Novel machine learning-based method for estimation of the surface area of porous silica particles

This work reports a novel and quick method to estimate the surface area of porous materials. Conventionally, surface area measurement requires the BET method/N2 adsorption experiment which is time-consuming. In this work, we developed a method based on machine learning (ML) and the adsorption of a conductive dye on porous materials. The rate and quantity of dye adsorption, which is characterized by dynamic measurement of conductivity, provide an indirect measure of surface area and zeta potential. An ML-based soft sensor is developed to relate the measured conductivity profiles with surface area and zeta potential. A phenomenological model on dye adsorption is also developed, validated, and used to augment experimental data for training the soft sensor. The developed method was tested for porous silica particles with a range of surface areas (250−1100 m2 /g) and zeta potential (−17 mV: −29 mV). The developed soft sensor was able to estimate the surface area and zeta potential quite well. The developed approach and method reduce overall measurement time for surface area from several hours to a few minutes. The method can potentially be implemented in continuous plants producing porous materials like silica