Sustainable transformation of rare earth metals value chain for dual-use technologies
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".