High-yield selective extraction of metal iron from JSC-1A using molten salt electrolysis
Bibliographic record
Abstract
Establishing a sustainable long-term human presence on the Moon and other celestial bodies requires the advancement of in-situ resource utilization (ISRU) for both oxygen and metals from the regolith. Among various metals, iron is considered a critical lunar resource due to its potential in construction, infrastructure, and energy systems. However, selective iron extraction from lunar regolith is challenging because of the complex composition of minerals. In this study, an FFC Cambridge process was employed to perform electrolysis of JSC-1 A in molten CaCl 2 at 900 with a variety of voltages between 1.0 and 1.75 V, allowing effective and selective extraction of metallic iron without contamination from the reduction of other metal oxides. During electrolysis, the reduced metallic iron was directly adhered onto the surface of the Nichrome cathode wire, leading to convenient collection and purification of the product. The optimal yield of pure metallic iron extraction and current efficiency from JSC-1 A were approximately 83.47 % and 79.65 %, respectively, achieved at 1.5 V. Comparing the electrochemical reduction behaviors of Fe 2 O 3 and FeO with the regolith matrix provided a better understanding of the different reaction stages during the process, which caused significant changes in the size and morphology of the produced metallic iron crystals. The deposited product on the cathode surface was also found to be affected by the concentration of iron oxide in the mixture. These findings demonstrate the feasibility of selective and efficient in-situ iron extraction from lunar regolith for future space missions through controlled electrochemical reduction. • Selective extraction of metallic iron from lunar regolith simulant (JSC-1A) via the FFC process. • High-purity and high-yield production enabled by reduction of powder-form simulant. • Elimination of additional chemical reducer to allow fully in-situ production of metal. • Identification of complex reduction stages of Fe from JSC-1A. • ISRP-based metallic feedstock production on the Moon supported by experimental finding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".