Laser-induced Breakdown Spectroscopy of Ice-regolith Mixtures: Implications for Measurements on Planetary Surfaces
Bibliographic record
Abstract
Abstract Laser-induced breakdown spectroscopy (LIBS) is an analytical technique enabling in situ chemical analysis of planetary surfaces. Its use on the Mars Science Laboratory and Mars 2020 missions has demonstrated the potential for quantifying water in hydrated minerals, prompting investigations into its application for detecting and quantifying water ice in lunar regolith. Although promising results have been reported under vacuum conditions, previous measurements do not fully represent the range of physical forms that ice-regolith mixtures may take on the Moon. In this study, we use a scanning LIBS micro-analyzer to assess the main sources of signal variance and the response of the Hα emission line to 0–40 wt% water ice in two types of ice-regolith mixtures. We find that the primary factor influencing hydrogen emission is enhanced laser coupling with larger grains in wet or ice-cemented regolith due to increased cohesion. Emission from “cemented” ice-regolith mixtures exhibits increasing Hα intensity up to ∼15 wt%, followed by a decline attributed to water saturation. In contrast, emission from “discrete” ice-regolith mixtures shows no consistent Hα response in the 0–10 wt% range. Regression models trained on physically diverse mixtures reduce the rms error of predicted H2O content by 5.6% across the 0–40 wt% range. These results highlight the need for calibration using geologic materials that reflect grain size, porosity, and type of ice-regolith mixture. They also demonstrate the value of scanning LIBS technologies for identifying sources of signal variability in planetary applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".