GREEN SYNTHESIS OF SUPERPARAMAGNETIC IRON OXIDE NANOPARTICLE FROM FICUS CARICA FRUIT EXTRACT, CHARACTERIZATION STUDIES AND ITS APPLICATION ON DYE DEGRADATION STUDIES
Bibliographic record
Abstract
ABSTRACTObjective: The synthesis of nanoparticles (NPs) has become a matter of great interest in recent times due to their various advantageous propertiesand applications in a variety of fields. Metal NPs are being increasingly used in many sectors, and there is growing interest in the biological andenvironmental safety of their production.Methods: In this study, iron oxide NPs (Fe3O4-NPs) were synthesized from fruits of Ficus carica using a rapid, single step and completely greenbiosynthetic method by reduction of ferrous sulfate solution with F. carica ethanolic extract. The prepared Fe3O4-NPs were investigated by X-raydiffraction, Fourier transform infrared spectroscopy, and ultraviolet-visible spectroscopy.Results: The report emphasizes the effect of superparamagnetic Fe3O4-NPs on the degradation rate of hazardous dyes acid blue.Conclusion: To conclude, Fe3O4-NPs were prepared from fruits of F. carica using a rapid, single step and completely green biosynthetic method byreduction of ferrous sulfate solution with F. carica ethanolic extract.Keywords: Ficus carica, Ethanolic extract, Reduction, Ferrous sulfate, Superparamagnetic iron oxide nano particles, Dye degradation.
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".