Intelligent Systems for Inorganic Nanomaterial Synthesis DOI Creative Commons

Cunhao Han,

Xinghua Dong, Wang Zhang

et al.

Nanomaterials, Journal Year: 2025, Volume and Issue: 15(8), P. 631 - 631

Published: April 21, 2025

Inorganic nanomaterials are pivotal foundational materials driving traditional industries’ transformation and emerging sectors’ evolution. However, their industrial application is hindered by the limitations of conventional synthesis methods, including poor batch stability, scaling challenges, complex quality control requirements. This review systematically examines strategies for constructing automated systems to enhance production efficiency inorganic nanomaterials. Methodologies encompassing hardware architecture design, software algorithm optimization, artificial intelligence (AI)-enabled intelligent process analyzed. Case studies on quantum dots gold nanoparticles demonstrate enhanced closed-loop machine learning-enabled autonomous optimization parameters. The study highlights critical role automation, technologies, human–machine collaboration in elucidating mechanisms. Current challenges cross-scale mechanistic modeling, high-throughput experimental integration, standardized database development discussed. Finally, prospects AI-driven envisioned, emphasizing potential accelerate novel material discovery revolutionize nanomanufacturing paradigms within framework AI-plus initiatives.

Language: Английский

Intelligent Systems for Inorganic Nanomaterial Synthesis DOI Creative Commons

Cunhao Han,

Xinghua Dong, Wang Zhang

et al.

Nanomaterials, Journal Year: 2025, Volume and Issue: 15(8), P. 631 - 631

Published: April 21, 2025

Inorganic nanomaterials are pivotal foundational materials driving traditional industries’ transformation and emerging sectors’ evolution. However, their industrial application is hindered by the limitations of conventional synthesis methods, including poor batch stability, scaling challenges, complex quality control requirements. This review systematically examines strategies for constructing automated systems to enhance production efficiency inorganic nanomaterials. Methodologies encompassing hardware architecture design, software algorithm optimization, artificial intelligence (AI)-enabled intelligent process analyzed. Case studies on quantum dots gold nanoparticles demonstrate enhanced closed-loop machine learning-enabled autonomous optimization parameters. The study highlights critical role automation, technologies, human–machine collaboration in elucidating mechanisms. Current challenges cross-scale mechanistic modeling, high-throughput experimental integration, standardized database development discussed. Finally, prospects AI-driven envisioned, emphasizing potential accelerate novel material discovery revolutionize nanomanufacturing paradigms within framework AI-plus initiatives.

Language: Английский

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