Green synthesized AgNPs@MSNs reinforced polycaprolactone nanofibers for removal of heavy metals, bacteria, and methylene blue dye from industrial wastewater
Bibliographic record
Abstract
This study aims to find solutions to global water scarcity and to address the environmental pollution and serious adverse effects on human health and aquatic ecosystems caused by the inadequacy of conventional water treatment methods. In order to contribute to the solution of these problems, the potential of silver nanoparticles (AgNPs) and mesoporous silica nanoparticles (MSNs) doped nanofibers obtained by the green synthesis method using Betula pendula plant extract in industrial wastewater treatment was investigated. The characterization of the synthesized nanoparticles was carried out in detail using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), UV-Vis spectrophotometry, scanning electron microscopy (SEM), and antibiogram tests. The filtration performance of industrial wastewater simulated in a laboratory setting was comprehensively evaluated by atomic absorption spectroscopy (AAS) and UV-spectrophotometry analyses. The characterization of polycaprolactone (PCL) based composite nanofibers produced by electrospinning was carried out by scanning electron microscopy (SEM) and thermogravimetric analysis (TGA). The main findings of the research reveal the successful biosynthesis of silver nanoparticles (AgNPs) with an average diameter of 113 nm and mesoporous silica nanoparticles (MSNs) with an average diameter of 185 nm. Furthermore, AgNPs@MSNs reinforced composite nanofibers provided effective adsorption of heavy metals, organic dyes, and bacterial pollutants found in industrial wastewater. These results indicate that the developed nanofibers have high potential in reducing environmental pollution.
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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) | 0.000 | 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) | 0.001 | 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".