Inorganic Chemistry and Catalysis, Debye Institute for Nanomaterials Science, Utrecht University, 3584 CG, Utrecht, The Netherlands.
Soft Condensed Matter and Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, 3584 CC, Utrecht, The Netherlands.
Sci Rep. 2022 Jun 10;12(1):9595. doi: 10.1038/s41598-022-13446-0.
Single-particle tracking is a powerful approach to study the motion of individual molecules and particles. It can uncover heterogeneities that are invisible to ensemble techniques, which places it uniquely among techniques to study mass transport. Analysis of the trajectories obtained with single-particle tracking in inorganic porous hosts is often challenging, because trajectories are short and/or motion is heterogeneous. We present the DiffusionLab software package for motion analysis of such challenging data sets. Trajectories are first classified into populations with similar characteristics to which the motion analysis is tailored in a second step. DiffusionLab provides tools to classify trajectories based on the motion type either with machine learning or manually. It also offers quantitative mean squared displacement analysis of the trajectories. The software can compute the diffusion constant for an individual trajectory if it is sufficiently long, or the average diffusion constant for multiple shorter trajectories. We demonstrate the DiffusionLab approach via the analysis of a simulated data set with motion types frequently observed in inorganic porous hosts, such as zeolites. The software package with graphical user interface and its documentation are freely available.
单粒子追踪是研究单个分子和颗粒运动的一种强大方法。它可以揭示出整体技术无法发现的异质性，这使得它在研究质量传输的技术中独具特色。在无机多孔宿主中，使用单粒子追踪获得的轨迹分析通常具有挑战性，因为轨迹较短且/或运动具有异质性。我们介绍了 DiffusionLab 软件包，用于分析这种具有挑战性的数据集的运动。首先将轨迹分类为具有相似特征的群体，然后在第二步中针对这些群体进行运动分析。DiffusionLab 提供了基于运动类型的轨迹分类工具，可通过机器学习或手动进行分类。它还提供了轨迹的定量均方根位移分析。如果轨迹足够长，软件可以计算单个轨迹的扩散常数，或者可以计算多个较短轨迹的平均扩散常数。我们通过分析具有沸石等无机多孔宿主中常见运动类型的模拟数据集来演示 DiffusionLab 方法。带有图形用户界面的软件包及其文档均可免费获得。
