Green Synthesis and Characterization of Silver Nanoparticles / CM-Chitosan-Isopropanol by Gamma Irradiations Method
Bibliographic record
Abstract
The present study demonstrates a green process for the synthesis of spherical-shaped and stabilized silver nanoparticles Ag NPs using gamma irradiated conditions. This method has certain advantages over conventional methods since it can manage the particle size and structure and generates totally reduced and extremely pure nanoparticles free from by-products or chemical reducing agents. Silver nanoparticles composite (Ag NPs / CM) is prepared from an aqueous solution of silver nitrate, chitosan and isopropanol, at room temperature using gamma irradiation doses to induce reduction and cross-linking to formation in situ Ag NPs/ CM-chitosan-isopropanol solutions. The production and homogenous distribution of silver nanoparticles in the hydrogel matrix were characterized using transmission electron microscopy, XRD, Zeta potential and UV-Vis spectrophotometer analysis. The silver/ CM-chitosan-isopropanol matrixes possessed different gamma doses using a Co- 60 gamma source. Through the comprehensive results of IR- Ag NPs /(CM- chitosan isopropanol solutions due to antibacterial activity test, the results show the prepared Ag NPs /CM-chitosan-isopropanol could be used as an antibacterial agent. The obtained Ag nanoparticles were stable for over 3 months at room temperature. the analysis outcomes indicate that the colloidal Solution (silver nanoparticles / (CM chitosan - isopropanol)) has a promising ability to be used in antibacterial applications in the future.
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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".