Adsorption of Rare Earths(Ⅲ) Using an Efficient Sodium Alginate Hydrogel Cross-Linked with Poly-γ-Glutamate
Bibliographic record
Abstract
With the exploitation of rare earth ore, more and more REEs came into groundwater. This was a waste of resources and could be harmful to the organisms. This study aimed to find an efficient adsorption material to mitigate the above issue. Through doping sodium alginate (SA) with poly-γ-glutamate (PGA), an immobilized gel particle material was produced. The composite exhibited excellent capacity for adsorbing rare earth elements (REEs). The amount of La3+ adsorbed on the SA-PGA gel particles reached approximately 163.93 mg/g compared to the 81.97 mg/g adsorbed on SA alone. The factors that potentially affected the adsorption efficiency of the SA-PGA composite, including the initial concentration of REEs, the adsorbent dosage, and the pH of the solution, were investigated. 15 types of REEs in single and mixed aqueous solutions were used to explore the selective adsorption of REEs on gel particles. Scanning electron microscopy (SEM) and Fourier transform infrared (FT-IR) spectroscopy analyses of the SA and SA-PGA gel beads suggested that the carboxyl groups in the composite might play a key role in the adsorption process and the morphology of SA-PGA changed from the compact structure of SA to a porous structure after doping PGA. The kinetics and thermodynamics of the adsorption of REEs were well fit with the pseudo-second-order equation and the Langmuir adsorption isotherm model, respectively. It appears that SA-PGA is useful for recycling REEs from wastewater.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".