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High abundance of solar wind-derived water in lunar soils from the middle latitude.

Yuchen XuHeng-Ci TianChi ZhangMarc ChaussidonYangting LinJialong HaoRuiying LiLixin GuWei YangLiying HuangJun DuYazhou YangYang LiuHuaiyu HeYongliao ZouXian-Hua LiFu-Yuan Wu
Published in: Proceedings of the National Academy of Sciences of the United States of America (2022)
Remote sensing data revealed that the presence of water (OH/H 2 O) on the Moon is latitude-dependent and probably time-of-day variation, suggesting a solar wind (SW)-originated water with a high degassing loss rate on the lunar surface. However, it is unknown whether or not the SW-derived water in lunar soil grains can be preserved beneath the surface. We report ion microprobe analyses of hydrogen abundances, and deuterium/hydrogen ratios of the lunar soil grains returned by the Chang'e-5 mission from a higher latitude than previous missions. Most of the grain rims (topmost ~100 nm) show high abundances of hydrogen (1,116 to 2,516 ppm) with extremely low δD values (-908 to -992‰), implying nearly exclusively a SW origin. The hydrogen-content depth distribution in the grain rims is phase-dependent, either bell-shaped for glass or monotonic decrease for mineral grains. This reveals the dynamic equilibrium between implantation and outgassing of SW-hydrogen in soil grains on the lunar surface. Heating experiments on a subset of the grains further demonstrate that the SW-implanted hydrogen could be preserved after burial. By comparing with the Apollo data, both observations and simulations provide constraints on the governing role of temperature (latitude) on hydrogen implantation/migration in lunar soils. We predict an even higher abundance of hydrogen in the grain rims in the lunar polar regions (average ~9,500 ppm), which corresponds to an estimation of the bulk water content of ~560 ppm in the polar soils assuming the same grain size distribution as Apollo soils, consistent with the orbit remote sensing result.
Keyphrases
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  • molecular dynamics
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  • single cell
  • organic matter
  • molecular dynamics simulations
  • deep learning
  • optical coherence tomography