hydrogen isotope composition
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2022 ◽  
Vol 216 ◽  
pp. 106338
Author(s):  
Nadine J. Kanik ◽  
Fred J. Longstaffe ◽  
Artur Kuligiewicz ◽  
Arkadiusz Derkowski

2022 ◽  
pp. 127416
Author(s):  
Fernando Gázquez ◽  
Luis Quindós ◽  
Daniel Rábago ◽  
Ismael Fuente ◽  
Santiago Celaya ◽  
...  

2021 ◽  
Vol 18 (19) ◽  
pp. 5363-5380
Author(s):  
Johannes Hepp ◽  
Christoph Mayr ◽  
Kazimierz Rozanski ◽  
Imke Kathrin Schäfer ◽  
Mario Tuthorn ◽  
...  

Abstract. The hydrogen isotope composition of leaf-wax-derived biomarkers, e.g., long-chain n-alkanes (δ2Hn-alkane), is widely applied in paleoclimate. However, a direct reconstruction of the isotope composition of source water based on δ2Hn-alkane alone is challenging due to the enrichment of heavy isotopes during evaporation. The coupling of δ2Hn-alkane with δ18O of hemicellulose-derived sugars (δ18Osugar) has the potential to disentangle this limitation and additionally to allow relative humidity reconstructions. Here, we present δ2Hn-alkane as well as δ18Osugar results obtained from leaves of Eucalyptus globulus, Vicia faba, and Brassica oleracea, which grew under controlled conditions. We addressed the questions of (i) whether δ2Hn-alkane and δ18Osugar values allow reconstructions of leaf water isotope composition, (ii) how accurately the reconstructed leaf water isotope composition enables relative humidity (RH) reconstruction, and (iii) whether the coupling of δ2Hn-alkane and δ18Osugar enables a robust source water calculation. For all investigated species, the n-alkane n-C29 was most abundant and therefore used for compound-specific δ2H measurements. For Vicia faba, additionally the δ2H values of n-C31 could be evaluated robustly. Regarding hemicellulose-derived monosaccharides, arabinose and xylose were most abundant, and their δ18O values were therefore used to calculate weighted mean leaf δ18Osugar values. Both δ2Hn-alkane and δ18Osugar yielded significant correlations with δ2Hleaf water and δ18Oleaf water, respectively (r2=0.45 and 0.85, respectively; p<0.001, n=24). Mean fractionation factors between biomarkers and leaf water were found to be −156 ‰ (ranging from −133 ‰ to −192 ‰) for εn-alkane/leaf water and +27.3 ‰ (ranging from +23.0 ‰ to 32.3 ‰) for εsugar/leaf water, respectively. Modeled RHair values from a Craig–Gordon model using measured Tair, δ2Hleaf water and δ18Oleaf water as input correlate highly significantly with modeled RHair values (R2=0.84, p<0.001, RMSE = 6 %). When coupling δ2Hn-alkane and δ18Osugar values, the correlation of modeled RHair values with measured RHair values is weaker but still highly significant, with R2=0.54 (p<0.001, RMSE = 10 %). Finally, the reconstructed source water isotope composition (δ2Hs and δ18Os) as calculated from our coupled approach matches the source water in the climate chamber experiment (δ2Htank water and δ18Otank water). This highlights the great potential of the coupled δ2Hn-alkane–δ18Osugar paleohygrometer approach for paleoclimate and relative humidity reconstructions.


2021 ◽  
Author(s):  
Sen Hu ◽  
Huicun He ◽  
Jianglong Ji ◽  
Yangting Lin ◽  
Hejiu Hui ◽  
...  

Abstract The distribution of water in the Moon’s interior carries key implications for the origin of the Moon1, the crystallisation of the lunar magma ocean2, and the duration of lunar volcanism2. The Chang’E-5 (CE5) mission returned the youngest mare basalt samples, dated at ca. 2.0 billion years ago3, from the northwestern Procellarum KREEP Terrane (PKT), providing a probe into the spatio-temporal evolution of lunar water. Here we report the water abundance and hydrogen isotope composition of apatite and ilmenite-hosted melt inclusions from CE5 basalts, from which we derived a maximum water abundance of 370 ± 30 μg.g-1 and a δD value (-330 ± 160‰) for their parent magma. During eruption, hydrogen degassing led to an increase in the D/H ratio of the residual melts up to δD values of 300-900‰. Accounting for low degrees of mantle partial melting followed by extensive magma fractional crystallisation4, we estimate a maximum mantle water abundance of 2-6 μg.g-1, which are too low for water contents alone to account for generating the Moon’s youngest basalts. Such modest water abundances for the lunar mantle are at the lower end of those estimated from mare basalts that erupted from ca. 4.0-2.8 Ga5, 6, suggesting the mantle source of CE5 basalts dried up by ca. 2.0 Ga through previous melt extraction from the PKT mantle during prolonged volcanic activity.


