Lunar Regolith as a Construction Material via In-Situ Thermite Reactions
Bibliographic record
Abstract
In-situ manufacturing and construction techniques must be researched and optimized to enable the sustainable development of lunar infrastructure. In this work, we present multi-parameter optimization of a novel, dual fuel, regolith based in-situ thermite material for additive manufacturing and construction on the Moon. Magnesium and aluminum are used as metal fuels and lunar regolith simulant is used as an oxidizer in thermite reactions to sinter metallic regolith samples. The loading of magnesium, aluminum, and simulant was varied across samples, as well as the simulant particle size to determine their effects on the combustion and final product. The combustion and physical properties of the materials were experimentally measured to demonstrate applicability to lunar AM and construction. The high reactivity of magnesium enabled reliable self-propagation, but excess magnesium created weak, porous products. Aluminum was less reactive and was not able to reliably cause a thermite reaction to self propagate, but did enabled greater mechanical strength of the products. The use of both metals in regolith-based thermite is demonstrated to provide reaction reliability and improved physical characteristics after combustion. The application to Martian regolith is discussed with sustainability implications and future technology developments.
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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 | 0.000 | 0.000 |
| 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".