Synthesis and Characterization of Multiwalled Carbon Nanotubes Decorated by ZnO and Ag2O for Using to Remove Methyl Green and Erythrosin B Dyes from Their Aqueous Solutions
Bibliographic record
Abstract
A new nanocomposite for multiwalled carbon nanotubes with zinc oxide and silver oxide was prepared by utilizing hydrothermal method with methanol as solvent.Zinc oxide (ZnO) and silver oxide (Ag2O) were synthesized using co-precipitation method under basic medium.They were identified by several techniques such as UV-Vis, X-ray powder diffraction (XRD), scanning electron microscopy (SEM) and EDX analysis.The crystal size of the prepared nano compounds was revealed 17.6, 26.8, 20.3 and 21.5nm for MWCNT (after their functionalized by utilizing acids mixture from sulfuric and nitric acid by ratio 3:1(v/v)), ZnO, Ag2O and MWCNTs/ZnO & Ag2O nanocomposite respectively.The batch adsorption was used for removing two different class as cationic and anionic dyes from its aqueous solutions under various conditions such as pH level, temperature, contact time and agitation speed, the data exhibited a high value to remove dyes methyl green (MG) and erythrosin B (EB) onto the surface nanocomposite were 96.4 and 99.71% respectively.Adsorption equilibrium isotherm appeared the Langmuir model is more fitted than Freundlich to remove erythrosin B dyes with adsorption capacity 184.9 mg/g, while the adsorbed of methyl green dye more fitted with Freundlich isotherm and adsorption capacity is 836.9 mg/g.Thermodynamic parameters (∆G ° , ∆H ° and ∆S ° ) have been computed and revealed the negative values for the free energy.
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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".