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/AgNO 3 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/AgNO 3 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".