Development of α- and γ-Fe<sub>2</sub>O<sub>3</sub>decorated graphene oxides for glyphosate removal from water
Bibliographic record
Abstract
In this study, the proposed adsorbent composed of graphene oxide (GO) functionalized by magnetic nanoparticles of iron oxide (α-γ-Fe2O3) was obtained by a simple ultrasonication process. This new material was used for the removal of glyphosate in water. The nanoparticulated iron oxide used was synthesized by means of a modified sol–gel method, which does not use organic solvents. The adsorbent material (GO-α-γ-Fe2O3) obtained was characterized by magnetic measurements, and it can be proved that it has superparamagnetic properties, allowing fast and efficient magnetic separation. The equilibrium time for the adsorption of glyphosate when using GO-α-γ-Fe2O3 was 2 hours and the maximum removal was 92% at 15°C, with a maximum adsorption capacity of 46.8 mg g−1. Langmuir model and pseudo-second-order kinetic model correlated satisfactorily to the experimental data. The thermodynamic parameters showed that the adsorption of glyphosate on GO-α-γ-Fe2O3 was spontaneous, exothermic and thermodynamically favorable at temperature of 15–45°C. Thus the adsorbent material GO-α-γ-Fe2O3 proposed in this study is considered a good candidate to be used in the removal of glyphosate from aqueous solutions, presenting high adsorption capacity, low cost and magnetic properties that facilitate the separation of the adsorbent material.
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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".