Development of a sustainable ternary magnetic nanocomposite GCNI for efficient and synergistic photodegradation of Rhodamine B under solar irradiation: kinetic and mechanistic studies
Bibliographic record
Abstract
In this study, a novel sunlight-active ternary magnetic nano-photocatalyst GCNI (Nanozero valent Iron @ Graphene oxide and Chitosan support) was fabricated. This easily retrievable magnetic nanocomposite was prepared by depositing NZVI (Nano Zero Valent Iron, Fe0) over binary GO-CS in seven different compositions for its best optimisation in the solar-light-promoted degradation of RhB. NC (Nano Composite) was characterised by SEM demonstrating their surface morphology. Further characterisation and properties of NC were also studied using XRD, FT-IR, VSM and BET and EDS techniques. The efficacy of the NC was investigated for the removal of RhB in comparison to binary GO-CS and bare NZVI. GCNI 1:1:3 was found to be the best suitable photocatalyst showing synergistic effect in the photodegradation of RhB under given conditions of pH, concentration and time. The mechanism of dye removal and degradation was further discovered by UV-VIS and mass spectroscopy techniques. FT-IR and XRD results of fresh and treated GCNI successively corroborated the mechanism. The sorption kinetics of RhB on to GCNI was found to be described by the pseudo-second-order kinetic equation. The parameters such as dye concentration, pH and the reaction time are varied to understand the effective removal of RhB in wastewater. The outcomes exhibited the highly efficient; up to 99.4% of dye removal, easy magnetic separation of photocatalyst, excellent reusability up-to 87.5% till six consecutive cycles, synergistic effect in adsorption and photo-degradation of dye.
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".