Investigation of nickel adsorption onto low Jordanian zeolite dose: efficiency and Langmuir – Freundlich behaviour
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it