Effect of dispersed hydrophilic silicon dioxide nanoparticles on batch adsorption of benzoic acid from aqueous solution using modified natural vermiculite: An equilibrium study
Bibliographic record
Abstract
The equilibrium adsorption of benzoic acid from an aqueous medium on a natural vermiculite-based adsorbent was studied in the presence and absence of hydrophilic silicon dioxide nanoparticles in batchwise mode. The adsorbent was prepared through grinding natural vermiculite in a laboratory vibratory disk mill and the surfactant modification of ground vermiculite by cetyltrimethylammonium bromide, subsequently. The equilibrium isotherm in the presence and absence of nanoparticles was experimentally obtained and the equilibrium data were fitted to the Langmuir, Freundlich, Dubinin–Radushkevich and Temkin models. The results indicated that the dispersion of silicon dioxide nanoparticles at optimum concentration in the liquid phase remarkably increases the removal efficiency. Furthermore, it yields a more favorable equilibrium isotherm and changes the compatibility of equilibrium data from the Langmuir and Temkin equations to just the Langmuir equation. A quadratic polynomial model predicting the equilibrium adsorbent capacity in the presence of nanoparticles as a function of the adsorbate and initial nanoparticle concentrations was successfully developed using the response surface methodology based on the rotatable central composite design. A desirability function was used in order to optimize the values of all variables, independent and dependent ones, simultaneously.
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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) | 0.001 | 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".