Division of Chemical and Biological Sciences, Ames Laboratory USDOE, Iowa State University, Ames, Iowa 50010, USA.
J Chem Phys. 2018 Jul 14;149(2):024101. doi: 10.1063/1.5037618.
The reaction yield for conversion of p-nitrobenzaldehyde (PNB) to an aldol product in amine-functionalized mesoporous silica nanoparticles (MSN) exhibits a 20-fold enhancement for a modest increase in pore diameter, d. This enhanced catalytic activity is shown to reflect a strong increase in the "passing propensity," P, of reactant and product species inside the pores. We find that P ≈ 0, corresponding to single-file diffusion, applies for the smallest d which still significantly exceeds the linear dimensions of PNB and the aldol product. However, in this regime of narrow pores, these elongated species must align with each other and with the pore axis in order to pass. Thus, P reflects both translational and rotational diffusion. Langevin simulation accounting for these features is used to determine P versus d. The results are also augmented by analytic theory for small and large d where simulation is inefficient. The connection with the catalytic activity and yield is achieved by the incorporation of results for P into a multi-scale modeling framework. Specifically, we apply a spatially coarse-grained (CG) stochastic model for the overall catalytic reaction-diffusion process in MSN. Pores are treated as linear arrays of cells from the ends of which species adsorb and desorb, and between which species hop and exchange, with the exchange rate reflecting P. CG model predictions including yield are assessed by Kinetic Monte Carlo simulation.
胺功能化介孔硅纳米粒子(MSN)中,将对硝基苯甲醛(PNB)转化为醇醛产物的反应产率随孔径 d 的适度增加而提高 20 倍。这种增强的催化活性反映了反应物和产物在孔内的“通过倾向”P 的强烈增加。我们发现 P ≈ 0,对应于单分子扩散,适用于仍显著超过 PNB 和醇醛产物线性尺寸的最小 d。然而,在这些狭窄的孔中,这些伸长的物种必须相互对齐并与孔轴对齐才能通过。因此,P 反映了平移和旋转扩散。考虑到这些特征的 Langevin 模拟用于确定 P 与 d 的关系。对于小和大 d 的情况,模拟效率较低,也通过解析理论进行了补充,其中模拟效率较低。通过将 P 的结果纳入多尺度建模框架,实现了与催化活性和产率的联系。具体来说,我们将空间粗粒化(CG)随机模型应用于 MSN 中的整体催化反应-扩散过程。将孔视为线性单元阵列,其中物种在其末端吸附和解吸,并在单元之间跳跃和交换,交换率反映 P。包括产率在内的 CG 模型预测通过动力学蒙特卡罗模拟进行评估。