Chitosan-Coated Silver Nanocomposites: Biosynthesis, Mechanical Properties, and Ag+ Release in Liquid and Biofilm Forms
Bibliographic record
Abstract
This study explores the biosynthesis, characterization, and evaluation of silver nanoparticles coated with chitosan (AgChNPs) for liquid nanocomposite and film formation in integrated pest management (IPM). AgChNPs were synthesized using Galega officinalis leaf extract as a reducing agent, with varying chitosan concentrations (0.5%, 1%, and 2%) and pH levels (3, 4, and 5). Synthesis was optimized based on nanoparticle size, stability, and polydispersity index (PDI) over 21 days. Films incorporating AgChNPs were analyzed for chemical, physical, mechanical, and thermal properties via Ultraviolet-visible spectroscopy (UV-vis), Dynamic Light Scattering (DLS), Zeta Potential Analysis, Fourier Transform Infrared Spectroscopy (FTIR), X-Ray Diffraction (XRD), Transmission Electron Microscopy with Energy Dispersive X-ray Spectroscopy (TEM-EDX), and Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) to quantify silver ionization. TEM confirmed spherical nanoparticles (5.54–61.46 nm), and FTIR validated G. officinalis functionalization on chitosan. AgChNPs with 1% chitosan at pH 4 exhibited optimal properties: a size of 207.88 nm, a zeta potential of +42.30 mV, and a PDI of 0.62. Films displayed tunable mechanical strength, with a tensile strength of 3.48 MPa using 5% glycerol and 2% chitosan and an elongation at break of 24.99 mm. TGA showed a two-step degradation process (98.19% mass loss). Ag ionization was 62.57 mg/L in the liquid nanocomposite and 184.07 mg/kg in the films. These findings highlight AgChNPs’ potential for controlled-release properties and enhanced mechanical performance, supporting sustainable agricultural applications.
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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".