journal article·2025-12-01Open access

Scalable metal-based nanoparticle synthesis via laser ablation in liquids for transformative sensory and synaptic devices

Jun-Gyu Choi, Seokhyeon Baek, Junghoon Lee, Sungjun Park

Abstract

Abstract Artificial sensory systems (ASS) are pivotal to next-generation extended reality technologies, now evolving into flexible platforms for comfortable wear and immersive user experiences, while ensuring high performance and operational reliability. To address these demands, metal-based nanoparticles (NPs), such as noble metal, oxide, and multi-elemental NPs, have been extensively incorporated into functional materials of sensory and synaptic devices due to their tunable optical, electrical, and chemical properties, enhancing sensory precision, stability, and environmental adaptability. However, traditional NP fabrication methods often involve complex processing, residual contaminants, and scalability issues, limiting their effectiveness in ASS applications. State-of-the-art laser ablation in liquids (LAL) presents a promising alternative, offering scalable production of surfactant-free NPs with customizable physicochemical properties, though their application in electronics remains underexplored. This review delves into the transformative potential of LAL-fabricated NPs in ASS, covering the fundamental mechanisms of LAL, the role of process parameters, the derivative strategies for size modulation, the diversity of metal-based NPs, their applications in sensory and synaptic devices, and the challenges and perspectives for meeting industrial standards. Bridging the gap between LAL and ASS is poised to revolutionize both industrial manufacturing and academic research by offering scalable solutions to overcome intrinsic tradeoffs between flexibility and performance, fostering innovations in human-centric, immersive electronics.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Laser-Ablation Synthesis of Nanoparticles · Electrohydrodynamics and Fluid Dynamics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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