Ultra-Portable Solar-Powered 3D Printers for Onsite Manufacturing of Medical Resources
Bibliographic record
Abstract
INTRODUCTION: The first space-based fused deposition modeling (FDM) 3D printer is powered by solar photovoltaics. This study seeks to demonstrate the feasibility of using solar energy to power a FDM 3D printer to manufacture medical resources at the Mars Desert Research Station and to design an ultra-portable solar-powered 3D printer for off-grid environments. METHODS: Six solar panels in a 3×2 configuration, a voltage regulator/capacitor improvised from a power adapter, and two 12V batteries in series were connected to power a FDM 3D printer. Three designs were printed onsite and evaluated by experts post analogue mission. A solar-powered 3D printer composed of off-the-shelf components was designed to be transported in airline carry-on luggage. RESULTS: During the analogue mission, the solar-powered printer could only be operated for <1 h/d, but was able to fabricate a functional dental tool, scalpel handle, and customized mallet splint over 2 d. Post analogue mission, an ultra-portable plug-and-play solar-powered 3D printer was designed that could print an estimated 16 dental tools or 8 mallet finger splints or 7 scalpel handles on one fully charged 12V 150Wh battery with a 110V AC converter. CONCLUSION: It is feasible to use solar energy to power a 3D printer to manufacture functional and personalized medical resources at a Mars analogue research station. Based on these findings, a solar-powered suitcase 3D printing system containing solar panels, 12V battery with charge controller and AC inverter, and back-up solar charge controller and inverter was designed for transport to and use in off-grid communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".