A comprehensive review on rare earth elements: resources, technologies, applications, and prospects
Bibliographic record
Abstract
Abstract Rare earth elements (REEs), with their unique magnetic, optical, and electrical properties, have become indispensable strategic resources. Widely applied in critical fields such as aviation, telecommunications, electronics, energy, transportation, and medicine, REEs play a vital role in advancing technology and driving social and economic development. However, the REE industry faces numerous challenges, including unbalanced resource distribution, supply and demand imbalances, international competition, technological limitations, and associated environmental pollution. This paper, incorporating both the historical evolution and current state of the REE industry, provides a comprehensive examination of the chemistry, applications, resources, technologies, challenges, and prospects of REEs. Specifically, it analyzes China’s REE industry, which holds the largest global reserves and production capacity. As a key feature, this paper introduces the Tai Chi model for sustainable development in the REE industry, offering an in‐depth analysis of two primary approaches—mining and recycling; the four critical participants—governments, enterprises, researchers, and consumers; and the eight essential influencing factors—resources, energy, environment, policy, applications, technology, supply and demand, and economy. The Tai Chi model not only clarifies the responsibilities and significance of each individual but also highlights their interconnectedness, providing a compelling framework for envisioning the sustainable development of the REE industry. Moreover, the paper identifies the major challenges currently facing the industry and offers insights into the future development of REEs. As such, this work contributes to a deeper understanding of the multifaceted REE landscape and underscores the importance of sustainable practices to ensure REEs’ lasting positive impact on the global industry.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".