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Modeling Propulsion of Soft Magnetic Nanowires.
Mirzae, Yoni; Rubinstein, Boris Y; Morozov, Konstantin I; Leshansky, Alexander M.
Affiliation
  • Mirzae Y; Department of Mathematics, Technion-Israel Institute of Technology, Haifa, Israel.
  • Rubinstein BY; Stowers Institute for Medical Research, Kansas City, MO, United States.
  • Morozov KI; Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa, Israel.
  • Leshansky AM; Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa, Israel.
Front Robot AI ; 7: 595777, 2020.
Article in En | MEDLINE | ID: mdl-33501356
ABSTRACT
The emergent interest in artificial nanostructures that can be remotely navigated a specific location in a fluidic environment is motivated by the enormous potential this technology offers to biomedical applications. Originally, bio-inspired micro-/nanohelices driven by a rotating magnetic field were proposed. However, fabrication of 3D helical nanostructures is complicated. One idea to circumvent complex microfabrication is to use 1D soft magnetic nanowires that acquire chiral shape when actuated by a rotating field. The paper describes the comprehensive numerical approach for modeling propulsion of externally actuated soft magnetic nanowires. The proposed bead-spring model allows for arbitrary filament geometry and flexibility and takes rigorous account of intra-filament hydrodynamic interactions. The comparison of the numerical predictions with the previous experimental results on propulsion of composite two-segment (Ni-Ag) nanowires shows an excellent agreement. Using our model we could substantiate and rationalize important and previously unexplained details, such as bidirectional propulsion of three-segment (Ni-Ag-Au) nanowires.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Front Robot AI Year: 2020 Document type: Article Affiliation country: Israel

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Front Robot AI Year: 2020 Document type: Article Affiliation country: Israel