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您的检索式：作者名="Hanfei Yan"
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| 1 | Paleoclimatic indication of X-ray fluorescence core-scanned Rb/Sr ratios: A case study in the Zoige Basin in the eastern Tibetan Plateau显示文摘The X-ray fluorescence(XRF)core-scanned Rb/Sr ratios of lake sediments have been widely used as a proxy for chemical weathering intensity and past climate change.However,some factors could affect Rb/Sr ratios,causing misinterpretation and limiting its application.In this study,we present a high-resolution XRF core-scanned Rb/Sr record of core ZB13-C1 from the Zoige Basin in the eastern Tibetan Plateau.To validate its application,we correlated this record with the chemical index of alteration(CIA)and other paleoclimatic proxies.Our results showed that(1)the core-scanned Rb/Sr ratios and CIA were reliable proxies of chemical weathering intensity in fine-grained sedimentary sequences;(2)the low values of corescanned Rb/Sr ratios and CIA were significantly correlated with high total organic carbon content,arboreal pollen content,carbonate content,and C/N ratios,confirming its reliability as a proxy for the Asian summer monsoon intensity;(3)the corescanned Rb/Sr ratios at core depths of 25–0.3 and 56–47 m were unable to reliably reflect chemical weathering intensity due to both the grain-size effect and the low test accuracy.Our study highlights the need for mutual verification of multiple indicators before accurately applying Rb/Sr as a paleoclimatic proxy in other similar study areas. | Hanfei YANG Yan ZHAO Qiaoyu CUI Weihe REN Quan LI | 2021 | Science China Earth Sciences2021，64，1： | 5 |
| 2 | Focusing of hard X-rays to 16 nanometers with a multilayer Laue lens 显示文摘 | Hyon Chol Kang Hanfei Yan Robert P Winarski a al | 2008 | Appl Phys Lett2008，92，22： | 1 |
| 3 | Deep learning enables nanoscale X-ray 3D imaging with limited data显示文摘Deep neural network can greatly improve tomography reconstruction with limited data.A recent effort of combining ptycho-tomography model with the 3D U-net demonstrated a significant reduction in both the number of projections and computation time,and showed its potential for integrated circuit imaging that requires high-resolution and fast measurement speed. | Chonghang Zhao Hanfei Yan | 2023 | Light(Science & Applications)2023，12，7： | 0 |
| 4 | X-ray focusing with efficient high-NA multilayer Laue lenses显示文摘Multilayer Laue lenses are volume diffraction elements for the efficient focusing of X-rays.With a new manufacturing technique that we introduced,it is possible to fabricate lenses of sufficiently high numerical aperture(NA)to achieve focal spot sizes below 10 nm.The alternating layers of the materials that form the lens must span a broad range of thicknesses on the nanometer scale to achieve the necessary range of X-ray deflection angles required to achieve a high NA.This poses a challenge to both the accuracy of the deposition process and the control of the materials properties,which often vary with layer thickness.We introduced a new pair of materials—tungsten carbide and silicon carbide—to prepare layered structures with smooth and sharp interfaces and with no material phase transitions that hampered the manufacture of previous lenses.Using a pair of multilayer Laue lenses(MLLs)fabricated from this system,we achieved a two-dimensional focus of 8.4×6.8 nm2 at a photon energy of 16.3 keV with high diffraction efficiency and demonstrated scanning-based imaging of samples with a resolution well below 10 nm.The high NA also allowed projection holographic imaging with strong phase contrast over a large range of magnifications.An error analysis indicates the possibility of achieving 1 nm focusing. | Saša Bajt Mauro Prasciolu Holger Fleckenstein Martin Domaracký Henry N Chapman Andrew J Morgan Oleksandr Yefanov Marc Messerschmidt Yang Du Kevin T Murray Valerio Mariani Manuela Kuhn Steven Aplin Kanupriya Pande Pablo Villanueva-Perez Karolina Stachnik Joe PJ Chen Andrzej Andrejczuk Alke Meents Anja Burkhardt David Pennicard Xiaojing Huang Hanfei Yan Evgeny Nazaretski Yong S Chu Christian E Hamm | 2017 | Light(Science & Applications)2017，6，1： | 0 |
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