Disassembly and spreading of magnetic nanoparticle clusters on uneven surfaces. (March 2020)

• Record Type: Journal Article
• Authors: Wang, Qianqian; Yu, Jiangfan; Yuan, Ke; Yang, Lidong; Jin, Dongdong; Zhang, Li
• Is Part Of: Applied materials today. Volume 18(2020)
• Publication Date: 2020-03
• Subjects: Disassembly -- Dynamic magnetic fields -- Collective behaviors -- Magnetic actuation -- Micro/nanorobots; Materials science -- Periodicals; Materials -- Research -- Periodicals; 620.1105
• DOI: 10.1016/j.apmt.2019.100489
• Languages: English
• ISSNs: 2352-9407

Abstract

Graphical abstract: Abstract: Magnetic nanoparticles are widely applied in biochemical applications due to their diverse functionalization and fast response to external magnetic fields. However, when actuated by magnetic fields, magnetic nanoparticles have a natural tendency to form clusters due to the induced magnetic attractive forces. In this study, we propose a strategy to controllably disassemble and spread magnetic nanoparticle clusters on uneven surfaces using dynamic magnetic fields. Magnetic nanoparticle clusters are disassembled into short nanoparticle chains, and the lengths of chains are controlled by adjusting the field parameters. To prevent reassembly, the separation distance between chains are enlarged by exploiting magnetic chain-chain repulsive forces, resulting in an increased coverage area of nanoparticles. Additionally, the induced tumbling motion of nanoparticles chains enables them to further spread on patterned surfaces. We demonstrate that the proposed disassembly and spreading strategy is effective on an uneven surface of organ ex vivo (bladder of swine). The disassembled nanoparticles are capable of regathering again, and this spreading-regathering process can be monitored using ultrasound imaging in real time. Our strategy shows great potential for increasing reproducibility and effectiveness of magnetic nanoparticle-based applications which requires high surface-to-volume ratio, and provides support to fundamentally understand collective behavior at the small scales.