Artificial Intelligence-Guided Pulsed Synthesis of Zinc Oxide Nanostructures on Thin Metal Shells
Year: 2025
Type: article
Abstract: Zinc oxide (ZnO) nanostructures have been intensively investigated for applications in sensing, photocatalysis, and optoelectronic devices, where functional performance is strongly governed by morphology, crystallinity, and defect structure. Conventional wet-chemical and vapor-phase growth methods often require long processing times or complex chemistries and face reproducibility and compatibility challenges when applied to thin, flexible, or curved metallic substrates. Pulsed high-energy techniques—such as pulsed laser deposition (PLD), high-power impulse magnetron sputtering (HiPIMS), and pulsed laser or plasma processing—offer a versatile alternative, enabling rapid and localized synthesis both from and on Zn-bearing thin shells. These methods create transient nonequilibrium conditions that accelerate oxidation and promote spatially controlled nanostructure formation. This review highlights the emerging integration of artificial intelligence (AI) with pulsed ZnO synthesis on thin metallic substrates, emphasizing standardized data reporting, Bayesian optimization and active learning for efficient parameter exploration, physics-informed and graph-based neural networks for predictive modeling, and reinforcement learning for adaptive process control. By connecting synthesis dynamics with data-driven modeling, the review outlines a path toward predictive and autonomous control of ZnO nanostructure formation. Future perspectives include autonomous experimental workflows, machine-vision-assisted diagnostics, and the extension of AI-guided pulsed synthesis strategies to other functional metal oxide systems.
Source: Processes
DOI: 10.3390/pr13113755
Author: Serguei P. Murzin
Institutions: TU Wien, Samara National Research University
Language: English
Topic: ZnO doping and properties
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4416457355