Facile Synthesis of ZnSe/Co3O4 Heterostructure Nanocomposites for the Photocatalytic Degradation of Congo Red Dye
Bibliographic record
Abstract
In the present paper, simple hydrothermal and solid-state methods are reported for the synthesis of metal chalcogenide (ZnSe), metal oxide (Co3O4) and their nano-heterostructure (ZnSe/Co3O4 3:1, 1:1 and 1:3 ratios by weight), while their photocatalytic efficiencies are also investigated. The X-ray diffraction results corroborate the good crystallinity and purity of all synthesized products, i.e., ZnSe, Co3O4 and their nanocomposites. The scanning electron micro-images of ZnSe show a mixed morphology of nanoparticles (≈16 nm), including spherical and distorted cubes, while Co3O4 has a worm-like morphology (≈20 × 50 nm). The EDX results show that all the elements are present in accordance with their anticipated amounts in the products. The UV/visible absorption spectrum of ZnSe depicts a sharp absorption at around 480 nm, while Co3O4 demonstrates two prominent peaks, 510 nm and 684 nm. The prepared samples were employed for the photocatalytic degradation of Congo red dye and the nano-heterostructure (ZnSe/Co3O4 3:1) shows an exceptional photocatalytic degradation efficiency of 96%. This enhanced photocatalytic activity was due to the synergic effect of ZnSe and Co3O4 that reduced the electron/hole recombination and caused suitable bandgap alignment.
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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".