Uptake of Zn, Pb, Cu and Fe Ions from Spent and Unspent Engine Oil Using Termite Soil
Bibliographic record
Abstract
Removal of Zn, Pb, Cu and Fe ions from unspent and spent engine oil was studied using Termite soil. Process parameters such as contact time and adsorbent dosage were varied. Values from contact time were used for predicting kinetics equation of their uptake. At optimum time of 40 minutes, percentage adsorption was of the order Fe>Zn>Cu>Pb for both spent and unspent engine oil. Kinetics equation such as Elovich, Intra-particle, Pseudo-first order and Pseudo-second order were tested. Results obtained shows that their sequestering pattern fit into the pseudo-second order kinetics. Initial reaction rates, h (mg/g.min) and α (mg. g-1min-1) for all metal ions obtained from Pseudo-second order and Elovich kinetic models followed the trends Zn>Fe>Cu>Pb and Zn>Fe>Pb>Cu respectively in spent engine oil while for unspent engine oil, the trend was Fe>Zn>Cu>Pb for h (mg/g.min) and Zn>Fe>Pb>Cu for α (mg. g-1min-1). Electrostatic attraction existing on the surface of the adsorbent assisted in the high initial reaction of Zn and Fe ions, implying good affinity of the ions for the adsorbent. Desorption constant ᵦ (g/mg) was of the trend Cu>Pb>Fe>Zn and Cu>Pb>Zn>Fe for spent and unspent engine oils respectively. Intra-particle diffusion constant kid (mgg-1min-1/2) followed a similar pattern, revealing strong binding between Zn and termite soil than any of the metal ion. This pilot research has been able to suggest a kinetic process for uptake of the studied ions from spent and unspent engine oil.
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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) | 0.000 | 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) | 0.001 | 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".