Influence of Physicochemical Parameters pH, Concentration, Temperature and Reaction Time on the Green Synthesis of Metallic Nanoparticles Using Plant Extracts: A Review
DOI:
https://doi.org/10.62643/Keywords:
green synthesis; plant extract; nanoparticles; pH; precursor concentration; reaction temperature; reaction time; phytochemicalsAbstract
Utilizing plant extracts for the green (biological) synthesis of metallic and metal-oxide nanoparticles has become a safe, cost-effective, and environmentally friendly alternative to traditional physical and chemical methods. The phytochemicals found in plant extracts, including flavonoids, polyphenols, terpenoids, tannins, and proteins, serve dual roles as reducing and capping agents. They facilitate the transformation of metal ions into zero-valent or oxide nanoparticles while also managing their growth and stability. Nonetheless, the size, shape, yield, crystallinity, and biological activity of these nanoparticles are highly influenced by the physicochemical conditions during the synthesis process. This review integrates insights from the literature on plant-mediated nanoparticles to thoroughly assess the influence of factors such as reaction pH, metal-precursor concentration, plant-extract concentration and the ratio of extract to precursor, reaction temperature, incubation or reaction duration, and illumination on nanoparticle synthesis. By comparing evidence from systems involving silver, gold, copper, and zinc oxide nanoparticles, common patterns are identified: a pH that is near-neutral to moderately alkaline and moderate precursor concentrations typically lead to smaller, more uniform, and stable particles. However, higher temperatures can speed up bioreduction kinetics but may also degrade thermolabile phytochemicals. The review explores the mechanistic reasons behind these patterns, current characterization methods, notable biomedical and environmental applications, and the challenges that remain for reproducibility and scaling up. It concludes that systematically optimizing these interacting parameters, with the aid of statistical design-of-experiments methods, is crucial for achieving reproducible, adjustable, and industrially applicable green nanoparticle synthesis.
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