Synthesis of nanostructured cupric oxide for visible light assisted degradation of organic wastewater pollutants
Bibliographic record
Abstract
When organic dye-containing wastewater from textile industries are sometimes released into the environment, the liquids tend to pollute the environment whilst their solid residue accrues on land after the evaporation of the water. Most of these synthetic compounds are known to be poisonous and carcinogenic to living organisms. For this study, a relatively simple, sustainable and cost-effective approach have been utilized to synthesize CuO nanoparticles using copper precursor salts: (CuSO4.5H2O) and (Cu(NO3)2.3H2O), as a remedy for dye pollution reduction in water. Due to their simplicity of synthesis, insignificant harmfulness and cost, copper (II) oxide (CuO) nanoparticles were used to breakdown three generally utilized dyes; Rhodamine B (RhB), Methylene Blue (MB)- [Methylthioninium chloride] and Methyl Orange (MeO). The as-prepared nanoparticles were characterized to determine the ordered arrangement of atoms, functional groups, weight loss, thermal properties, microstructure and surface characteristics. Most significantly, the predominant preferential crystal growth was along the {002}/{-111} plane for the sulphate-based precursor whiles for the nitrate based precursor, it was preferentially grown along the {111} direction. The mesoporous nanoparticles had average crystallite sizes of 12 nm and 15 nm; and BET surface areas of 42.9 m2/g, and 69.6 m2/g respectively. The as-prepared nanoparticles were assessed for their photocatalytic behaviour in response to visible light exposure for 100 minutes at 25-min’ intervals. The nitrate precursor-based CuO photocatalysts showed relatively higher photodegradation efficiency (MeO-94.3%; MB- 90.6%; RhB - 99.6%) as compared with the sulphate precursor-based CuO photocatalysts (MeO-85.2 %; MB- 87.9%; RhB- 98.8%). Synthesis of nanostructured cupric oxide for visible light assisted degradation of organic wastewater pollutantsAll authorsDavid Dodoo-Arhin , Etchu E. Mbu, Seteno K. Ntwampe, Edward N. Malenga, Elvis Fosso-Kankeu, Benjamin Agyei-Tuffour , Emmanuel Nyankson, Abu Yaya & Henry Agbe | https://doi.org/10.1080/23311916.2021.1920563Published online:11 May 2021Display full size
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".