Synthesis of Gold Nanoparticles as a Chemometric Evaluation of Phytochemical-Mediated Green Protocols for Biomedical Applications

Gold nanoparticles (AuNPs) possess unique optical and physicochemical properties that make them valuable for biomedical applications. This review presents a comparative assessment of chemical and green synthesis methods for AuNPs, integrating literature analysis with Artificial Neural Network (ANN) modeling to evaluate synthesis efficiency, environmental impact, and biocompatibility quantitatively. Conventional chemical approaches, such as the Turkevich method, provide precise control over nanoparticle size and morphology (achieving ۹۲% efficiency in synthesis control) but utilize hazardous reagents and operate under energy-intensive conditions with significant environmental burden (۳۸% environmental performance). In contrast, green synthesis methods leverage natural reducing agents from biological sources, aligning with sustainability principles (۸۸% environmental performance) and producing inherently biocompatible nanoparticles (۸۴% biocompatibility score), though they face reproducibility challenges (۴۵% synthesis control). A shallow feed-forward ANN with one hidden layer containing five neurons was developed to model the nonlinear relationships between synthesis methods and their outcomes. The network demonstrated exceptional predictive accuracy with correlation coefficients exceeding ۰.۹۹ and prediction errors below ۱% across all parameters. Sensitivity analysis showed that chemical methods excel in synthesis control, while green methods demonstrate significant advantages in environmental impact and biocompatibility—achieving approximately ۸۵% of optimal biomedical suitability compared to ۶۰% for chemical methods. The results show that optimal solutions may lie in hybrid approaches combining chemical precision with bio-derived capping agents. This article shows future directions, including hybrid synthesis strategies and machine learning optimization for sustainable nanoparticle production.

کلیدواژه ها:

Artificial Neural Network ، Gold nanoparticles ، Green synthesis ، Chemical Synthesis ، biocompatibility ، Sustainability