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Byline: Hongfei Zhang (1), Nigel Harris (2), Liang Guo (1), Wangchun Xu (1) Keywords: U--Pb zircon dating; Geochemistry; Sr--Nd--Hf isotopes; Granites; Eastern Himalayan syntaxis Abstract: The major and trace-element geochemistry, Sr--Nd bulk-rock isotopes, U--Pb zircon chronology and Lu--Hf isotopic compositions are described for three granitic bodies which intrude the Nyingchi gneisses (Lhasa terrane) along the western margin of the eastern Himalayan syntaxis. The Bayi two-mica granite and Lunan granite--granodiorite were intruded at 22 +- 1 and 25.4 +- 0.3 Ma, respectively, whereas the Confluence biotite granite was emplaced at 49.1 +- 0.4 Ma. All share strong depletions in Y and HREE requiring a garnet-bearing source both during and following the Eocene collision of the Indian plate with the Lhasa terrane. The isotope geochemistry of these intrusives ([epsilon] .sub.Nd(t) = -3 to -5, 87.sup.Sr/ [.sup.86]Sr.sub.(t) = 0.706--0.707) indicates a crustal source within the Lhasa terrane. Sr--Nd systematics of the garnet-bearing Nyingchi gneisses together with the U--Pb and Lu--Hf isotopic ratios of detrital zircons recovered from this unit identifies it as a potential melt source. The combined element and isotope geochemistry of the plutons indicate a mixed source the gneisses provide the older component whereas the Gangdese batholith provides a younger, siliceous component. The involvement of garnet-bearing crustal material in melt sources from the Cretaceous (80 Ma) to the Miocene (20 Ma) is consistent with the presence of a thicker continental crust in the eastern Lhasa terrane, as is the presence of magmatic epidote in several plutons which indicates a regional deepening level of exposure eastwards. Post-collision crustal melting is synchronous with proposed slab break-off during the early Miocene, suggesting advective heating by rising asthenospheric melts. Author Affiliation: (1) State Key Laboratory of Geological Processes and Mineral Resources and Faculty of Earth Sciences, China University of Geosciences, 430074, Wuhan, China (2) Department of Earth and Environmental Sciences, The Open University, Milton Keynes, MK7 6AA, UK Article History: Registration Date: 16/11/2009 Received Date: 14/05/2009 Accepted Date: 16/11/2009 Online Date: 01/12/2009 Article note: Communicated by J. Hoefs.

• Applications of characterization approaches in asphalt performance prediction were reviewed. • The relationship between MD simulation parameters and macro-properties was summarized. • The potential links between micro–macro scales were discussed. • The combination of characterization and simulation approaches was prospected. Examining the intricate relationship between micro-components and macro-performance represents a pivotal focus within asphalt materials research. This review offers a comprehensive discussion on the micro–macro correlations in asphalt, delineating these correlations from two primary perspectives: characterization approaches and molecular dynamic (MD) simulation. This review begins by outlining foundational principles and assessment methodologies concerning saturates, aromatics, resins, and asphaltenes (SARA) components separation method, as well as techniques such as Fourier-transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR), and gel permeation chromatography (GPC) methods. The application of the above methods in examining the relationship between chemical structure, components, and performance is highlighted, offering both qualitative and quantitative insights. The review then considers how MD simulation contributes to the investigation of SARA fractions interactions and analyses of physical, rheological, self-healing, and interfacial adhesion performance in asphalt materials, within the broader context of the micro–macro links. Finally, a proposition is forwarded to amalgamate characterization methodologies with data obtained from MD simulations, aiming to establish a comprehensive material information database. This initiative seeks to develop a robust structure–activity relationship model for asphalt, utilizing advanced machine learning (ML) methodologies. This review not only advances comprehension regarding the intricate components of asphalt but also furnishes a foundation for the targeted design and optimization of superior-quality asphalt materials. [ABSTRACT FROM AUTHOR]