Inorganic Chemistry and Catalysis, Debye Institute of Nanomaterials Science, Utrecht University , Utrecht 3584 CG, The Netherlands.
Centre for Surface Chemistry and Catalysis, Faculty of Bioscience Engineering, KU Leuven , B-3001 Heverlee, Belgium.
J Am Chem Soc. 2017 Oct 4;139(39):13632-13635. doi: 10.1021/jacs.7b07139. Epub 2017 Sep 13.
We used single-molecule fluorescence microscopy to study self-diffusion of a feedstock-like probe molecule with nanometer accuracy in the macropores of a micrometer-sized, real-life fluid catalytic cracking (FCC) particle. Movies of single fluorescent molecules allowed their movement through the pore network to be reconstructed. The observed tracks were classified into three different states by machine learning and all found to be distributed homogeneously over the particle. Most probe molecules (88%) were immobile, with the molecule most likely being physisorbed or trapped; the remainder was either mobile (8%), with the molecule moving inside the macropores, or showed hybrid behavior (4%). Mobile tracks had an average diffusion coefficient of D = 8 × 10 ± 1 × 10 m s, with the standard deviation thought to be related to the large range of pore sizes found in FCC particles. The developed methodology can be used to evaluate, quantify and map heterogeneities in diffusional properties within complex hierarchically porous materials.
我们使用单分子荧光显微镜以纳米级精度研究了类似于原料的探针分子在微米级真实流化催化裂化(FCC)颗粒的大孔中的自扩散。单荧光分子的电影允许重建它们在孔网络中的运动。通过机器学习对观察到的轨迹进行分类,发现所有轨迹都在颗粒上均匀分布。大多数探针分子(88%)处于不移动状态,分子最有可能被物理吸附或捕获;其余的要么是可移动的(8%),分子在大孔内移动,要么表现出混合行为(4%)。移动轨迹的平均扩散系数 D = 8×10 ± 1×10 m s,标准偏差被认为与 FCC 颗粒中发现的大孔径范围有关。所开发的方法可用于评估、量化和绘制复杂分级多孔材料中扩散性质的异质性。