Synthesis of a Novel Adsorbing Agent by Coupling Chitosan, β-Cyclodextrin, and Cerium Dioxide: Evaluation of Hexavalent Chromium Removal Efficacy from Aqueous Solutions
Bibliographic record
Abstract
The present study aimed at synthesizing a novel adsorbing agent by coupling chitosan, β-cyclodextrin, and cerium dioxide (Chit/β-CyD/Ce). Its efficiency towards the removal of hexavalent chromium from aqueous solutions was studied and compared to an adsorbent comprising of only chitosan and cerium dioxide. Batch water purification experiments in varying experimental conditions (initial adsorbent concentration 5–100 mg/L, adsorbate concentration 0.1–2 g/L, pH 2–11, and temperature 15–50 °C) were carried out to evaluate the effectiveness of both adsorbents. In all the experimental cases, the Chit/β-CyD/Ce adsorbent exhibited the higher efficacy. The optimum operating conditions were found to be at an initial adsorbent concentration of 2 g/L, pH = 3, and temperature of 50 °C, with the Chit/β-CyD/Ce adsorbent being able to fully remove Cr(VI) from solutions with up to 50 mg/L Cr(VI) at these conditions. The adsorption of hexavalent chromium onto both adsorbents occurs in a multilayer pattern of a heterogeneous surface following the Freundlich isotherm model. Furthermore, the adsorption process was exothermic and obeyed the pseudo-second-order kinetic model, thus indicating the occurrence of chemisorption. Finally, FTIR, XRD, and SEM analyses were performed to characterize the synthesized adsorbents and verify the adsorption process.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".