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<p>This study investigates the effects of various silicon-based treatments, including sodium silicate, DA-6, Atonik, and green-synthesized silicon nanocomplexes (silicon-DA6 and silicon-Atonik), applied through foliar spray on the growth and physiological traits of <i>Zataria multiflora</i>, a valuable medicinal plant. Treatments were applied at three concentrations (1.5, 3.0, and 6.0 mM) under field conditions using a randomized complete block design. Among all treatments, silicon-DA6 and silicon-Atonik nanocomplexes demonstrated the highest improvements in plant performance, surpassing standalone treatments in enhancing growth, physiological adaptation, and phytochemical profiles. These nanocomplexes significantly increased root and shoot dry weight, chlorophyll content, total phenolic and flavonoid content, and concentrations of key secondary metabolites such as epicatechin, naringenin, rosmarinic acid, quercetin, and essential oils, while also enhancing silicon accumulation. Additionally, reductions in DPPH (IC<sub>50</sub>) and vanillin levels indicated improved antioxidant activity. The integration of artificial neural networks (ANN) modeling further validated these findings, with high predictive accuracy (<i>R</i><sup>2</sup> values ranging from 0.985 to 0.999) for key phytochemical and antioxidant parameters. This underscores the potential of ANN as a robust tool for predicting plant responses to nano-enabled fertilizers. Results highlight the synergistic effects of combining silicon nanoparticles with biostimulants like DA-6 and Atonik, offering sustainable approaches to enhance crop productivity and medicinal quality in <i>Z. multiflora</i>. These findings pave the way for advanced agricultural practices leveraging nanotechnology and machine learning for optimized plant growth and environmental resilience.</p>

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Modulation of Polyphenolic Content, Essential Oils, and Antioxidant Activity in Zataria multiflora by Silicon-Based Nanocomplexes

  • 期刊论文

  • 发表日期：

    2025年8月18日

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  • Sahar Mostafavi,
  • Vahid Tavallali,
  • Hossein Ali Asadi-Gharneh,
  • Vahid Rowshan

摘要

This study investigates the effects of various silicon-based treatments, including sodium silicate, DA-6, Atonik, and green-synthesized silicon nanocomplexes (silicon-DA6 and silicon-Atonik), applied through foliar spray on the growth and physiological traits of Zataria multiflora, a valuable medicinal plant. Treatments were applied at three concentrations (1.5, 3.0, and 6.0 mM) under field conditions using a randomized complete block design. Among all treatments, silicon-DA6 and silicon-Atonik nanocomplexes demonstrated the highest improvements in plant performance, surpassing standalone treatments in enhancing growth, physiological adaptation, and phytochemical profiles. These nanocomplexes significantly increased root and shoot dry weight, chlorophyll content, total phenolic and flavonoid content, and concentrations of key secondary metabolites such as epicatechin, naringenin, rosmarinic acid, quercetin, and essential oils, while also enhancing silicon accumulation. Additionally, reductions in DPPH (IC50) and vanillin levels indicated improved antioxidant activity. The integration of artificial neural networks (ANN) modeling further validated these findings, with high predictive accuracy (R2 values ranging from 0.985 to 0.999) for key phytochemical and antioxidant parameters. This underscores the potential of ANN as a robust tool for predicting plant responses to nano-enabled fertilizers. Results highlight the synergistic effects of combining silicon nanoparticles with biostimulants like DA-6 and Atonik, offering sustainable approaches to enhance crop productivity and medicinal quality in Z. multiflora. These findings pave the way for advanced agricultural practices leveraging nanotechnology and machine learning for optimized plant growth and environmental resilience.