dependent on at least one lithography step during fabrication. Here, we demonstrate a lithography-free fabrication method to grow hexagonal arrays of self-standing VA-MWCNTs with tunable pitch and MWCNT size. The MWCNTs are synthesized by plasma enhanced chemical vapor deposition (PECVD) from Ni catalyst particles. Template guided dewetting of a thin Ni film on a hexagonally close-packed silica particle monolayer provides periodically distributed Ni catalyst particles as seeds for the growth of the periodic MWCNT arrays. The diameter of the silica particles directly controls the pitch of the periodic VA-MWCNT arrays from 600 nm to as small as 160 nm. The diameter and length of the individual MWCNTs can also be readily adjusted and are a function of the Ni particle size and PECVD time. This unique method of lithography-free growth of periodic VA-MWCNT arrays can be utilized for the fabrication of large-scale biomimetic materials

Słowa kluczowe:
periodic, ithography free, nanofabrication, template guided, vertically-aligned multi- walled carbon nanotubes, self-standing

Afiliacje autorów:
Chen R. - inna afiliacja
Xue Yafei . - South China Normal University (CN)
Xu X. - inna afiliacja
Yang H. - South China Normal University (CN)
Qiu T. - inna afiliacja
Shui Lingling . - South China Normal University (CN)
Wang Xin . - inna afiliacja
Zhou G. - South China Normal University (CN)
Giersig M. - IPPT PAN
Pidot S. - inna afiliacja
Hutchison J.A . - inna afiliacja
Akinoglu E.M. - University of Melbourne (AU)
3.  Bozheyev F., Akinoglu E.M., Wu L., Lu H., Nemkayeva R., Xue Y., Jin M., Giersig M., Band gap optimization of tin tungstate thin filmsfor solar water oxidation, International Journal of Hydrogen Energy, ISSN: 0360-3199, DOI: 10.1016/j.ijhydene.2020.01.126, Vol.45, No.15, pp.8676-8685, 2020

Semiconducting ternary metal oxide thin films exhibit a promising application for solarenergy conversion. However, the efficiency of the conversion is still limited by a band gapof a emiconductor, which determines an obtainable internal photovoltage for solar watersplitting. In this report the tunability of the tin tungstate band gap by O2 partial pressurecontrol in the magnetron co-sputtering process is shown. A deficiency in the Sn concentration increases the optical band gap of tin ungstate thin films. The optimum band gap of 1.7 eV for tin tungstate films is achieved for a Sn to W ratio at unity, which establishes thehighest photoelectrochemical activity. In particular, a maximum photocurrent density of 0.375 mA cm^2 at 1.23 VRHE and the lowest reported onset potential of -0.24 VRHE for SnWO4 thin films without any co-catalyst are achieved. Finally, we demonstrate that a Ni protection layer on the SnWO4 thin film enhances the photoelectrochemical stability, which isof paramount importance for application.

Słowa kluczowe:
thin film, tin tungstate, reactive magnetron sputtering, photocurrent density, thickness band gap

Afiliacje autorów:
Bozheyev F. - inna afiliacja
Akinoglu E.M. - University of Melbourne (AU)
Wu L. - inna afiliacja
Lu H. - South China Normal University (CN)
Nemkayeva R. - inna afiliacja
Xue Y. - South China Normal University (CN)
Jin M. - South China Normal University (CN)
Giersig M. - inna afiliacja

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