Molecular hydrogen isotope analysis of living and fossil plants—Metasequoia as an example

来源 :Progress in Natural Science | 被引量 : 0次 | 上传用户:yayayda
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Molecular hydrogen isotope analysis preformed on modern and fossil plants has made a significant impact on diverse research fields in biology and geology. Using living and fossil Metasequoia as an example, we review the technology of online GC-IRMS that made the molecular analysis of hydrogen isotope possible and discuss critical issues concerning with the studies of molecular dD and its applications. The apparent hydrogen fractionation factors between lipid molecules and source water (ewater–lipid) vary across plant taxonomy and differ among biomolecules and are affected by multiple environmental factors in which precipitation dD values exercise the first order of control. Eco-physiological factors and environmental parameters are also known to influence dD in plants. Molecular hydrogen isotope analysis of chemically stable lipid molecules, such as n-alkanes, finds a wide range of applications in detecting source sediments, reconstruction of paleoclimatic parameters, inference of air-mass trajectory, as well as in petroleum industry and environmental studies. Molecular hydrogen isotope analysis preformed on modern and fossil plants has made a significant impact on diverse research fields in biology and geology. Using living and fossil Metasequoia as an example, we review the technology of online GC-IRMS that made the molecular analysis of hydrogen isotope possible and discuss critical concerning concerning the studies of molecular dD and its applications. The apparent hydrogen fractionation factors between lipid molecules and source water (ewater-lipid) vary across plant taxonomy and differ among biomolecules and are affected by multiple environmental factors in which precipitation Molecular-hydrogen isotope analysis of chemically stable lipid molecules, such as n-alkanes, finds a wide range of applications in detecting source sediments, reconstruction of paleoclimatic parameters, inference of ai r-mass trajectory, as well as in petroleum industry and environmental studies.
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