Catalytic Degradation and Antibacterial Activity of Cinnamon-Mediated Green Synthesized Silver Nanoparticles Loaded on Alginate Beads
Bibliographic record
Abstract
Alginate-silver nanocomposites in the form of spherical beads were prepared using a green approach by using the aqueous extract of cinnamon bark. The nanocomposites were fabricated by a three-step process involving gelation by ionotropic crosslinking, adsorption, and in situ chemical reduction in solution. The rich phytochemicals of the cinnamon bark extract played a dual role as reducing and stabilizing agent in the synthesis of silver nanoparticles of average size of 16 nm. The presence of silver nanoparticles in the nanocomposite was studied using UV-Vis absorption spectroscopy, electron microscopy and energy dispersive x-ray spectroscopy. The morphology of the nanocomposite beads was dense and compact with random distribution of silver nanoclusters. The catalytic property of the nanocomposite beads was evaluated for the degradation of Congo-red dye in the presence of sodium borohydride. The degradation followed pseudo-first order kinetics with a rate constant of 0.012 min-1 at 23 °C. The activation energy for the degradation process was 27.57 ± 1.5 kJ mol-1. The thermodynamic parameters such as the enthalpy and entropy changes were evaluated using the Eyring equation and were determined to be 0.123 ± 0.05 kJ mol-1 and -197.25 ± 2 J mol-1 K-1, respectively. The nanocomposite exhibited antibacterial properties against the two strains of bacteria, Escherichia coli and Staphylococcus aureus.
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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".