Kinetics and thermodynamics of diquat removal from water using magnetic graphene oxide nanocomposite
Bibliographic record
Abstract
A graphene oxide nanocomposite (GO‐Fe3O4) was synthesized with a simple and low‐cost method. This nanocomposite was characterized by XRD, TEM, FT‐IR, TGA, and VSM. Spherical Fe3O4 nanoparticles with an average size of 10 nm were uniformly applied to the surface of graphene oxide sheets. GO‐Fe3O4 nanocomposite showed a superparamagnetic characteristic at room temperature and its saturation magnetization was 8.5 A · M2/kg. The adsorption behaviour of diquat at the surface of GO‐Fe3O4 was investigated, including effects of pH, temperature, and water matrix. The adsorption kinetics, thermodynamics, and adsorption isotherm were also examined. The adsorption was strongly dependent on pH. The adsorption process obeyed the pseudo‐second order kinetic model, and the rate‐determining step might be chemical sorption. The Langmuir adsorption isotherm model was applicable for describing the adsorption of diquat onto GO‐Fe3O4, and the adsorption capacity was 74.85 mg/g at room temperature. Thermodynamic parameters indicated that the adsorption process was spontaneous and exothermic. Most importantly, the GO‐Fe3O4 could remove 96.6 % of diquat from a real water sample when the concentration of diquat is 20 mg/L.
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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) | 0.001 | 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".