Verification of pore size effect on aqueous-phase adsorption kinetics: A case study of methylene blue

Due to the negative environmental effect of methylene blue (MB), researchers have been investigating several aspects of its adsorption. The kinetics/rapidity is an important aspect of its uptake which is affected by the adsorbent pore properties. The aim of this study was to investigate the effect of pore size on the adsorption kinetics of methylene blue (MB). The paper employed a novel methodology where empirical findings across studies were summarised, analysed juxtaposed to derive observations. It was observed from the study that the kinetic constant increased as the pore size progressed from the macroporous to the mesoporous range. However, MB uptake was significantly slower for micropores. Microporous pore size leads to a drop in kinetic constant because the diffusion of MB through very small pores is restricted and gradual due to the adsorbate size. The mesoporous range is the superior pore size for MB adsorption kinetics. The Pseudo-second-order (PSO) model was best suited for all cases. However, 15% of the studies for mesoporous adsorbent had Pseudo-second-order (PFO) model as best-fit and 6% for microporous adsorbents. PSO is superior to PFO because it captures both the amount of active sites and the concentration of adsorbate as rate-limiting factors. It was observed that 10% of the studies had PFO as best-fit when linear modelling was used but 15% was best-fit with non-linear modelling. This was due to the mathematical simplicity of the linearised form of the PSO and the errors generally associated with the linearisation of kinetic models.

Automated summary

The pore size plays a significant role in the adsorption kinetics of methylene blue, with mesoporous range being the most effective. The Pseudo-second-order model is found to be superior for all cases, capturing both active sites and the concentration of adsorbate as rate-limiting factors. Linear modelling may lead to different best-fit results compared to non-linear modelling due to the mathematical simplicity of the linearised form of the PSO and associated errors.