Session: 18-01: Advanced Materials for Sustainability

Paper Number: 156757

156757 - An Analytical Concentration-Dependent Diffusivity Model for Optimized Sorption Kinetics in Energy and Water Applications. 

Abstract: 

Sorption-based systems are critical to achieving decarbonization goals in the energy and water sectors, supporting applications such as carbon dioxide (CO₂) capture, atmospheric water harvesting, and thermochemical energy storage. These systems rely on sorbent materials to cyclically absorb and desorb under controlled conditions, such as temperature, pressure, or humidity swings. Unlike steady-state operations where sorbents saturate before regeneration, cyclic operation necessitates a fundamental understanding of sorption kinetics to optimize system performance and minimize energy consumption.

A primary challenge in designing and modeling these systems lies in accurately representing the resistance to mass transfer during the sorption process. The kinetics in a sorbent layer is typically dictated by intercrystallite and intracrystalline diffusivities. It can be shown that the ratio of the intracrystalline timescale to the intercrystalline is on the order of 10². This highlights the dominant role of intraparticle diffusion in governing sorption rates. Traditional modeling approaches often assume constant diffusivity for simplicity; however, this assumption fails to account for the strong influence of concentration gradients on diffusivity. Consequently, such models exhibit significant deviations from experimental data, particularly during early sorption stages and under conditions of low partial pressure, where the kinetics are highly sensitive to local concentration variations. Furthermore, S-shaped isotherms such metal organic frameworks (MOFs) (e.g., MOF-303 and MOF-801) tend to have significant deviations between simulation or predictions and the measured uptakes. This creates a challenge in designing these systems.

To address these limitations, this study develops a sorption kinetics model incorporating concentration-dependent diffusivity. The model accounts for the variations in diffusivity driven by local concentration changes, offering a more accurate representation of sorption dynamics. Validation against experimental data reveals that the concentration-dependent model significantly improves predictive accuracy, particularly during the early stages of sorption and at low relative humidities, where deviations from constant-diffusivity models are most pronounced. The improvement is characterized by vapor sorption and was found to be approximately 5.5× as compared to the constant diffusivity model for MOF-303 at 12.5% RH.

The findings of this study emphasize the importance of understanding the interplay between intra- and inter-crystalline diffusion and the impact of concentration gradients on sorption kinetics. By bridging the gap between experimental observations and theoretical predictions, the proposed model provides a robust framework for optimizing the design and operation of sorption-based systems. This advancement holds substantial implications for decarbonization, enabling the development of more efficient and scalable systems for CO₂ capture, water harvesting, and thermochemical energy storage. 

Presenting Author: Ibrahim Halil Sahin Georgia Institute of Technology

Presenting Author Biography: Ibrahim is a PhD student in the mechanical engineering department at Georgia Institute of Technology. He is currently working in the Energy Innovation Lab advised by Dr. Bachir El Fil. Prior to coming to Georgia Tech, he earned his B.S. degree in Aeronautical Engineering from Istanbul Technical University in 2024. He is interested in heat transfer and thermal energy storage, particularly in the context of renewable energy, solar power, and aerospace. He is currently exploring the fundamentals of transport phenomena in solid sorbents and hydrogels, focusing on applications in thermal energy storage and atmospheric water harvesting.