Department of Chemical and Biological Engineering , Rensselaer Polytechnic Institute , 110 8th Street , Troy , New York 12180 , United States.
Langmuir. 2018 Sep 11;34(36):10694-10701. doi: 10.1021/acs.langmuir.8b02331. Epub 2018 Aug 27.
Particle tracking of active colloidal particles can be used to compute mean-squared displacements that are fit to extract properties of the particles including the propulsive speed. Statistical errors in the mean-squared displacement leads to errors in the extracted properties especially for more weakly propelling particles. Brownian dynamics simulations in which the particle parameters are prescribed were used to examine the statistics of tracking self-propelling objects. It was found that the manner in which tracking data is analyzed has a profound impact on the precision and accuracy of measurements. To properly extract particle parameters, it was necessary to apply a nonlinear fit of the mean-squared displacement over a time region that includes transition behavior from ballistic to diffusive. The dependence of the statistics on the number of particles tracked and the length of movies was examined, showing how and why weakly propelling particles are difficult to analyze.
活性胶体颗粒的粒子追踪可用于计算均方位移，通过拟合这些位移可以提取颗粒的特性，包括推进速度。均方位移的统计误差会导致提取特性的误差，特别是对于推进力较弱的颗粒。通过布朗动力学模拟，其中规定了颗粒参数，用于研究跟踪自推进物体的统计特性。结果发现，跟踪数据的分析方式对测量的精度和准确性有深远的影响。为了正确提取颗粒参数，有必要对包含弹道到扩散的转变行为的时间区域进行均方位移的非线性拟合。研究了统计数据对跟踪的颗粒数量和电影长度的依赖性，展示了为什么以及为什么难以分析推进力较弱的颗粒。
