Removal of Acid Fuchsin Dyem from Industrial Effluents Using Green Synthesized Copper Oxide Nanoparticles and their Characterization
Bibliographic record
Abstract
Nanoparticles are the spearheads of the rapidly expanding field of nanotechnology. Development of the green synthesis has gained extensive attention as a reliable, sustainable and eco-friendly protocol for synthesizing a wide range of metal and metal oxide nanoparticles. The synthesized copper oxide nanoparticles were characterized by ultraviolet visible spectroscopy (UV-Vis), X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FT-IR), Scanning Electron Microscope (SEM), Transmission Electron Microscope (TEM), Energy Dispersive X-ray (EDX). Adsorption parameters such as Initial dye concentration, Adsorbent dosage, pH, contact time, and temperature have also beenstudied. Adsorption isotherms namely Langmuir, Freundlich, Temkin are used to test the adsorption data; Kinetic studies such as pseudo first order, pseudo second order and thermodynamic parameters were also evaluated. To synthesis copper oxide nanoparticles, a green chemical strategy is employed in the current work. It is an easy, affordable, and effective alternative method. The green copper oxide nanoparticles that were made may be a good choice for removing dye from coloured aqueous solution due to their strong dye adsorption ability. CuO nanoparticle prepared from above mentioned routes is expected to have more extensive applications such as chemical sensor, catalytic, gas sensor, semiconductor etc. This method is the most viable in terms of energy, time, and simplicity. This procedure resulted in the production of copper Oxide nanoparticles on a huge scale.
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".