Sustainable transformation of rare earth metals value chain for dual-use technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rare earth metals (REMs), including lanthanides, scandium, and yttrium, are crucial for civilian and defense applications owing to their superior magnetic, optical, and catalytic properties. Their strategic importance extends to clean energy, electric vehicles, corrosion protection, agriculture, catalysis, and advanced weaponry. Although abundant in Earth’s crust, rare earth elements (REEs) are geologically dispersed and economically challenging to extract owing to similar ionic properties, creating supply chain risk largely attributed to China’s refinery capacity. In this review, we analyze the entire REM value chain, including classification, global distribution, mining, mineral processing, beneficiation (physical and chemical), leaching, and separation and purification, along with their high-performance applications. The geopolitical impact, market pressures, and processing complexities, including environmental hazards, purity management, and scale-up, are also discussed within the context of international policy responses. In response, strategies such as green metallurgy, closed-loop recycling, and green extraction techniques have been proposed to reduce environmental impact and supply vulnerability. A dual-use perspective is adopted, linking REEs 4f-driven properties to essential roles in both advanced civilian industries and defense technologies. Future pathways such as AI-enabled separation, digital tracking, and circular economy models are identified as routes to resilient and sustainable supply chains. By fostering innovation, diversification, and recycling, nations can reduce reliance on limited suppliers while meeting rising demand, thereby supporting sustainable growth and national security.
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.
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 | 0.000 | 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) | 0.000 | 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 it