Exploring the Adsorption Efficiency of Sulfonated Graphene Oxide for Ciprofloxacin Removal from Aqueous Solution: Insights from Density Functional Theory, Kinetics, Thermodynamics, and Reusability
Bibliographic record
Abstract
The pollution of water resources with pharmaceutical substances poses a critical threat to global health and environmental stability. This study demonstrates a highly effective method for removing ciprofloxacin, a broad-spectrum antibiotic and pharmaceutical contaminant, from aqueous solutions using sulfonated graphene oxide (SGO) as an adsorbent. Comprehensive experiments, supported by density functional theory (DFT) analysis, confirm the strong interaction between ciprofloxacin and SGO through microscopy, spectroscopy, and theoretical techniques. The study rigorously evaluates the impacts of adsorption time, medium pH, adsorbent quantity, ciprofloxacin concentration, inorganic cations, and temperature on adsorption performance, providing compelling evidence of SGO's superior performance in mitigating this pressing environmental issue. Results reveal that the adsorption process follows a pseudo-second-order kinetic model and aligns with the Langmuir isotherm, underscoring SGO's high affinity for ciprofloxacin. SGO achieved a significant maximum uptake capacity of 1000.00 μmol/g within 240 min at a low adsorbent dose of 0.2 g/L and optimal pH of 4.0. Thermodynamic assessments indicate that ciprofloxacin adsorption on SGO is both spontaneous and endothermic. Additionally, ciprofloxacin release from antibiotic-loaded SGO was notably high (99.14%) in a solution of 1 M HCl in DMF, and SGO retained over 95.78% of its initial adsorption capacity after five adsorption-desorption cycles, demonstrating its robustness and reusability. These findings strongly position SGO among graphene oxide and their derivatives as a promising and sustainable adsorbent for removing pharmaceutical contaminants, particularly ciprofloxacin, from aqueous solutions. This offers significant potential for advancing water purification technologies in healthcare and environmental applications.
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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".