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454 results on '"Wang, Xianlong"'

31. Dynamic glucose‐enhanced MRI of gliomas: A preliminary clinical application.

Author

Mo, Jianhua, Xu, Xiang, Ma, Andong, Lu, Mingjun, Wang, Xianlong, Rui, Qihong, Zhu, Jianbin, Wen, Haitao, Lin, Genyun, Knutsson, Linda, van Zijl, Peter, and Wen, Zhibo

Subjects
BRAIN tumors, CONTRAST media, SMALL molecules, GRAY matter (Nerve tissue), WHITE matter (Nerve tissue)
Abstract

The study aimed to investigate the feasibility of dynamic glucose‐enhanced (DGE) MRI technology in the clinical application of glioma. Twenty patients with glioma were examined using a preoperative DGE‐MRI protocol before clinical intervention. A brief hyperglycemic state was achieved by injecting 50 mL of 50% w/w D‐glucose intravenously during the DGE imaging. The total acquisition time for the DGE was 15 min. Area‐under‐the‐curve (AUC) images were calculated using the DGE images. AUC2–7min values of the glioma core, margin area, edema area, and contralateral brain parenchyma were compared using Mann–Whitney U tests. Overall, gray and white matter areas in the AUC images showed relatively low DGE signal change and bilateral symmetry. However, the tumor cores displayed a significant hyperintensity. A high DGE signal change was also seen in the necrotic, cystic, and cerebrospinal areas. These results show that DGE MRI is a feasible technique for the study of brain tumors as part of a clinical exam. Importantly, DGE MRI showed enhancement in areas confirmed histopathologically as tumors, whereas Gd T1w MRI did not show any enhancement in this area. Since the D‐glucose molecule is smaller than Gd‐based contrast agents, DGE MRI may be more sensitive to subtle blood–brain barrier disruptions, thus potentially providing early information about possible malignancy. These findings provide a new perspective for the further exploration and analysis of D‐glucose uptake in brain tumors. [ABSTRACT FROM AUTHOR]

Published
2025
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33. Realized stable BP-N at ambient pressure by phosphorus doping.

Author

Chen, Guo, Zhang, Chengfeng, Zhu, Yuanqin, Cao, Bingqing, Zhang, Jie, and Wang, Xianlong

Subjects
DYNAMIC stability, DOPING agents (Chemistry), STRUCTURAL stability, ELECTROSTATIC interaction, ENERGY density
Abstract

Black-phosphorus-structured nitrogen (BP–N) is an attractive high-energy-density material. However, high-pressure-synthesized BP-N will decompose at low pressure and cannot be quenched to ambient conditions. Finding a method to stabilize it at 0 GPa is of great significance for its practical applications. However, unlike cubic gauche, layered polymeric, and hexagonal layered polymeric nitrogen (cg-N, LP-N, and HLP-N), it is always a metastable phase at high pressures up to 260 GPa, and decomposes into chains at 23 GPa. Here, on the basis of first-principles simulations, we find that P-atom doping can effectively reduce the synthesis pressure of BP-N and maintain its stability at 0 GPa. Uniform distribution of P-atom dopants within BP-N layers helps maintain the structural stability of these layers at 0 GPa, while interlayer electrostatic interaction induced by N–P dipoles enhances dynamic stability by eliminating interlayer slipping. Furthermore, pressure is conducive to enhancing the stability of BP-N and its doped forms by suppressing N-chain dissociation. For a configuration with 12.5% doping concentration, a gravimetric energy density of 8.07 kJ/g can be realized, which is nearly twice that of trinitrotoluene. [ABSTRACT FROM AUTHOR]

Published
2025
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50. A machine learning methodology for investigating the liquid–liquid transition of hydrogen under high-pressure.

Author

Li, Shao, Zhang, Chuanguo, Wang, Xianlong, and Zeng, Zhi

Subjects
ARTIFICIAL neural networks, POTENTIAL energy surfaces, DENSITY functional theory, MACHINE learning, SUPERFLUIDITY
Abstract

Due to its unique properties such as superconductivity and superfluidity, high-pressure properties of hydrogen attract a lot of attention. However, the Liquid–Liquid Transition (LLT) of hydrogen under high-pressure and high-temperature is of particular significance for understanding its metallization. We propose a data-driven machine learning approach based on the density functional theory data to fit the potential energy surface into a deep neural network form. This method overcomes the simulation scale limitations of first-principles approaches to investigate the dissociation behavior of hydrogen molecules under high pressure. Our findings reveal an LLT curve exhibiting a first-order continuous change along with transition zone corresponding to hydrogen molecule dissociation. This study offers valuable insights into the LLT phenomenon and the metallization of hydrogen under high pressure. [ABSTRACT FROM AUTHOR]

Published
2024
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