Synthesis of a green ALG@KLN adsorbent for high-efficient recovery of rare earth elements from aqueous solution
Bibliographic record
Abstract
The increasing consumption of rare earth elements (REEs) and disposal of REE-containing wastes pose a threat to resources and environment. Hence, it is important to achieve efficient REEs recovery while use eco-friendly methods. Herein, natural kaolinite is loaded on alginate hydrogel to prepare ALG@KLN composites via a facile one-step cross-link reaction without any hazardous chemicals. The characterizations of ALG@KLN, adsorption isotherms, adsorption kinetics, and desorption were investigated by SEM-EDS, XPS, FT-IR, and Zeta-potential. The maximum adsorption capacity of Gd (Ⅲ), Y (Ⅲ), Ho (Ⅲ), and Nd (Ⅲ) reached 74.40, 84.48, 85.18, and 75.17 mg/g, respectively. The adsorption kinetics and isotherm were well fitted to pseudo-second-order model and Langmuir model, respectively. The ∼2 mm diameter ALG@KLN can be easily collected and reused. The presence of Na + , Mg 2+ , and Al 3+ has a minor effect on the adsorption of REEs. The adsorption mechanism is mainly via the chemical chelation of oxygen-containing functional groups and electrostatic interaction on negatively charged surface of ALG@KLN. In addition, the cost of ALG@KLN is as low as ∼0.041 USD/gram. This work greatly contributes to the development of economical and eco-friendly REE adsorbents, which plays an important role in sustainable development.
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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 | 0.000 |
| 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) | 0.001 | 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 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".