Garlic (Allium sativum) extract mediated synthesis of self-redox SnO2 nanomaterials for reduction of Cr(VI) under dark condition
Why this work is in the frame
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Bibliographic record
Abstract
Hexavalent chromium [Cr(VI)] is a highly toxic heavy metal mainly released from various industrial processes. Its high-water solubility allows to readily enter the human body and posing serious health risks. Therefore, its remediation through catalytic reduction is an essential and effective treatment strategy. In this study, a green technology approach was employed to synthesize SnO₂ catalyst nanomaterials , with varied properties, for the reduction of Cr(VI) under dark condition. Various ratios of the two tin precursors, SnCl₂·2H₂O and SnCl₄·5H₂O, were used to modulate the catalyst characteristics. An extract from fresh garlic ( Allium sativum ) served as an efficient nucleating and precipitating agent for the formation of SnO₂ nanoparticles . The electronic properties, morphologies, crystal phases, and chemical states of the resulting SnO 2 nanomaterials were characterized. The SnO 2 nanocatalyst synthesized from SnCl₂·2H₂O (Sn-2) demonstrated 100 % Cr(VI) reduction efficiency within 14 min with a rate constant 68 times higher than SnO 2 derived from SnCl 4 ·5H₂O (Sn-4), which itself showed only 5.6 % reduction activity. Remarkably, combining equal weight ratios of both precursors to produce SnO 2 catalyst enhanced the Cr(VI) reduction to 100 % within 10 min. The presence of point defects and self-redox interactions between Sn 2+ and Sn 4+ in SnO 2 played pivotal roles for the reduction of Cr(VI) under dark conditions. Taken together, the green synthesized SnO₂ nanomaterials could offer significant potential for environmental remediations and public health protection.
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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) | 0.001 | 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 it