Automated monitoring the kinetics of homogeneous and heterogeneous chemical processes using a smartphone

Heterogeneous chemical processes occupy a pivotal position in many fields of applied chemistry. Monitoring reaction kinetics in such heterogeneous systems together with challenges associated with ex-situ analytical methodologies can lead to inaccurate information about the nature of the catalyst surfaces as well as information about the steps involved. The present work explores the possibility of kinetic measurements of chemical reactions and adsorption processes of homogeneous and heterogeneous systems through the variation of RGB intensities of digital images using a smartphone combined with a program written in Python to accelerate and facilitate data acquisition. In order to validate the method proposed, the base promoted hydrolysis of 4-nitrophenyl acetate was initially investigated. The rate constants obtained through RGB analysis (0.01854 min(-1)) is almost identical to that using traditional UV-Vis spectroscopy (0.01848 min(-1)). The proposed method was then applied to monitor the kinetics of three heterogeneous processes: (1) reduction of 4-nitrophenolate in the presence of dispersed Pd/C; (2) decomposition of methyl orange with TiO2; and (3) adsorption of rhodamine on montmorillonite. In general, the method via digital images showed high reproducibility and analytical frequency, allowing the execution of simultaneous analyses, with an accuracy comparable to UV-Vis spectrophotometry. The method developed herein is a practical and valuable alternative for obtaining kinetic data of heterogeneous reactions and processes where a color change is involved, bypassing sampling collection and processing which decreases analytical frequency and may lead to data errors.

Automated summary

Heterogeneous chemical processes play a crucial role in applied chemistry. This study explores the possibility of measuring the kinetics of chemical reactions and adsorption processes in homogeneous and heterogeneous systems using smartphone images and a Python program. The results show that this method is accurate and comparable to traditional UV-Vis spectroscopy for measuring reaction kinetics.