Green synthesis of zero-valent iron nanoparticles from cape gooseberry (physalis peruviana l.) Biomass for oil spill remediation
Bibliographic record
Abstract
The study explores eco-friendly synthesis of zero valent iron nanoparticles (nZVI) using Cape gooseberry fruit, leaf or husk extracts as reducing agents for iron (III) chloride (0.5 M, 0.1 M or 0.01 M) precursor in a 2:1 ratio under sonication. The nZVI were characterized using ultraviolet-visible (UV–vis) spectroscopy, Fourier transform-infrared (FTIR) spectroscopy, X-ray diffraction (XRD), Scanning electron microscopy (SEM), Zetasizer analysis, and Brunauer-Emmett-Teller (BET) analysis. Batch remediation experiments for 1 ml, 5 ml, or 10 ml of diesel in 100 ml of deionized water were performed using 0.33 g of nZVI. The highest quantity of nZVI was obtained from 0.5 M FeCl 3 and fruit extract. FTIR and UV–vis spectroscopy confirmed that Cape gooseberry polyphenols reduced and stabilized the nZVI, while XRD indicated a crystalline alpha-iron core with iron oxide shells. SEM imaging revealed agglomeration in nZVI from fruit and leaf extracts, while husk extract nZVI showed uniform size and porosity. Zytasizer analysis showed nZVI from fruit extract had diameters under 100 nm, while leaf and husk extracts nZVI were slightly over 100 nm. Zeta potentials were -29.48 mV (fruit), -33.62 mV (leaf), and -33.5 mV (husk). BET analysis showed husk extract nZVI had the highest surface area. The synthesized nZVI achieved diesel remediation efficiencies of 94.3 % (fruit), 94.3 % (leaf), and 94.6 % (husk) demonstrating successful synthesis of nZVI for diesel contamination cleanup. In addition to being a waste material, husks are advantageous over the fruits and leaves as feedstock for nZVI synthesis due to their superior uniformity and surface area.
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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".