Abstract
Abstract
An automated data analysis tool is provided which holds a statistical approach to analyze the magnetic suspension balance data, a gravimetry method, in order to identify the gas sorption behavior of polymeric materials such as a rubbery polymer (polydimethylsiloxane, polyactive™ 1500), glassy polymer (polymer of intrinsic microporosity), and porous solids (activated carbon and novel material such as covalent organic framework). The investigations were performed at 30° C and in the pressure range (0-50 bar) that particularly depends upon the behavior of the gases involved such as helium, nitrogen, carbon dioxide, and n-butane. The processing of MSB data is achieved by a robust algorithm design to analyze the variation in equilibrium data i.e. change in weight of sample with the pressure of the gas at equilibrium, for various materials by detecting and removing the outliers. The automation tool accurately measures the density of the material, adsorption isotherm, solubility and diffusion coeflicient of the gas. The results are compared with literature and time-lag machine data. The study shows that it is quite convenient to use the automation tool for MSB data analysis. The machine learning algorithm was used to check the performance of the developed automation tool. Furthermore, in this study, optical calorimetry, a versatile screening technique, was used for the evaluation of the adsorption capacity of different materials extended from porous solids, rubbery polymer, and glassy polymer, and compares the results with available literature data. The results show that optical calorimetry is suitable for porous solids and glassy polymer analysis.
