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Record W4412120760 · doi:10.5194/epsc-dps2025-432

Experimental Simulation of Intimate Water Ice and Regolith Mixtures at Lyman-Alpha Wavelengths for Lunar Permanently Shadowed Region (PSR) Prospecting

2025· preprint· en· W4412120760 on OpenAlexaff
M. Z. Markovich, Kevin Axelrod, John E. Moores, Bill Morrow

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsYork University
Fundersnot available
KeywordsRegolithProspectingAstrobiologyWavelengthWater iceGeologyPhysicsAstronomyOpticsGeochemistry

Abstract

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Background: The Permanently Shadowed Regions (PSRs) of the Moon are thought to contain water ice [1], which is a vital resource for on-site production of breathable air, potable water and rocket fuel. While the study of PSRs on the Moon is a benefit to both space exploration and scientific knowledge, observing water ice in PSRs is difficult due to the low levels of scattered sunlight or earthshine in these locations. However, scattered sunlight from crater walls and starlight in the form of Lyman-alpha photons (121 nm) both provide a faint light source in the VUV spectrum that has previously been used for passive reflectance spectroscopy in PSRs [2, 3].Godin et al. [4] explored the feasibility of using Lyman-alpha wavelengths to detect water ice on the lunar surface. A key limitation of this study was the inability of the experimental setup to simulate intimate regolith-ice mixtures due to sublimation of water ice over time, resulting in the formation of a dust lag layer at the surface of samples no matter how little dust they contained.This lag presents significant limitations to our understanding of this technology’s application in lunar environments, since water ice is likely to exist as small ice grains mixed with regolith on the surface of PSRs, rather than as surface water frost [5, 6, 7]. This study bridges the current knowledge gap by tackling the issue of rapidly sublimating water ice in laboratory-simulated ice-regolith mixtures. Building upon the work done by Godin et al., we have developed a passive thermal shroud to limit the sublimation of water ice in intimate ice-regolith mixtures.Methodology: The shroud (fig. 1) is manufactured from copper due to the material’s high thermally conductivity and capacitance. The thermal shroud sits atop a cold plate, such that it is in contact with the liquid nitrogen exchanger. This will act as a barrier to the radiative heating from the chamber walls, thereby cooling the enclosed environment containing samples of water ice-regolith mixtures to prevent ice from sublimating in the time it takes for the lunar simulator to stabilize.The passive thermal shroud consists of several parts which fit together for easy and quick assembly inside the simulation chamber. The shroud also includes cutouts which will allow for the lamp and camera components to see the sample tray.This study follows the well-established methodology employed by Godin’s experiment, using a Vacuum Ultraviolet (VUV) Camera supplied by our industry partner, Resonance Ltd., to image intimate mixtures of lunar regolith simulant and ice in linearly increasing concentrations (i.e. starting from 10% ice, 90% regolith by weight). We expect to see linear variation in brightness detected by the VUV camera as water ice concentrations in ice-regolith samples are increased.Preliminary experiments indicate that with sufficient pre-cooling of all components (i.e. experimental chamber, sample tray and shroud tiles) water ice-regolith slurries remain intact, with no visible evidence of sublimation. Pictured in Figure 1 is a slurry with 10% by weight regolith and 90% by weight water ice after reaching 6×10-4 Torr in the cryovacuum chamber and being brought back to atmospheric conditions. Ice crystals are present throughout the sample, not just at a top layer, indicating that the water ice observed is not surface frost.Figure 1: Visual image of 10% regolith and 90% water ice slurry after cycle through cryovacuum chamber and brought back to atmospheric conditions. The sample sits atop a cold plate, within the assembled copper tiles forming the thermal shroud.We succesfully captured Lyman-alpha images of the sample pictured in Figure 1 with no evidence of a dust lag layer (fig.2). The same camera settings identified by Godin et al. were used to capture the UV images, with a gain of 29dB and exposure time of 3800ms. The sample is circled in red for visibility. Further investigations comparing Lyman-alpha images of water ice-regolith slurries against pure water ice and pure regolith samples will be done to ascertain a linear increase in brightness with increasing concentrations of regolith.Figure 2: Lyman-alpha UV image of 10% regolith and 90% water ice slurry, taken at 29dB gain and 3800ms exposure time.Once the detection capability of Lyman-alpha technology is verified for intimate mixtures of lunar regolith and water ice, this project can be expanded to investigate other factors which may affect imaging with a Lyman-alpha camera. The camera system may then be used to observe more complex ice mixtures known to exist on the lunar surface (i.e. containing water ice mixed with CH4, SO2, H2S and CO2), commonly referred to as “hypervolatiles” [8].Impact: This study will validate the use of Lyman-alpha cameras for in-situ detection of water ice in permanently shadowed regions of the Moon. Such technology will further the scientific community’s understanding lunar water ice properties and pave the way for further development and optimization of ISRU techniques and prospecting of water ice in the lunar PSRs.References: [1] Colaprete, A., et al. (2010) Science, 330(6003), 463–468. [2] Gladstone, G. R., et al. (2009) Space Science Reviews, 150(1-4), 161–181. [3] Kloos, J. L., Moores, J. E., Godin, P. J., & Cloutis, E. (2021) Acta Astronautica, 178, 432–451. [4] Godin, P. J., Kloos, J. L., Seguin, A., & Moores, J. E. (2020) Acta Astronautica, 177, 604–610. [5] Gladstone, G. R., et al. (2012) JGR, 117, E00H04. [6] Colaprete, A., et al. (2016) [White paper] NASA. [7] Hayne, Paul O., et al. (2015) Icarus, 255, 58-69. [8] Hayes, C. W., Minton, D. A., Kloos, J. L., & Moores, J. E. (2024) Journal of Geophysical Research Planets, 129(7).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.283
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
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