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Institution

Xi'an Jiaotong University

EducationXi'an, China
About: Xi'an Jiaotong University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Heat transfer & Dielectric. The organization has 85440 authors who have published 99682 publications receiving 1579683 citations. The organization is also known as: '''Xi'an Jiaotong University''' & Xi'an Jiao Tong University.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors present a new paradigm to fully recycle epoxy-based carbon fiber reinforced polymer (CFRP) composites, where the epoxy matrix can be dissolved as the EG molecules participate in bond exchange reactions (BERs) within the covalent adaptable network (CAN), effectively breaking the long polymer chains into small segments.
Abstract: Both environmental and economic factors have driven the development of recycling routes for the increasing amount of composite waste generated. This paper presents a new paradigm to fully recycle epoxy-based carbon fiber reinforced polymer (CFRP) composites. After immersing the composite in ethylene glycol (EG) and increasing the temperature, the epoxy matrix can be dissolved as the EG molecules participate in bond exchange reactions (BERs) within the covalent adaptable network (CAN), effectively breaking the long polymer chains into small segments. The clean carbon fibers can be then reclaimed with the same dimensions and mechanical properties as those of fresh ones. Both the dissolution rate and the minimum amount of EG required to fully dissolve the CAN are experimentally determined. Further heating the dissolved solution leads to repolymerization of the epoxy matrix, so a new generation of composite can be fabricated by using the recycled fiber and epoxy; in this way a closed-loop near 100% recycling paradigm is realized. In addition, epoxy composites with surface damage are shown to be fully repaired. Both the recycled and the repaired composites exhibit the same level of mechanical properties as fresh materials.

310 citations

Journal ArticleDOI
TL;DR: Nursing is important in quality and safety of hospital care and in patients' perceptions of their care, and expanding the number of baccalaureate-prepared nurses hold promise for improving hospital outcomes in China.

310 citations

Journal ArticleDOI
TL;DR: This paper presents a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part, and develops an efficient algorithm for solving the resulting optimization problem by using the augmented Lagrange multiplier method.
Abstract: Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, etc. Such complex noise could degrade the quality of the acquired HSIs, limiting the precision of the subsequent processing. In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part. Specifically, for the clean HSI part, we use tensor Tucker decomposition to describe the global correlation among all bands, and an anisotropic spatial–spectral total variation regularization to characterize the piecewise smooth structure in both spatial and spectral domains. For the mixed noise part, we adopt the $\ell _1$ norm regularization to detect the sparse noise, including stripes, impulse noise, and dead pixels. Despite that TV regularization has the ability of removing Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian noise for some real-world scenarios. Then, we develop an efficient algorithm for solving the resulting optimization problem by using the augmented Lagrange multiplier method. Finally, extensive experiments on simulated and real-world noisy HSIs are carried out to demonstrate the superiority of the proposed method over the existing state-of-the-art ones.

310 citations

Journal ArticleDOI
TL;DR: A comprehensive review on the effect of water on the state-of-the-art lead-based perovskite solar cells is provided in this paper, where it is shown that a moderate amount of water can facilitate nucleation and crystallization of the perovsite material, resulting in better perov-skite film quality and enhanced PSC performance.
Abstract: The performance and stability of organic–inorganic hybrid perovskite solar cells (PSCs) is sensitive to water and moisture in an ambient environment. Understanding how H2O influences the perovskite material is also important for developing appropriate control strategies to mitigate the problem. Here we provide a comprehensive review on the effect of water on the state-of-the-art lead-based perovskite solar cells in terms of perovskite material design, perovskite film preparation, device fabrication, and photovoltaic application. It is found that a moderate amount of water can facilitate nucleation and crystallization of the perovskite material, resulting in better perovskite film quality and enhanced PSC performance. The perovskite materials are irreversibly destroyed by H2O after a certain level of water, but they exihibit better tolerance than initially expected. Humidity resistant fabrication of high-performance PSC devices and modules should therefore be favoured. Generally, water shows a negative effect on the long-term stability and lifetime of PSCs. To reduce the effects from water during outdoor operation, attention should be paid to different protection methods such as varying the perovskite composition, optimizing the electron/hole transport layer and encapsulation of the device.

310 citations

Journal ArticleDOI
TL;DR: In this paper, the concept of enhancing parabolic convective heat transfer by reducing the intersection angle between velocity and temperature gradient is reviewed and extended to elliptic fluid flow and heat transfer situation.

310 citations


Authors

Showing all 86109 results

NameH-indexPapersCitations
Feng Zhang1721278181865
Yang Yang1642704144071
Jian Yang1421818111166
Lei Zhang130231286950
Yang Liu1292506122380
Jian Zhou128300791402
Chao Zhang127311984711
Bin Wang126222674364
Xin Wang121150364930
Bo Wang119290584863
Xuan Zhang119153065398
Jian Liu117209073156
Andrey L. Rogach11757646820
Yadong Yin11543164401
Xin Li114277871389
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023306
20221,655
202111,508
202011,183
201910,012
20188,215