Removal of nickel from aqueous solutions by citric acid modified <i>Ceiba pentandra</i> hulls: Equilibrium and kinetic studies
Bibliographic record
Abstract
Abstract The removal of Ni(II) from aqueous solutions using biomass prepared from Ceiba pentandra hulls powder modified with citric acid treatment (CAMCPH) has been studied by batch method. The biosorbent was characterised before and after citric acid modification using SEM, FT‐IR and XRD. Experimental parameters that influence the biosorption of Ni(II), such as pH, biosorbent dose, contact time and initial concentration of metal ion have been investigated. The adsorption of Ni(II) increased with increase in contact time and reached equilibrium within 50 min. The maximum removal of Ni(II) was observed at pH 5.0. The kinetic data were analysed using three adsorption kinetic models: the pseudo‐first, second‐order kinetics and intra‐particle diffusion. The results showed that the pseudo‐second‐order model fits the experimental data very well. The equilibrium data were analysed using Langmuir, Freundlich and Dubinin–Radushkevich isotherm models. Langmuir model provided the best correlation for the adsorption of Ni(II) by CAMCPH and the monolayer biosorption capacity for Ni(II) removal was 34.34 mg/g. Desorption experiments were carried out using HCl solution and the recovery of the metal ion from CAMCPH was found 98%. Desorption experiments showed the feasibility of regeneration of the biosorbent for further use after treating with dilute HCl. © 2011 Canadian Society for Chemical Engineering
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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) | 0.001 | 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) | 0.000 | 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 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".