2021 ◽  
Author(s):  
Meisha Holloway-Philips ◽  
Jochem Baan ◽  
Daniel Nelson ◽  
Guillaume Tcherkez ◽  
Ansgar Kahmen

&lt;p&gt;The hydrogen isotope composition (&amp;#948;&lt;sup&gt;2&lt;/sup&gt;H) of cellulose has been used to assess ecohydrological processes and carries metabolic information, adding new understanding to how plants respond to environmental change. However, experimental approaches to isolate drivers of &amp;#948;&lt;sup&gt;2&lt;/sup&gt;H variation is limited to the Yakir &amp; DeNiro model (1990), which is difficult to implement and largely unvalidated. Notably, the two biosynthetic fractionation factors in the model, associated with photosynthetic (&amp;#949;&lt;sub&gt;A&lt;/sub&gt;) and post-photosynthetic (&amp;#949;&lt;sub&gt;H&lt;/sub&gt;) processes are currently accepted as constants, and the third parameter &amp;#8211; the extent to which organic molecules exchange hydrogen (f&lt;sub&gt;H&lt;/sub&gt;) with local water &amp;#8211; is usually tuned in order to resolve the difference between modelled and observed cellulose &amp;#948;&lt;sup&gt;2&lt;/sup&gt;H values. Thus, by virtue, the metabolically interpretable parameter is only f&lt;sub&gt;H&lt;/sub&gt;, whilst from theory, metabolic flux rates will also impact on the apparent fractionations. To overcome part of this limitation, we measured the &amp;#948;&lt;sup&gt;2&lt;/sup&gt;H of extracted leaf sucrose from fully-expanded leaves of seven species and a phosphoglucomutase &amp;#8216;starchless&amp;#8217; mutant of tobacco to estimate the isotopic offset between sucrose and leaf water (&amp;#949;&lt;sub&gt;sucrose&lt;/sub&gt;). Sucrose &amp;#948;&lt;sup&gt;2&lt;/sup&gt;H explained ~60% of the &amp;#948;&lt;sup&gt;2&lt;/sup&gt;H variation observed in cellulose. In general, &amp;#949;&lt;sub&gt;sucrose&lt;/sub&gt;&amp;#160;was higher (range: -203&amp;#8240; to -114&amp;#8240;; mean: -151 &amp;#177; 21&amp;#8240;) than the currently accepted value of -171&amp;#8240; (&amp;#949;&lt;sub&gt;A&lt;/sub&gt;) reflecting &lt;sup&gt;2&lt;/sup&gt;H-enrichment downstream of triose-phosphate export from the chloroplast, with statistical differences in &amp;#949;&lt;sub&gt;sucrose&lt;/sub&gt;&amp;#160;observed between species estimates. The remaining &amp;#948;&lt;sup&gt;2&lt;/sup&gt;H variation in cellulose was explained by species differences in f&lt;sub&gt;H&amp;#160;&lt;/sub&gt;(estimated by assuming &amp;#949;&lt;sub&gt;H &lt;/sub&gt;= +158&amp;#8240;). We also tested possible links between model parameters and plant metabolism. &amp;#949;&lt;sub&gt;sucrose&lt;/sub&gt;&amp;#160;was positively related to dark respiration (R&lt;sup&gt;2&lt;/sup&gt;=0.27) suggesting an important branch point influencing sugar &amp;#948;&lt;sup&gt;2&lt;/sup&gt;H. In addition, f&lt;sub&gt;H&lt;/sub&gt; was positively related to the turnover time (&amp;#964;) of water-soluble carbohydrates (R&lt;sup&gt;2&lt;/sup&gt;=0.38), but only when estimated using fixed &amp;#949;&lt;sub&gt;A &lt;/sub&gt;= -171&amp;#8240;. To decipher and isolate the &amp;#8220;metabolic&amp;#8221; information contained within &amp;#948;&lt;sup&gt;2&lt;/sup&gt;H values of cellulose it will be important to assess &amp;#948;&lt;sup&gt;2&lt;/sup&gt;H values of non-structural carbohydrates so that hydrogen isotope fractionation during sugar metabolism can be better understood. This study provides the first attempt at such measurements showing species differences in both source and sink processes are important in understanding &amp;#948;&lt;sup&gt;2&lt;/sup&gt;H variation of cellulose.&lt;/p&gt;


2019 ◽  
Vol 33 (12) ◽  
pp. 12758-12766
Author(s):  
Yi Duan ◽  
Yingzhong Wu ◽  
Zhongping Li ◽  
Lantian Xing ◽  
Ting Zhang

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