A Comprehensive Review of Adsorbents for Rare Earth Separation: Design, Synthesis, Adsorption Performance, and Mechanisms
Bibliographic record
Abstract
ABSTRACT Rare earth elements (REEs) play an irreplaceable role in modern technology and industry. However, due to the highly similar physicochemical properties among REEs, their separation remains a significant challenge. Additionally, REEs often exist in low‐concentration solutions, making efficient REE recovery an urgent task. This paper presents a comprehensive review of the latest research advances in adsorbents for REE adsorption from aqueous solutions. It systematically examines the performance characteristics of organic, inorganic, biological, and composite adsorbents, with a focus on innovative design, synthesis strategies, and practical applications of various adsorbents, particularly highlighting their excellent adsorption performance and diverse mechanisms. Notably, composite and hybrid materials significantly enhance adsorption selectivity and stability through synergistic effects. Future research should focus on machine learning (ML)‐driven adsorbent intelligent design using quantitative structure–activity/property relationship (QSAR/QSPR) models, green synthesis pathways, adsorption–desorption performance enhancement, and industrial process optimization via interdisciplinary collaboration. This review aims to provide a systematic reference for research on adsorption and separation of REEs, thereby promoting the development and application of high‐efficiency and eco‐friendly adsorbents.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".