Magneto-capillary particle dynamics at curved interfaces: inference and criticism of dynamical models
Magneto-capillary particle dynamics at curved interfaces: inference and criticism of dynamical models
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DOI：
10.1039/d3sm01256e
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发表时间：
2023-11-09
期刊：
影响因子：
3.4
通讯作者：
Bishop，Kyle J. M.
中科院分区：
文献类型：
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作者：
Livitz，Dimitri;Dhatt-Gauthier，Kiran;Bishop，Kyle J. M.
时变磁场驱动吸附在液滴上的磁性颗粒的运动，这是由于将磁转矩耦合到毛细力的界面约束。这种磁毛细粒子动力学和相关的流体流动是潜在的有用的推进液滴运动，混合液滴内容物，并增强相之间的传质。这些功能的设计得益于预测模型的开发和验证。在这里，我们应用贝叶斯数据分析的方法来识别和验证一个动态模型，该模型准确地预测了吸附在球形液滴的界面处的磁性粒子的场驱动运动。在以前工作的基础上，我们考虑候选模型，描述粒子在界面倾斜，磁场相关的贡献的磁矩，引力，以及它们的组合。每个候选人的分析是由粒子跟踪数据的磁性Janus球在不同的频率和角度的旋进场移动。我们推断每个模型的不确定参数，批评他们的能力来描述和预测实验数据，并选择最可能的候选人，占重力和倾斜的Janus球在接口。我们展示了这个受欢迎的模型如何在考虑的驱动场范围内以微米级的精度预测复杂的粒子轨迹。我们将讨论如何知识，这个“最好的”模型可以用来设计实验，通知准确的参数估计或实现所需的粒子轨迹。
Time-varying fields drive the motion of magnetic particles adsorbed on liquid drops due to interfacial constraints that couple magnetic torques to capillary forces. Such magneto-capillary particle dynamics and the associated fluid flows are potentially useful for propelling drop motion, mixing drop contents, and enhancing mass transfer between phases. The design of such functions benefits from the development and validation of predictive models. Here, we apply methods of Bayesian data analysis to identify and validate a dynamical model that accurately predicts the field-driven motion of a magnetic particle adsorbed at the interface of a spherical droplet. Building on previous work, we consider candidate models that describe particle tilting at the interface, field-dependent contributions to the magnetic moment, gravitational forces, and their combinations. The analysis of each candidate is informed by particle tracking data for a magnetic Janus sphere moving in a precessing field at different frequencies and angles. We infer the uncertain parameters of each model, criticize their ability to describe and predict experimental data, and select the most probable candidate, which accounts for gravitational forces and the tilting of the Janus sphere at the interface. We show how this favored model can predict complex particle trajectories with micron-level accuracy across the range of driving fields considered. We discuss how knowledge of this “best” model can be used to design experiments that inform accurate parameter estimates or achieve desired particle trajectories.