Adsorption of Rare Earth Elements (REEs) onto Activated Carbon Modified with Potassium Permanganate (KMnO4)
Bibliographic record
Abstract
The adsorption capacity of activated carbon modified with potassium permanganate (KMnO4) for rare earth elements (REEs) from aqueous solution was investigated. The modified activated carbon was characterized by SEM (scanning electron microscopy), FT-IR (Fourier transform infrared spectrometer), and N2 adsorption-desorption tests. Adsorption experiments from aqueous solutions containing known amounts of some REEs (i.e. La, Lu, Yb, Eu, Y, Sc) onto the carbon were explored in a batch system. The amount of REEs adsorbed at different pH values, initial concentrations, and contact times were determined by inductively coupled plasma-atomic emission spectrometry (ICP-AES) in order to determine the optimum conditions for REEs adsorption. The adsorption of REEs on the activated carbon was well fitted by the Langmuir isothermal adsorption equation. The rates of adsorption were found to conform to pseudo-second order kinetic. These results present the modified activated carbon as an efficient adsorbent for REEs, hence creating new avenues for the treatment of industrial waste waters including pollutants. It is very significant information from the viewpoint of environmental protection.
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 | 0.000 | 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 | 0.001 | 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".