Optimization of Nickel(II) adsorption by sodium tripolyphosphate crosslinked chitosan using response surface methodology (RSM)
Bibliographic record
Abstract
This study aimed to synthesize chitosan crosslinked by sodium tripolyphosphate with different concentrations and optimize it for Ni(II) adsorption in a batch adsorption system. The BET analysis and the solubility/swelling test showed that as the crosslinking degree increased, the surface area and total pore volume decreased while the physicochemical properties improved. The effect of the initial pH and temperature on the adsorption of Ni(II) was studied in a batch adsorption system, and thermodynamic parameters were calculated. In order to enhance the adsorption capacity of crosslinked chitosan, Response Surface Methodology (RSM) was employed to establish a correlation between two independent experimental factors, namely pH and crosslinking degree, and the Ni(II) adsorption capacity. By manipulating these factors, the aim was to optimize the adsorption capacity of crosslinked chitosan. The constructed model was well-aligned with the experimental data with an R2 = 96.05%. According to the model, an adsorption capacity of 31.94 mg/g could be achieved at a pH of 7.21 and a crosslinking degree of 2.93% (w/v). Among isotherm models investigated, the Langmuir model was found to be more appropriate for studying Ni(II) adsorption with maximum adsorption capacity in the order of chitosan beads > crosslinked chitosan beads. The mechanism of adsorption and crosslinking was also investigated using FT-IR and SEM-EDS techniques, implying that the amine groups were the primary sites for metal ions uptake as well as crosslinking reaction. A mixture of NaCl and H2SO4 could release the sorbed Ni(II) ions from STPP-CB5% with a recovery of 83% after one sorption-desorption cycle.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".