Adsorption isotherms, kinetics and mechanism of Pb(II) ions removal from aqueous solution using chemically modified agricultural waste
Bibliographic record
Abstract
Abstract The removal of Pb(II) ions from aqueous solution by chemically surface modified Strychnos potatorum seeds (SMSP) was investigated. The surface modification of the adsorbent was confirmed by the FTIR and SEM analyses. The Freundlich adsorption isotherm model provides a better fit to the adsorption isotherm data. The maximum adsorption capacity of SMSP for Pb(II) ions was found to be 166.67 mg/g at optimum conditions of pH 5.0, contact time of 30 min, SMSP dosage of 2 g/L and temperature of 30°C. The adsorption kinetics for Pb(II) ions removal by the SMSP follows the pseudo‐second order kinetic model. Adsorption mechanism of Pb(II) ions onto the SMSP was explained with the intraparticle diffusion, Boyd kinetic and Shrinking core models (SCM). The effective diffusivity values were calculated from the Boyd kinetic model: 7.723 × 10−12, 8.464 × 10−12, 6.877 × 10−12, 8.358 × 10−12 and 6.983 × 10−12 m2/s for the initial Pb(II) ions concentration from 100 to 500 mg/L, respectively. The diffusivity values were estimated from the SCM: 8.901 × 10−9, 8.586 × 10−9, 8.359 × 10−9, 5.368 × 10−9 and 4.318 × 10−9 m2/s for the initial Pb(II) ions concentration from 100 to 500 mg/L, respectively. The results suggest that SMSP can be used as an effective low‐cost adsorbent for the Pb(II) ions removal.
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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".