Investigation of nickel adsorption onto low Jordanian zeolite dose: efficiency and Langmuir – Freundlich behaviour
Bibliographic record
Abstract
In this work, nickel adsorption onto low Jordanian zeolite dose is being investigated. Natural zeolite doses were stirred continuously with nickel solutions in batch reactors at 180 RPM for 24 hours, where the temperature was set to 20°C. The pH was initially 4.5 and reached 5.2 at equilibrium. The removal efficiency of nickel reaches maximum value when the initial nickel concentration is around 1 ppm and then tends to decrease when the initial nickel concentration increases above 1 ppm. The optimal nickel removal reaches 65% when the initial nickel concentration is 1 ppm and the zeolite dose is 26 mg·dm–3. This study investigates the behaviour of nickel removal and modelling isotherms below and above this critical peak point. At this level of zeolite dose, the adsorption does not follow either Freundlich or Langmuir isotherms, but rather, it follows Freundlich for the data plot just below the peak point with the highest coefficient of determination (R2) equals (0.98) when the zeolite dose is (26 mg·dm–3), whereas it follows Langmuir for the data plot just above the peak point with the highest coefficient of determination (R2) equals (0.99) when the zeolite dose is (10 mg·dm–3). These findings clarify the theory behind each isotherm and can be used to find new information for efficient treatment techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".