Tuning Self‐Assembled Topological Dipoles in Optoelectronic Traps
ABSTRACT Precise control of microstructure topology is fundamental in the fields of photonic materials, optical sensing, microfabrication, and biomanufacturing, where tailored particle arrangements are essential for optimizing functional properties and enabling advanced applications. However, conventional self‐assembly methods often lack tunability and dynamic control over topological configurations. Here, we present an optical approach that leverages optoelectronic traps to guide the self‐assembly of microspheres into distinct topological arrangements. Our experiments reveal that microspheres self‐assemble into structured dipolar arrays and polygonal lattices with configurations determined by the co‐influence of dielectrophoretic (DEP) forces and interparticle interactions. Notably, these assemblies exhibit self‐restoration properties, allowing disrupted structures to recover their original topological configurations due to the restoring DEP forces. Numerical simulations reveal that the topological arrangements emerge from a balance between DEP‐induced attraction and electrostatic repulsion, modulated by the geometry of the optoelectronic potential well. Furthermore, by tailoring the shape of the light pattern, the system enables dynamic topological transformations, allowing controlled deformations or phase transitions in the micro‐assembly. To further demonstrate the generality and cross‐domain applicability of this strategy, we extended it to biological systems using yeast cells as a model, which also exhibited robust and ordered topological self‐assembly behaviors. This study provides a novel framework for designing and assembling programmable and resilient topological microstructures with potential applications in advanced micro‐fabrication, micro‐assembly and beyond.
Authors
- Shuailong Zhang (ORCID: https://orcid.org/0000-0003-1004-021X)
- Bingrui Xu (ORCID: https://orcid.org/0009-0001-3597-7263)
- Huikai Xie (ORCID: https://orcid.org/0000-0002-8393-0021)
- Chao Huang (ORCID: https://orcid.org/0000-0003-1186-0973)
- Ziang Ma
- Hai‐nan Xie
- Jinyao Tang (ORCID: https://orcid.org/0000-0002-0051-148X)
- Rongxin Fu (ORCID: https://orcid.org/0000-0002-4547-2269)
- Jiafang Li (ORCID: https://orcid.org/0000-0002-9658-8261)
- Yaxin Huang (ORCID: https://orcid.org/0000-0003-2626-9318)
- Xiaorong Hong (ORCID: https://orcid.org/0000-0002-0467-9191)
- Wenbo Dong (ORCID: https://orcid.org/0009-0002-4142-6123)
- Hang Li (ORCID: https://orcid.org/0000-0002-6283-9069)
- Xueqiang Zhang (ORCID: https://orcid.org/0009-0008-4096-4281)
- Zonghao Li (ORCID: https://orcid.org/0000-0003-4019-4957)
- Yang Fan (ORCID: https://orcid.org/0009-0004-1290-4418)
- Gong Li
Institutions
- Beijing Institute of Technology (CN)
- Institute of Microelectronics (CN)
- Beike Biotechnology (China) (CN)
- Beijing Advanced Sciences and Innovation Center (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- Laser & Photonics Review
- Published
- 2025-11-23
- DOI
- https://doi.org/10.1002/lpor.202501697
- Citations
- 1
- Primary Topic
- Modular Robots and Swarm Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.71