Response surface methodological optimization of batch Cu(II) sorption onto succinic acid functionalized SiO<sub>2</sub> nanoparticles
Bibliographic record
Abstract
Functionalizing nanosilica (n-SiO2) particles with suitable active organic moiety leads to the formation of surfaces with precisely controlled physical and chemical characteristics. In this work, a novel nanosorbent (31 ± 2.4 nm), namely succinic acid functionalized nanosilica (n-SiO2@SA), was synthesized via a simple protocol using microwave irradiation to remove Cu(II) ions from aqueous media. The successful functionalization of n-SiO2 was confirmed by FTIR, and the thermal stability of n-SiO2@SA was investigated by TGA study. Other techniques, including HRTEM, DLS and zeta-potential, were utilized to investigate the chemical, surface, and morphological properties of the fabricated n-SiO2@SA. The response surface methodology (RSM) combined with three-level, three-factorial Box–Behnken design (BBD) was applied to optimize the multivariable sorption system using data obtained from 17 batch runs to reach 98.9% of Cu(II) ion removal. The predicted optimal conditions were as follows: contact time = 30 min, pH = 7.1, initial Cu(II) concentration = 317.5 mg L−1, and sorbent dose = 15 mg at which the maximum sorption capacities for n-SiO2 and n-SiO2@SA were 209.3 and 386.4 mg g−1, respectively, at 25 °C, thus supporting the validity of functionalization process. Non-linear regression and linear least-squares methods confirm the suitability of Langmuir model to describe the experimental endothermic, feasible, and chemisorption data, whereas the normalized standard deviation Δq% recommends the pseudo second-order kinetic model to represent the kinetic data. Real Cu-contaminated wastewaters were used to examine n-SiO2@SA nanosorbent for removing Cu(II) ions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".