TiO2-NPs/ZnO-NPs@Co3O4 nanocomposite from natural extracts for the Rhodamine 6 G photodegradation
Bibliographic record
Abstract
Socio-environmental issues are frequent in industrial growth, especially wastewater containing dyes resulting in toxic effects on fauna and flora. Parallelly, synthesizing nanomaterials using extracts allows for improved properties with fewer environmental effects. In this way, the present study aims to synthesize using natural extracts and characterize a ceramic nanocomposite containing titanium and zinc dioxide doped with tricobalt tetroxide nanoparticles (TiO 2 NPs/ZnO NPs@Co 3 O 4 NPs) for the removal of Rhodamine 6 G under visible radiation. TiO 2 -NPs/ZnO-NPs@Co 3 O 4 -NPs ceramic nanocomposite showed a heterogeneous morphology with a negative charge surface of -16.73 mV, V-type isotherm and H1 hysteresis, surface area (S BET ) of 62.2 m² g −1 , pore diameter (Dp) of 10.41 nm and bang gap energy (Eg) of 2.98 eV. The ideal condition of photocatalytic heterogeneous was of [Rh 6 G] = 3.18 mg L −1 , [NPs] = 1 g L −1 and pH = 7 with 99.97 % for the Rh 6 G photodegradation and the apparent rate of the pseudo first-order reaction ( k ) of 0.0193 min −1 . After IV cycles of TiO 2 -NPs/ZnO-NPs@Co 3 O 4 -NPs, there was a small reduction in Rh 6 G dye removal (99.97 to 85.71 %), confirming the photocatalytic stability of the nanocatalyst. Ecotoxicity tests were carried out with Lactuca sativa seeds and demonstrated root radicular inhibition of 6.41 % to 20.73% ranging from 12.5 - 100 mg L −1 . Machine Learning (ML) study was used to predict the Rh 6 G photodegradation mechanism, where the dye molecules successively cleavage such aromatic structure and carbonic acid to produce smaller fragments that are further form CO 2 and H 2 O. Therefore, ceramic nanocomposite presents potential application as a nanocatalyst for the wastewater treatment.
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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) | 0.001 | 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".