A Review on Phyto-Mediated Gold Nanoparticles for Efficient Dye Degradation
Bibliographic record
Abstract
The production of metal nanoparticles using plant extracts is one of the simplest, most efficient, affordable, and environmentally friendly solutions to prevent the use of harmful chemicals. Of all the nanoparticles, Gold (Au) nanoparticles (NPs) have garnered the most interest due to their distinct catalytic properties for degradation of dyes. Concerns concerning the synthesis of these materials, such as the use of hazardous solvents and precursor chemicals, microbial contamination, and the formation of undesirable byproducts, led to the development of the alternative method known as "green synthesis." As a result, several rapid and environmentally friendly ways to make Au NPs utilising aqueous extracts of various plant components, including bark, roots, leaves, and so on, have been described recently. Here, we review recent developments in the ecologically friendly production of Au NPs, their use as antifungal agents, and their mechanism of action. This paper sheds light on the ecologically friendly production of Au NPs, their use in dye removal with their mechanism of action, and their potential in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".