Facile fabrication of nano zerovalent iron – Reduced graphene oxide composites for nitrate reduction in water
Why this work is in the frame
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Bibliographic record
Abstract
Nano zerovalent iron is used to destruct a wide range of organic and inorganic contaminants in water. However, its performance is limited due to rapid aggregation and surface passivation. To minimise aggregation, we fabricated nano zerovalent iron on the reduced graphene oxide sheets using green tea derived polyphenols (hereafter rGO-nZVI-P) or borohydride ions (hereafter rGO-nZVI-B). Both rGO-nZVI-P and rGO-nZVI-B composites were characterised by electron microscopic, molecular spectroscopic and electrochemical methods. The spherical nZVI particulates (e.g. ~4–15 mm diameter) are well dispersed among rGO sheets. Polyphenols act as a capping agent for Fe (0) to prevent its aggregation. The X-ray diffraction and X-ray photon spectroscopic results show an admixture of Fe (0) with rGO and Fe oxides (e.g. FeOOH, Fe2O3, and Fe3O4 phases). The association of Fe (0) on the reduced graphene oxide matrix is believed to occur via π–π framework thus minimising surface passivation. The reduction efficiency of the nano zerovalent iron composites was determined using nitrate as index ion. When compared with rGO-nZVI-B, the rGO-nZVI-P reduces 70% of 0.8064 mM nitrate within an hour. Although traces of NO and NO2− are observed, ammonia is the dominant product that accounts for 95% nitrogen mass balance. The nitrate reduction by the rGO-nZVI composites follows pseudo-second-order kinetics. Fe (0) or its oxidation products are environmentally benign. The rGO-nZVI-P also has the potential to destruct excess nitrate in water remediation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it