Chemistry of plant extracts directs the silver nanostructures’ crystal structure into hexagonally close-packed: a comparative study using elecampane and blueberry extracts
Bibliographic record
Abstract
In this study, silver nanostructures (AgNSs) were obtained using aqueous extracts of elecampane ( Inula viscosa) and blueberry ( Vaccinium arctostaphylos L.). The synthesized AgNSs were characterized using UV–visible spectrophotometers, Fourier-transformed infrared spectroscopy, scanning electron microscopy-energy dispersive X-ray analysis, high-resolution transmission electron microscopy, and X-ray photoelectron spectroscopy. The optimization studies revealed that pH and the extract/AgNO3 ratio played significant roles in the morphology and size of the AgNSs, while temperature did not show any significant role. Both the blueberry and elecampane extracts resulted in a mixture of spherical and aspherical silver nanoparticles (AgNPs). In contrast, utilization of a relatively high extract/AgNO3 ratio accompanied by an extended incubation period (i.e., 10 days) caused the formation of larger spherical (e.g., up to micron-sized) and large 1D AgNPs for the blueberry and elecampane extracts, respectively. The major difference related to the crystal structures was obtained, as the blueberry extracts resulted in face-centric cubic crystals, and the elecampane extracts allowed the formation of hexagonally close-packed (hcp) crystalline structures. The AgNPs were tested for their antibacterial activities using gram (−) and gram (+) bacterial species, where the elecampane-synthesized AgNPs possessed higher antibacterial activity that can be attributed to the hcp crystalline structure of the AgNPs.
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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".