In recent decades, single particle tracking (SPT) has been developed into a sophisticated analytical approach involving complex instruments and data analysis schemes to extract information from time-resolved particle trajectories. Very often, mobility-related properties are extracted from these particle trajectories, as they often contain information about local interactions experienced by the particles while moving through the sample. This tutorial aims to provide a comprehensive overview about the accuracies that can be achieved when extracting mobility-related properties from 2D particle trajectories and how these accuracies depend on experimental parameters. Proper interpretation of SPT data requires an assessment of whether the obtained accuracies are sufficient to resolve the effect under investigation. This is demonstrated by calculating mean square displacement curves that show an apparent super- or subdiffusive behavior due to poor measurement statistics instead of the presence of true anomalous diffusion. Furthermore, the refinement of parameters involved in the design or analysis of SPT experiments is discussed and an approach is proposed in which square displacement distributions are inspected to evaluate the quality of SPT data and to extract information about the maximum distance over which particles should be tracked during the linking process.

中文翻译：

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二维单粒子跟踪实验的分析和完善

近几十年来，单粒子跟踪（SPT）已发展成为一种复杂的分析方法，其中涉及复杂的仪器和数据分析方案，以从时间分辨的粒子轨迹中提取信息。通常，与流动性相关的特性是从这些粒子轨迹中提取的，因为它们通常包含有关粒子在穿过样本时经历的局部相互作用的信息。本教程旨在提供有关从2D粒子轨迹提取与迁移率相关的特性时可以实现的精度以及这些精度如何取决于实验参数的全面概述。对SPT数据的正确解释需要评估所获得的准确性是否足以解决所调查的影响。通过计算均方位移曲线可以证明这一点，该曲线显示出由于测量统计不佳而不是真实的异常扩散而导致的明显的超扩散或亚扩散行为。此外，讨论了SPT实验设计或分析中涉及的参数的优化，并提出了一种方法，其中检查方位移分布以评估SPT数据的质量并提取有关应跟踪粒子的最大距离的信息在链接过程中。