(383u) Study and Discovery of Porous Materials for Gas Separation and Storage

Research Interests: Accelerating materials discovery and advancing sustainable industrial processes using process modeling, molecular simulations, Monte Carlo methods, density functional theory (DFT), and machine learning.

Abstract: Eighty percent of US energy consumption is fossil–fuel–based, which leads to significant greenhouse gas emissions. The transportation and industrial sectors collectively account for seventy percent of US energy consumption. Chemical separations are central to numerous industrial processes, such as petrochemical refinement, purification of pharmaceutical ingredients, and reverse osmosis for water desalination. Replacing conventional separation methods – such as distillation and evaporation – will directly lead to a significant reduction in energy consumption, because these processes account for nearly half of the net energy consumption of the industrial sector.

The essential factors affecting the performance of chemical separations are material properties (stability, reusability), operational conditions (temperature and pressure), and mixture composition. Hence, our work lies at the intersection of materials, mixtures, and computational methods. Our research focus is to discover porous materials – such as zeolites and metal organic frameworks (MOFs) – for separation processes, gas storage, and fuel delivery using advanced molecular simulation techniques that consider realistic gas mixtures (with trace impurities), multicomponent adsorption, extreme conditions, and reusability. The following sections outline the three works that form the core of our research efforts:

1. Curation of porous material databases: We have enriched freely available zeolite and MOF databases with ready-to-use pore characterization and hydrophilicity classification, that can be utilized by researchers to screen materials for specific application.
2. Gas storage in porous materials: We have developed a simulation approach that, for the first time, allows to assess the storage of a realistic mixture with trace compounds in an adsorbed natural gas (ANG) tank over multiple filling/delivery cycles.
3. Material design for separations: We have explored zeolite–based membranes for the selective separation of ammonia from nitrogen/hydrogen at high temperatures and pressures.

The insights gained from this work pave the way to guide experiments, implement data–driven material screening, and train machine learning models that can significantly advance materials discovery, leading to reduced energy consumption through efficient chemical–separation processes.