动力学状态切换控制多孔介质中活性微杆的导航
Dynamical-state switching controls active microrod navigation in porous media
AI总结：
该研究针对合成微游动体难以实现生物级自适应策略的问题，揭示Janus微游动体的动力学状态切换机制，实现多孔介质中高效导航，优化了局部探索与长程输运的权衡。
摘要：
自适应搜索与行进策略使生物游动体能探索复杂环境，但难以在极简合成微游动体中实现。本文揭示了电场驱动的杆状Janus微游动体的多种动力学与取向状态：平面活性态、取向手性态及持续态。我们在预取向后发现一种隐藏动力学状态，可按需从强手性运动切换至持续运动。我们利用这种取向与手性切换现象在微结构多孔阵列中开展探索：强手性促进局部扫描，但会导致圆形捕获和净位移差；预取向动力学状态则能实现高效障碍物分选与逃逸。通过将实验与相同实验提取障碍物几何下的活性布朗粒子模拟对比，我们利用首通时间统计量化导航效率，揭示手性调谐如何优化局部探索与长程输运间的权衡。
英文摘要：
Adaptive search and travel strategies enable biological swimmers to explore complex environments, but remain difficult to encode in minimal synthetic microswimmers. Here, we unveil multiple dynamical and orientational states of rod-like Janus microswimmers actuated via electric fields: planar active, oriented chiral and persistent rods. We discover a hidden dynamical state after pre-orienting them, which allows us to switch on demand from strong chiral to persistent motion. We harness this phenomenological orientation and chirality switch for exploration in microstructured porous arrays. Here, strong chirality promotes local scanning but drives circular trapping and poor net displacement, whereas the pre-aligned dynamical state enables efficient obstacle sorting, and trap escape. Comparing experiments with simulations of active Brownian particles in the very same experimentally-extracted obstacle geometries, we quantify navigation efficiency via first-passage time statistics, revealing how chirality tuning optimizes the trade-off between local exploration and long-range transport.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
