An Investigation of Lanthanum Recovery from an Aqueous Solution by Adsorption (Ion Exchange)
Bibliographic record
Abstract
Lanthanum (La(III)) is one of the high-demand rare earth elements with applications in various products. However, La(III) in mining waste streams and electronic waste also poses environmental and health concerns. Therefore, the recovery of La(III) in the waste is needed. In the present study, the adsorption of La(III) with Dowex 50W-X8, Amberchrom50WX4, Amberlyst 15, and Amberchrom 50WX2 was evaluated using a shaker water bath. Dowex 50W-X8 was found to be the best adsorbent and was used to investigate the effect of the shaker speed (RPM = 50–150), adsorbent dosage (1.0–4.0 g), pH (2.0–7.0), and temperature (20–40 °C) on adsorption. La(III) adsorption was found to increase with the shaker speed, as expected. On the other hand, the adsorption capacity decreased with the adsorbent amount. Also, the highest La(III) adsorption was observed at pH = 6.0. La(III) percentage removal did not vary significantly with a temperature from 20 °C to 40 °C. However, the first-order kinetic rate constant decreased moderately with increases in temperature. The adsorption of La(III) by Dowex 50-X8 followed the Freundlich isotherm model better than the Langmuir model. In addition, the adsorption kinetics were represented well by the pseudo-first-order kinetic model. Moreover, enthalpy and Gibbs free energy changes were found to be negative, indicating an exothermic and thermodynamically favorable adsorption process.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".