Scholarlink

<p>Natural products derived from plants exhibit substantial potential in the field of self-assembled nanoparticles, owing to their structural diversity, biocompatibility, and inherent bioactivity. However, the traditional “trial-and-error” research paradigm is inefficient, making it difficult to unravel complex assembly mechanisms and achieve precise regulation. Given the significant application potential and research value of computational technology and artificial intelligence (AI) in the field of natural product self-assembled nanotechnology, this review focuses on analyzing self-assembly mechanisms driven by computational techniques. Notably, this review presents the first analysis of the key differences in self-assembly driving forces and mechanisms between plant-derived natural product self-assembled nanoparticles (SA-PNP-NPs) and other self-assembly systems. This review then discusses the preparation strategies and the conditions required for the self-assembly of SA-PNP-NPs. Subsequently, the characterization techniques employed for these nanostructures are summarized. Finally, the practical application challenges of SA-PNP-NPs are analyzed. This work pioneers the introduction of a data-driven and model-guided paradigm into the fabrication of SA-PNP-NPs. This approach not only significantly accelerates scientific discovery and process development but, more importantly, enables the precise harnessing of natural principles. It facilitates the efficient conversion of structurally diverse and functionally rich plant-derived molecules into high-performance smart nanomaterials with broad applicability. The primary objective of this work is to provide theoretical guidance for the efficient and precise synthesis of self-assembled nanomaterials, thereby offering technical support for the development of advanced functional materials.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Green by design: self-assembly of plant natural products into nanoparticles – mechanisms, computational dissection, and comparison with synthetic systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月9日

查看全文
  • Yuxiao Huo,
  • Xingxing Shang,
  • Qingli Yang,
  • Jian Ju

摘要

Natural products derived from plants exhibit substantial potential in the field of self-assembled nanoparticles, owing to their structural diversity, biocompatibility, and inherent bioactivity. However, the traditional “trial-and-error” research paradigm is inefficient, making it difficult to unravel complex assembly mechanisms and achieve precise regulation. Given the significant application potential and research value of computational technology and artificial intelligence (AI) in the field of natural product self-assembled nanotechnology, this review focuses on analyzing self-assembly mechanisms driven by computational techniques. Notably, this review presents the first analysis of the key differences in self-assembly driving forces and mechanisms between plant-derived natural product self-assembled nanoparticles (SA-PNP-NPs) and other self-assembly systems. This review then discusses the preparation strategies and the conditions required for the self-assembly of SA-PNP-NPs. Subsequently, the characterization techniques employed for these nanostructures are summarized. Finally, the practical application challenges of SA-PNP-NPs are analyzed. This work pioneers the introduction of a data-driven and model-guided paradigm into the fabrication of SA-PNP-NPs. This approach not only significantly accelerates scientific discovery and process development but, more importantly, enables the precise harnessing of natural principles. It facilitates the efficient conversion of structurally diverse and functionally rich plant-derived molecules into high-performance smart nanomaterials with broad applicability. The primary objective of this work is to provide theoretical guidance for the efficient and precise synthesis of self-assembled nanomaterials, thereby offering technical support for the development of advanced functional materials.