Green Synthesis of Bimetallic ZnO-CuO Nanocatalyst for theHydro-dechlorination of 1,2-Dichlorobenzene and 3-Chlorophenol
Bibliographic record
Abstract
Bimetallic nanoparticles exhibit advantageous catalytic, optical and electrical properties and among the various methods of synthesis of nanoparticles, green synthesis gained great attention due to its simplicity, cost effective, ecofriendly and versatility. In view of this, the present work intended to synthesize the ZnO-CuO bimetallic nanoparticles using aqueous root extract of Suaeda maritima (L.) Dumort. The bimetallic nanoparticles synthesized were observed to be spherical in shape with particle size in the range of 20-45 nm and the particles were distributed with less aggregation. The synthesized nanoparticles have 41% of zinc element and 33% copper with hexagonal phase crystalline structure of zinc oxide and monoclinic copper oxide phase. The synthesized ZnO-CuO bimetallic nanoparticles were used for the hydro-dechlorination of 1,2-dichlorobenzene and 3-chlorophenol. The results indicated that rapid dechlorination was observed during the initial time of study. The percentage dechlorination of 19.37 ± 0.243 and 15.52 ± 0.193% was observed within 5 min of the study for 1,2-dichlorobenzene and 3-chlorophenol, respectively. The high % dechlorination was achieved within 25 min wherein the % dechlorination was observed to be 93.35 ± 0.103 and 89.75 ± 0.091 for 1,2-dichlorobenzene and 3- chlorophenol, respectively. This study reports the cost-effective and eco-friendly method for synthesizing ZnO-CuO bimetallic nanoparticles that can utilize for the dechlorination of various polychlorinated aromatic compounds in natural samples.
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How this classification was reachedexpand
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 | 0.001 | 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 teacher head, 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".