Garlic (Allium sativum) extract mediated synthesis of self-redox SnO2 nanomaterials for reduction of Cr(VI) under dark condition
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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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) | 0.001 | 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".