亚稳态
毛细管冷凝
纳米孔
冷凝
物理吸附
蒙特卡罗方法
化学物理
多孔性
材料科学
多孔介质
弯月面
介孔材料
桥接(联网)
吸附
纳米技术
微型多孔材料
能源景观
分子动力学
势能
化学
作者
Jakob Söllner,Nicholas J. Corrente,Shivam Parashar,Alexander V. Neimark,Matthias Thommes
出处
期刊:Langmuir
[American Chemical Society]
日期:2026-08-03
卷期号:42 (32): 23216-23227
标识
DOI:10.1021/acs.langmuir.6c00638
摘要
Physisorption network modeling has proven to be a significant improvement in the characterization of mesoporous materials compared to the independent pore model. Recent models have, in particular, advanced the inclusion of cooperative condensation in nanopore networks as an additional network mechanism during adsorption. This Monte Carlo study provides a detailed investigation of how the network structure of the connected pore systems affects this mechanism. Simulations in the grand- and mesocanonical ensembles are performed to study when condensation occurs independently via liquid bridging and when condensation occurs via meniscus propagation from neighboring filled pores. The study shows, that meniscus propagation in porous networks is associated with an energy barrier and corresponding metastable states. If the number of prefilled pores at the junction is small (0 or 1 out of 4), condensation in neighboring pores occurs independently via liquid bridging, while a sufficient number of prefilled pores (2 or 3 out of 4) allows for different mechanisms of initiated condensation. The associated energy barrier is, for pores with diameters larger than 8 nm, sufficiently large to lead to long-lasting metastable states in the experimental setting. The connectivity z has a strong influence on the magnitude of the energy barrier and the results indicate, that certain junctions in porous materials might act as barriers for meniscus propagation. This is a phenomenon that can explain why the pores in some disordered materials behave as if they were independent pores during adsorption and thus fill and empty according to their size and shape. The results allow to identify the guidelines for further advancing physisorption network models.
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