Biosorption of lead(II) from aqueous solution on glutaraldehyde cross‐linked chitosan beads
Bibliographic record
Abstract
Abstract The ability of glutaraldehyde cross‐linked chitosan beads (GCC beads) as synthetic adsorbent for adsorptive removal of Pb(II) ions from aqueous solutions is investigated in the present study. The biosorbent has been characterised by Brunner, Emmett, and Teller (BET) analysis, Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) techniques. Equilibrium and column flow adsorption characteristics of Pb(II) ions on the biosorbent were studied. The effects of experimental variable parameters such as pH, concentration of metal ion, amount of adsorbent, contact time, temperature and interfering ions on adsorption have been investigated. The equilibrium data are fitted to pseudo‐first order, pseudo‐second order, fractionary order, chemisorption, Weber–Morris and Boyd models. Based on R2 and error function values, it is observed that the kinetic data are better fitted to pseudo‐second‐order kinetic model and chemisorption model. The experimental data are analysed using Langmuir, Freundlich, and Sips adsorption isotherm models. The monolayer adsorption capacity of GCC beads as obtained from Langmuir isotherm at 35°C is found to be 204.0 mg/g. The thermodynamic constants of the adsorption process: ΔH0, ΔS0 and ΔG0 are evaluated. The results show that biosorption of Pb(II) ions on GCC beads are endothermic and spontaneous.
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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) | 0.000 | 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".