Prediction by Convolutional Neural Networks of CO2 /N2 Selectivity in Porous Carbons from N2 Adsorption Isotherm at 77 K

Abstract

Porous carbons are an important class of porous materials with many applications, including gas separation. An N₂ adsorption isotherm at 77 K is the most widely used approach to characterize porosity. Conventionally, textual properties such as surface area and pore volumes are derived from the N₂ adsorption isotherm at 77 K by fitting it to adsorption theory and then correlating it to gas separation performance (uptake and selectivity). Here the N₂ isotherm at 77 K was used directly as input (representing feature descriptors for the porosity) to train convolutional neural networks to predict gas separation performance (using CO₂ /N₂ as a test case) for porous carbons. The porosity space for porous carbons was explored for higher CO₂ /N₂ selectivity. Porous carbons with a bimodal pore-size distribution of well-separated mesopores (3-7 nm) and micropores (<2 nm) were found to be most promising. This work will be useful in guiding experimental research of porous carbons with the desired porosity for gas separation and other applications.

Keywords: adsorption; machine learning; materials science; neural networks; porous materials.