Terminalia catappa Fallen Leaves Stabilized Zirconia Nanoparticles for Enhanced Anticancer and Antibacterial Activities
Bibliographic record
Abstract
Zirconia nanoparticles (ZrO₂ NPs) are eco-friendly and biocompatible materials used for various biological applications. Herein, we reported Terminalia catappa (T. catappa) fallen leaves extract encapsulated ZrO₂ NPs without using a chemical reducing agent. The bandgap, absorption, functional groups, and crystalline structure were identified using a UV-Visible spectrophotometer (UV-Vis), a Fourier transform infrared spectrometer (FT-IR), and an X-ray diffractometer. The transmission electron microscope (TEM) images confirmed the formation of spherical particles with an average size of 6 nm. The absorption in the range of 299-307 nm confirmed the formation of ZrO₂ NPs. The stretching vibration bands at 467 and 621 cm⁻¹ confirmed the presence of the Zr-O-Zr bond in ZrO₂ NPs. According to the X-ray diffraction pattern (XRD), the average crystallite size of the ZrO₂ NPs was 4 nm with a cubic structure. The nanoparticles Zr0.01, Zr0.02, and Zr0.03 exhibited 26, 28, and 32 mm zones of inhibition against Lactobacillus acidophilus, Staphylococcus albus, and Streptococcus mutans at a maximum concentration of 25µg/ml. The ZrO₂ exhibited an IC₅₀ value of ZrO₂ to be 32.63 μg/mL against A549 cell lines. Therefore, biogenic T. catappa extract-encapsulated zirconia nanoparticles can be used for the development of potential antibacterial and anticancer agents.
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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) | 0.000 | 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 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".