Synthesizing magnetic graphene oxide nanomaterial ( <scp> GO‐Fe <sub>3</sub> O <sub>4</sub> </scp> ) and kinetic modelling of methylene blue adsorption from water
Bibliographic record
Abstract
Abstract Methylene blue (MB) is a frequently used dye in several industries that is detrimental both to human health and aquatic ecosystems. Graphene oxide (GO) has the application potential for dye removal from wastewater due to its high specific surface area; however, it suffers from a labourious separation step. In this work, magnetic GO nanostructure was prepared by co‐precipitation of ferrous salts in presence of GO to achieve a GO‐Fe 3 O 4 hybrid. By choosing the proper ratio of GO to Fe 3 O 4 , the specific surface area of 280.26 m 2 /g could provide excellent adsorption capacity; the superparamagnetic property of the prepared adsorbent also ensures its magnetic separation from aqueous solutions. The results of adsorption experiments revealed that, within 2 min, 93%, 80%, and 50% of MB were separated from the solutions of 100, 120, 150 mg/L dye concentration, respectively. The adsorption of MB followed a pseudo‐first‐order kinetic model, and the equilibrium isotherm study indicated that experimental data were well fitted to the Langmuir isotherm model. The impact of pH and adsorbent dosage were investigated, and the results showed that the amount of adsorbed MB onto the GO‐Fe 3 O 4 was sharply increased to 231.6 and 246 mg/g by increasing pH and adsorbent dosage up to 6 and 15 g, respectively. Thermodynamic parameters indicated that the adsorption process of MB is endothermic and spontaneous. In addition, GO‐Fe 3 O 4 revealed a good regeneration ability by using ethanol as the desorbent. These results suggest that the proposed adsorbent has promising potential in removing dye from water resources.
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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".